Review of automated fault detection and diagnostic tools in air handling units
Ken Bruton, Paul Raftery, Barry Kennedy, Marcus M. Keane & D. T. J. O’Sullivan Energy Efficiency ISSN 1570-646X Energy Efficiency DOI 10.1007/s12053-013-9238-2
1 23
Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media Dordrecht. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”.
1 23
Author's personal copy Energy Efficiency DOI 10.1007/s12053-013-9238-2
REVIEW ARTICLE
Review of automated fault detection and diagnostic tools in air handling units Ken Bruton & Paul Raftery & Barry Kennedy & Marcus M. Keane & D. T. J. O’Sullivan
Received: 30 July 2013 / Accepted: 31 October 2013 # Springer Science+Business Media Dordrecht 2013
Abstract Studies have indicated that 20–30 % HVAC system energy savings are achievable by recommissioning air handling units (AHU) to rectify faulty operation. Studies have also demonstrated that on-going commissioning of building systems for optimum efficiency can yield savings of an average of over 20 % of total energy cost. Automated fault detection and diagnosis (AFDD) is a process concerned with automating the detection of faults and their causes in physical systems. AFDD can be used to assist the commissioning process at multiple stages. This article presents a review of the research work that has been carried out on the use of AFDD tools in improving the efficiency of AHUs. This updates and expands upon the most recent literature review in this area, published in 2005. The article offers a comparative analysis of the FDD techniques currently in use and offers an opinion as to which show most potential for widespread adoption as part of the on-going commissioning process. It then details the issues which have impeded the adoption of existing K. Bruton (*) : D. T. J. O’Sullivan Department of Civil & Environmental Engineering, University College Cork, College Road, Cork, Republic of Ireland e-mail:
[email protected] P. Raftery : M. M. Keane Informatics Research Unit for Sustainable Engineering, National University of Ireland, Galway, Galway, Ireland B. Kennedy Innovation for Ireland’s Energy Efficiency (I2E2), Leixlip, Co. Kildare, Ireland
AFDD tools for AHUs to date before concluding with an appraisal of current and recommended areas for future research to overcome the barriers to the widespread adoption of AFDD tools in AHUs. Keywords Heating . Ventilation and air conditioning (HVAC) . Fault detection and diagnosis (FDD) . Energy efficient buildings . Commissioning . Air handling unit (AHU)
Introduction Buildings rarely perform as well in practise as anticipated during design due to improper equipment selection or installation, lack of commissioning, or improper maintenance (Piette et al. 2001) to cite but a few reasons. Approximately 50 % of a commercial building’s energy consumption is associated with heating ventilation and air conditioning (HVAC) energy consumption (Xiao and Wang 2009). HVAC system, and more specifically air handling unit (AHU) energy consumption accounts on average for 40 % of an industrial sites total energy consumption (“HVAC SWG–Spin I 2007” 2011) due in some part to its inefficient operation. Overall, it is estimated that HVAC energy consumption accounts for 10– 20 % of total energy consumption in developed countries (Pérez-Lombard et al. 2008) with AHU associated energy use accounting for the majority of this. Studies have indicated that savings of 20–30 % in building system energy consumption are achievable by recommissioning HVAC systems, and more specifically
Author's personal copy Energy Efficiency
AHU operations, to rectify their faulty operation (International Energy Agency 2002). Studies have also demonstrated, using a sample set of over 80 buildings, that on-going commissioning of building systems for peak efficiency can yield savings of an average of over 20 % of total energy cost (Piette et al. 2001). By coupling the re-commissioning and on-going commissioning of a HVAC system into one-demonstration study, savings of 44 % of electricity consumption and 78 % of gas consumption over a 10-year period have been proven by the International Energy Agency (IEA) Annex 47, DABO Case Study(International Energy Agency 2010; Choinière and Corsi 2003). Automated fault detection and diagnosis (AFDD) is a process concerned with automating the detection of faults and their causes in physical systems (Katipamula and Brambley 2005a). IEA Annex’s 25(International Energy Agency 1996) and 34 (International Energy Agency 2002) were undertaken to develop and implement HVAC system AFDD tools in real buildings. While testing these tools, researchers realised that the building services they set out to optimise had never worked as efficiently as intended. Hence, they decided that obtaining tools to detect new faults was important, but that it was even more important to commission building services effectively, thus ensuring that initial faults were avoided on an on-going basis. Commissioning, as a means of optimising building services energy consumption, will therefore continue to be developed further over the coming years for four primary reasons as follows (International Energy Agency 2006): 1. Energy and environmental issues will drive reduced energy consumption 2. A business need to reduce costs 3. Technological improvements will facilitate the implementation of solutions 4. Buildings and their services are becoming more complex in order to meet increased efficiency requirements Current HVAC system management HVAC systems are typically supervised and maintained by either an onsite facilities team or an offsite third party contractor. The number of AHUs in a typical HVAC system often outnumbers those supervising and maintaining the system by 20 to 1. This means that routine mechanical maintenance is typically carried out only
when necessary due to an end user complaint, a machine breakdown or a breached alarm limit. The complexity of modern AHU control philosophies also commonly results in onsite personnel not having the required knowledge to ascertain the cause of issues without costly external consultancy. Building operators are also typically overwhelmed by HVAC system data as the systems have had little effort put into consolidating the vast quantities of information into a clear and coherent format (Wang et al. 2012b). Both top-down (system level) and bottom-up (component level) approaches are common methods of managing AHU operation in terms of optimising their energy consumption and achieving other operational targets. The top down approach is growing in its application though it is not yet commonplace. Many industrial and large/multi-commercial sites now employ monitoring and targeting approaches, energy performance indicators and performance dashboards to manage site energy consumption. Typically, these systems focus on the most significant energy end-uses. HVAC systems, of which AHUs are a member, are typically identified as a significant energy user. Wang et al. (2010) carried out FDD at a component level in a HVAC system by implementing performance indicators based on variables on which energy use is dependent, such as outside temperature and humidity. Structured energy management systems such as those in compliance with En16001 (CEN) 2010 or ISO50001 (ISO—International Organisation for Standardisation 2011) promote this philosophy of energy management and are growing in their adoption. The bottom-up approach is far more common in practise. This method has developed from breakdown maintenance of key AHU components, such as fans and filters, to time-based maintenance, to today’s not uncommon process of monitoring equipment based on its condition and then carrying out maintenance tasks as required. To take this maintenance regime a step further, prognostic maintenance practises could be used by industry to manage the risks associated with unexpected equipment failure (Sikorska et al. 2011) focusing on preventative rather than reactive maintenance regimes. This change in maintenance philosophy is already in use in some industries with an international standard (ISO 13381–1) (“ISO 13381–1:2004—Condition Monitoring and Diagnostics of Machines-Prognostics-Part 1: General Guidelines” 2011) lending direction, but its widespread adoption could be supported with AFDD tools.
Author's personal copy Energy Efficiency
BEMS-assisted AHU monitoring Building energy management systems (BEMS) are typically the repository of HVAC system and more specifically AHU operational data and are now commonly installed in commercial and industrial buildings Gordon 2002). Typically, the cost of installing a BEMS is the biggest constraint to doing so. Approximately, 45–75 % of the associated cost is due to wiring (materials and labour) (Jang et al. 2008). However, as the cost of wireless sensor networks decreases and their robustness and interoperability increases, so too will their application in building energy management systems (Jang et al. 2008). This will serve to reduce the cost and expand the application of these systems still further. The availability of BEMSs offers the potential for innovative commissioning services with lower set-up costs than cases where a BEMS is not present. BEMS systems are also used to supervise the performance of AHUs in HVAC systems, raising alarms when upper or lower limits of operation are breached. However, they do not diagnose the root cause of these alarms, with few current BMS systems having fault detection or diagnostic capabilities. Furthermore, when no alarm levels are breached, they do not detect underlying faults during what appears to be normal operation of these systems. A typical example is the refrigeration energy wasted by a passing cooling coil control valve, as illustrated in Fig. 1 by the drop in temperature across the cooling coil in the AHU when the control valve is showing closed. This particular example typically goes unnoticed for long periods of time, as it is often possible for the AHU to maintain control of all set-points due either to the availability of hot return or outside air or due to the availability of a heating coil up or
downstream to compensate for temperature drop across the cooling coil. Depending on the criticality of the area being conditioned by a AHU, a fault could potentially require downtime in order to rectify it. An AFDD tool could detect and diagnose this fault at a component level before it becomes an issue warranting such downtime. If this tool were deployed as an on-going commissioning tool, it would detect and diagnose these issues in (almost) real time. This information could then be gathered and displayed using system performance indicators. The level of detail would be distilled depending on the particular user reviewing system performance. For example, the approximate total cost of all faults would be displayed at a managerial level, while detailed individual fault descriptions would be displayed to technicians in order of priority of repair. This is a key point to note, as building owners will need to know the potential savings achievable using an AFDD solution before deciding whether to invest or not (Lee and Yik 2010). The next section of this paper discusses how AFDD can aid the commissioning process and deliver on the potential 20–30 % HVAC system energy savings possible via this process, with the section entitled "How would an AFDD commissioning tool work?" detailing how the AFDD tool would operate in practise. The section entitled "Which method of FDD should be used in an AFDD tool to aid the commissioning of AHU’s in HVAC systems? outlines the various methods of AFDD currently either in use or in development and concludes with an opinion as to which show most potential. The section entitled "What is impeding the adoption of automated AFDD tools on AHU’s?" serves to detail the issues which have impeded the adoption of existing AFDD tools for AHUs to date, while section six offers up hypotheses, based on current research
Mixed Air Temperature
Control Valves Closed
Fig. 1 Example of a typically undetected fault in a cooling coil control valve
Post Coil Temperature
Author's personal copy
Ideal Case
Energy Efficiency
Building Phase
Retro Case
Re Commissioning Case
Initial Commissioning
Continuous Commissioning
Initial Commissioning
Re- Commissioning
Continuous Commissioning
No initial Commissioning
Retro Commissioning
Continuous Commissioning
Stage in Building Lifecycle
Conception/ Preliminary Design
Design
Construction
Operation & Maintenance
Fig. 2 Four step commissioning process
work in the area, as to the potential mechanisms to overcome these barriers. The article then concludes with an analysis of the future work required to develop an AFDD tool for AHUs which will be enveloped by the building services industry in section seven.
How can AFDD tools aid the commissioning process? Most commissioning activities are currently performed manually, as labour-intensive, once-off undefined processes upon completion of an installation. For commissioning to be truly successful, it must be embedded in the overall project process, and must follow a defined procedure in order to ensure it is effective and repeatable. It was for these reasons that the IEA launched Annex 40 (International Energy Agency 2006), with a view to developing, validating and documenting tools for the commissioning of buildings and their services. This study concluded that there are four ideal types of commissioning (Fig. 2) as follows: 1. Initial commissioning—a first time process carried out on a newly constructed building 2. Retro commissioning—a first time process on an existing building where no documented commissioning was previously carried out
3. Re-commissioning—a process undertaken on a new or existing building to verify or improve performance 4. On-going commissioning—undertaken continually to maintain, improve and optimise performance As HVAC systems grow more complex, so too will the commissioning process required to ensure their efficient operation. As current practises are mostly manual, and hence costly, this will further decrease the likelihood of undertaking any of the aforementioned commissioning processes. For these reasons, the manual nature of commissioning is set to change, moving towards an automated commissioning process. Xiao and Wang support this hypothesis stating that commissioning is labour intensive and that the future will be that of an automated lifecycle commissioning process embedded in the operation of the building management system (Xiao and Wang 2009). Xiao and Wang also state that AFDD is a key means of achieving automated commissioning with many AFDD projects now in the implementation phase from their initial research and development (Xiao and Wang 2009). IEA annex 47 (International Energy Agency 2010) reviewed the operation of 18 such tools, concluding that automation is still uncommon during the commissioning process, and that future tools should be developed that are easily embedded in existing operational practises.
Author's personal copy Energy Efficiency
Djuric and Novakovic (2009) conducted a review of the practical applications of lifecycle commissioning in buildings services. Methods of AFDD were discussed as were their application as commissioning tools. They concluded that technologies for carrying out automated commissioning are still in their infancy and that very few tools are available for practitioners to use. The IEA annex members came to the same conclusion and further state that the automation and more robust application of AFDD tools remains an important requirement to reduce the cost of the commissioning process (International Energy Agency 2002). Djuric and Novakovic (2009) also conclude that for a tool to be effective for commissioning, it must be designed with the end user in mind. Again, the IEA annex members agree, stating that AFDD tools should be better integrated and made easier to use (International Energy Agency 2002).
How would an AFDD commissioning tool work? By firstly re/retro-commissioning the AHUs in a HVAC system, their performance can be set to optimum levels. This would serve to return the AHUs in the HVAC system to optimal “fault-free” operation. This could be achieved using a BEMS-assisted AFDD tool, which identifies a number of areas which are not performing as intended. Once these items have been remedied, the AFDD tool could be used as an on-going commissioning tool to ensure that any new faults in the system are detected effectively. Since the last comprehensive review of FDD applied to HVAC systems undertaken by Katipamula and Brambley (2005b), significant work has been undertaken to prove the concept of AFDD by pilot assessment. IEA annex’s 25, 34, 40 and 47 (International Energy Agency 2002; International Energy Agency 2010; International Energy Agency 1996; International Energy Agency 2006) focused on the development of AFDD tools for use in the commissioning process. One such tool, DABO 2013, is based on this two-stage model, whereby a commissioning module assists during the initial commissioning phase, while a reporting module operates in real time during normal operations. This reporting module highlights inefficiencies as they occur and offers the user a list of prioritised faults requiring their attention. This prioritised list is sorted in terms of criticality and relative cost, from no impact, meaning that neither comfort nor energy efficiency are impacted,
to high impact, meaning that both energy and comfort are impacted. This information is then displayed to the user allowing them to make informed decisions as to which areas to remedy first. From 2006 to 2009, a total of 126 faults were identified using the DABO AFDD tool on a test building. Of these 91 were repaired based on their relative significance, with 49 of these having energy efficiency implications. This development model is likely to be adopted commercially, as the information needed and the issues most likely found during initial commissioning are different to those present during normal operation. For example, design-based issues such as the introduction of “outside air” from an internal space or the requirement for a high volume of air to maintain cleanliness requirements in a quality critical area should be identified at the initial commissioning step and should henceforth be ignored by the future on-going commissioning analysis if a decision has been taken by the system owner to maintain such operational parameters. If this were not the case, a continuous fault would register on the system unnecessarily thus clouding future results. Salsbury and Diamond agree, having tested a proprietary AFDD tool, and conclude that initial commissioning should be carried out in order to ensure a fault-free starting condition before the introduction of an AFDD tool to monitor the operational phase of AHUs in a HVAC system (Salsbury and Diamond 2001). In the author’s opinion, using this development model, current constraints in terms of maintenance resource availability and knowledge base could be overcome. The maintenance of large numbers of AHUs by a relatively small number of resources would now no longer be an issue as an AFDD tool could generate a list of prioritised action items focusing efforts where most required. The level of knowledge required to ensure that the AHUs in a HVAC system are operating correctly could be reduced as an AFDD tool could provide a reasoning engine to detect and diagnose issues automatically, only notifying the user when an issue has been diagnosed. Lastly, the issues associated with nonoptimal control strategies or a lack of understanding of their operation could be addressed as an AFDD tool could monitor both the initial control set-up and subsequent operation of the control algorithms, ensuring their robust and repeatable operation. To this end, future commissioning assisting AFDD tools should be developed with two distinct (but related) functions. The first function should be to ensure that the
Author's personal copy Energy Efficiency
system is performing optimally. The second function should be to ensure that it continues to do so.
Which method of FDD should be used in an AFDD tool to aid the commissioning of AHUs in HVAC systems? Wang et al. (2009) suggests that to be useful, AFDD must: & & & & &
Be embedded into existing systems Be proven from field/pilot tests on real buildings Minimise the number of false positives to build user trust in the system Be rolled out in a cost-effective manner Be generic enough to allow for large-scale implementation that is independent of platform/system (in order to ensure its widespread adoption in the industry)
FDD has been an active area of research in many industries since the early 1980s, but it is only in the last 20 years that its use for HVAC applications has begun to develop (Katipamula and Brambley 2005b). This development initially focused on vapour compression equipment, such as chillers, and used temperature and pressure readings to detect faults based on thermodynamic relationships within the systems. Figure 3 details the main methods and subcategories of FDD as expanded from an initial schematic compiled during the last comprehensive review of FDD applied to HVAC systems conducted by Katipamula and Brambley (2005b). Table 1 details the items listed in Fig. 3 while also outlining the advantages and disadvantages of each along with its potential for market implementation in the short to medium term. FDD methods range from those based on physical and analytical models to those driven by analysis of historical performance data using either artificial intelligence or statistical techniques. Model-based approaches use measured data and knowledge of the system to compare actual operating data with expected operating data. Differences between the two indicate a fault state based on quantified tolerance values. Model-based methods are either quantitative or qualitative in their makeup. Quantitative models are sets of quantitative mathematical relationships based on the underlying physics of the processes. Quantitative models are broadly split between detailed (Salsbury and
Diamond 2001; Le et al. 2005; Yu et al. 2002; Yoshida and Kumar 2001; Yoshida et al. 2001) and simplified (Berton and Hodouin 2003; Han et al. 2011a, b; Lee et al. 2007) techniques. Detailed techniques seek to take all variables within the HVAC process into account in the FDD analysis. Salisbury and Diamond (2001) used static simulation models of a HVAC system operating in control to generate feed forward control actions which supplement the operation of a conventional PID loop. Simplified methods in comparison attempt to focus on the key factors affecting the performance of the system without adversely affecting the quality of the results obtained, thus reducing analytical effort. Lee et al. (2007) use the simplified energy analysis procedure for fault detection at the whole-building level. The procedure developed during this research was applied retrospectively to 3 years of measured consumption data clearly identifying three significant operational changes that occurred during the test period. Qualitative models (Choinière and Corsi 2003; Song et al. 2008; Schein et al. 2006; Yang et al. 2008; Doukas et al. 2007; Lo et al. 2007) are methods consisting of qualitative relationships derived from knowledge of the underlying physics. Shein et al. (2006) tested the previously developed AHU performance assessment rules (APAR) using data emulation and test sites. The APAR rule set encompasses 28 individual rules which utilise control signals from the AHU under analysis to apply a subset of these rules to the relevant mode of operation of the AHU as identified by the rule set (House et al. 2001). Shein et al. found that this method was effective when embedded in commercial HVAC control equipment though problems did exist in diagnosing the root cause of some faults. Doukas et al. (2007) created a decision support tool based on an expert rule set to aid this diagnosis process. The decision support module utilised experience-based reasoning data and external parameters, such as new equipment cost, taxes, interest rates and fuel costs to arrive at the desired outcome. More recently, Yang et al. (2008) attempted to resolve the diagnosis issue by applying a sequenced mode specific rule set to the data to more accurately define the faulty component based on a process of elimination between parallel tests involving the same sensors or components. In general, though decision support systems are well founded in the general energy efficiency sector utilising methods such as mixed integer linear programming for the optimisation of processes in the paper and pulp
Author's personal copy Energy Efficiency Table 1 Advantages, disadvantages and commercial potential of AFDD using different FDD methods FDD method
Analysis technique
Detailed models
Auto Requires minimal regressive knowledge of system exogenous to set up (ARX) Feed forward Reduces the effects of non linearity of HVAC systems
Simplified models
Feature selection
Performance metrics
Rule or physics based
Reduced order models Expert systems
First principals analysis Fuzzy logic
Black or grey box and data driven techniques
Advantages
Potential for market deployment in short to medium term
Requires longer periods to stabilise parameters
Average
Accuracy of model dependant on the selection of the variables & the accuracy of the data. Considerable analysis must initially be undertaken to define the key features for FDD. This may also be specific to each system Statistical analysis must initially be used to develop metrics based on system specific existing data. Metrics must be trained to ensure suitability prior to roll out
Good
Reduces requirement for returned data by focusing on those areas most affecting operation Can be used for both system and component analysis effectively. Performance of metrics can be improved as new data comes on line Reduces requirement for returned Not as accurate as detailed models data by focusing on those areas most affecting operation Relatively easy to develop and Complex systems may require complex rules implement an effective rule set Rule set can be easily expanded Some bespoke rules may need to be developed for specific systems Relatively easy to develop and implement an effective equations Knowledge based so effective in non linear systems such as HVAC Can work effectively with non linear operation
Artificial neural networks Pattern Effective on large systems recognition Hidden Could be deployed to a wide Markov range of HVAC systems models relatively easily SVM Effective in identifying fault instances by categorisation PCA Easier to develop than some other detailed modelling techniques
Wavelet analysis
Disadvantages
Can separate special cause variation from common cause variation in a process.
industry (Karlsson 2011) to the use of expert system shells such as C-Language integrated production system
Excellent
Excellent
Good
Excellent
Each system will require bespoke development Average
Large amounts of measurement data over different conditions required to improve accuracy Require large amounts of data to set up
Good
Difficult to identify all possible fault patterns to compare against Can be difficult to identify which set of points are useful in detecting particular faults
Good
Average
Good
Good Significant quantity of training data required to set up accurately May need to be utilised in conjunction with Good another physical reasoning technique to improve accuracy Statistical analysis must initially be undertaken on bespoke data for each system in order to define an accurate model Complicated relationship between fault and Good fault signal patterns causes wavelet analysis to find it difficult to accurately diagnose localised faults and their relative magnitudes
to refine the design of a cogeneration plant (Matelli et al. 2011), their use in HVAC systems is not prevalent.
Author's personal copy Energy Efficiency
FDD Methods
Qualitative
Quantitative Model Based
Model Based
Detailed
Simplified
Rule
Models
Models
or Physics Based
Expert Systems Feed Forward [Salsbury and Diamond 2001]
Performanace Metrics [U. Lee, Painter, and Claridge]
[Daniel Choinière and Maria Corsi 2003], Song, Akashi, and Yee 2008; Schein et al. 2006; Yang et al. 2008; Doukas et al. 2007]
Reduced Order Models
Process History Based
Data Driven techniques
Artifical Neural Networks [Zhu, Jin, and Du 2012; Du, Jin, and Yang 2009]
Pattern Recognition [Seem 2007]
[Berton and Hodouin 2003] , [Han, Gu, Kang, et al. 2011] First Principals Based Analysis [Song, Akashi, and Yee 2008]
Hidden Markov Models [Samuel R West et al. 2011] Fuzzy Logic [Lo et al. 2007]
Support Vector Machine (SVM) [Liang and Du 2007]
Principal Component Analysis (PCA) (Wu and Sun 2011; Du, Jin, and Wu 2007; Chen and Lan 2010; S. Wang and Xiao 2004; Hao, Zhang, and Chen 2005) (S. Wang, Zhou, and Xiao 2010)]
Wavelet Analysis [Zhu, Jin, and Du 2012]
Genetic Algorithms [Wang et al 2012a], [Wang et al 2012b]
Auto Regressive Exogenous (ARX) [Harunori Yoshida, Kumar, and Morita 2001] [Haruniri Yoshida and Kumar 2001]
Fig. 3 Classification of AFDD methods expanded upon from (Katipamula and Brambley 2005a)
Author's personal copy Energy Efficiency
Process history-based data-driven fault detection and diagnosis utilises analysis techniques such as artificial neural networks (Zhu et al. 2012; Du et al. 2009), statistical hypothesis testing(Pakanen and Sundquist 2003), pattern matching (Seem 2007), support vector machine (Liang and Du 2007), hidden Markov models (West et al. 2011), principal component analysis (Wu and Sun 2011; Du et al. 2007; Chen and Lan 2010; Wang and Xiao 2004; Hao et al. 2005); (Wang et al. 2010) and wavelet analysis (Zhu et al. 2012) to identify fault instances based on ideal operating data, fault instance data and a period of training to streamline performance. This type of analysis is not as concerned with how the system operates but more so the relationships between key variables in the system. They achieve this by analysing how the process data varies over both fault-free and fault-driven conditions. West et al. (2011) developed a FDD method based on the modelling of operational faults in HVAC subsystems, using techniques from both statistical machine learning and information theory. This method analysed the probabilistic relationships between groups of points during both fault-free and faulty operation. Of particular note, in this, the most recent research is the combined application of multiple techniques to effectively detect and diagnose fault instances. Again, continuing the theme of more recent research in this domain towards the combination of methods, Wang et al. (2012a) and (Wang et al. 2012b) utilises a hybrid model and rulebased approach to detect and diagnose faults in variable air volume systems and terminal units. Wang et al. use cumulative sum analysis in conjunction with genetic algorithmic optimisation to detect faults while subsequently using an expert rule set to diagnose their root causes. Zhu et al. (2012) describes a diagnosis technique based on neural network pre-processed by wavelet and fractal, with the wavelet analysis utilised to separate special cause from common cause operational variations. The method of AFDD applied to a system differs depending on the following: & & & & & & &
The type of system to which it is applied The level of detail of diagnosis required The cost of implementation The degree of automation Tolerance of false positives The quantity of input data required The number and location of sensors present
Though physical model-based FDD methods are the most accurate and reliable, their widespread commercial adoption is currently unlikely due to the configuration effort required to deliver an accurate model (Salsbury and Diamond 2001). Typically, AFDD tools developed using these methods must first be trained with fault-free data to be effective in their diagnosis; data which in reality is seldom available. These trained models also have the disadvantage of being difficult to scale to other systems due to the fact that they have been developed and trained for the operation of a particular system. However, recent efforts to develop reduced order modelling approaches may become commercially viable in the medium to long term (Han et al. 2011a). Process history data-driven based systems can be developed in a reasonably generic manner. However, they do require significant quantities of measured data to develop. The solution may lie in the effective combination of techniques. Significant research has attempted to determine the most effective combination of techniques. Wang et al. (2009) suggests that process knowledge should be used in conjunction with a knowledge-based system in a combined data-driven and knowledge-based approach for maximum efficiency. Han et al. (2011b) describes a study which investigates a hybrid model that combines support vector machine (SVM) with genetic algorithm and parameter tuning techniques for chiller AFDD applications. This approach uses fewer physical parameters to simplify the detection and diagnosis process. Results showed that an eight parameter subset (as singled out by the SVM model) performed 2 % better than the full feature 64 parameter model. This showed that some features had more significance in terms of effectively diagnosing faults. Based on this analysis, in the opinion of the authors, if it were possible to combine the most applicable FDD methodologies into a combined method that only took key operational parameters into account, one would ensure that the least possible quantity of data was required for effective AFDD. This would ensure that the AFDD tool could be applied to the largest quantity of AHUs, while still returning high-quality results. This suggests that a reduced parameter, knowledge and expert rule-based approach will yield the most generic and commercially viable solution in the short to medium term.
Author's personal copy Energy Efficiency
What is impeding the adoption of automated AFDD tools on AHUs? In the last comprehensive review undertaken by Katipamula and Brambley (2005b), the authors suggest that more work is needed to ascertain the cause of faults as, at the time of publication, AFDD methods/tools yielded a number of possible causes for a specific fault. Most AFDD tools developed to date focus on detection and still rely heavily on user input or periods of training to diagnose the fault effectively. In addition, if multiple faults occur in the same time period, the actual cause of the fault may be difficult to identify. Again, this requires the user to apply their knowledge to diagnose the fault, a knowledge which may not always be available. In AHUs incorporating a mixing box, House et al. (2001) identified four primary modes of operation of AHUs to maintain temperature control as follows (Fig. 4): 1. Heating with minimum outside air 2. Modulation of fresh air with return air with no heating or cooling 3. Maximum outside air with cooling 4. Minimum outside air with cooling In mode 1, at low outside temperatures, the air is cold enough to bring the mixed air temperature to below the required supply temperature. At these temperatures, the outside air damper is set to its minimum position and only heating is necessary to bring the supply air temperature to the required set point. In mode 2, when the outside air is below the supply air set point but higher than design minimum conditions, the mixed air can be controlled at the required supply
temperature by modulating the dampers, thus operating without the need for mechanical heating or cooling. In mode 3, when the outside air is above the supply air set point, but below the return air temperature, the outside air is maintained at 100 % to give the lowest temperature of the recirculated air leaving the mixing box. It is still necessary to cool this air mechanically to the required supply air set point. In mode 4, when the outdoor air temperature rises above the return air temperature, the outside air damper moves to its minimum position to give the lowest possible mixed air temperature, thus minimising the mechanical cooling requirement insofar as is possible. House and Brambley also identified that there were periods when their APAR rule set could not identify the mode of operation (i.e. should the AHU be heating or cooling), thus rendering their rule set redundant. This is a key issue, as if the mode of operation of the AHU cannot be ascertained, then tests cannot be run to determine whether the unit is operating effectively or not. Significant research has been undertaken since the last review in 2005 (Katipamula and Brambley 2005b) with a view to overcoming each of these issues. Embedding AFDD Shein et al. (2006) embedded the APAR in commercial AHU controllers. The tool was tested in an emulator and using field data. At the time of their publication, Schein et al. stated that a number of AFDD tools were emerging from industry centred on stand-alone software. However, the authors suggested that because the APAR rule set used in their tool was computationally simple, it would be more scalable to embed the rule set on the commercial controller. Though there were 28 rules in total, only
Fig. 4 Typical temperature control modes utilised in most temperature only AFDD tools
Author's personal copy Energy Efficiency
a small number of temperature sensor and control signal data was required. However, if one set of data was not present, it did reduce the number of rules that could be applied. Field/emulation tests showed that APAR was effective when embedded in commercial HVAC equipment. However, it did sometimes give multiple possible fault reasons in its diagnosis due to a lack of sequencing of rules. It also had difficulty in determining the mode of operation in some instances of operation, which meant that rules were not applied and possible fault instances went undetected. Pilot studies The Sustainable Energy Authority of Ireland HVAC Special Working Group (SWG) 2007 (2011), an industry-led special initiative that was supported by the Irish government, identified that a high proportion of operational issues in HVAC systems could be identified by effective operational control of HVAC systems using an applicable tool. In phase three of the special working group (“HVAC Special Working Group—Spin III (2009)” 2011), a Microsoft Excel-based FDD tool was developed which utilised a knowledge-based expert rule set to identify faults in the operation of AHUs. This tool required manual set-up in order to apply it to the AHU under investigation. Once set up, the user manually populated the tool with BEMS data for the AHU in question. This step required a significant level of user understanding and was open to
different interpretation depending on the experience level of the user. Once populated, however, conditional formatting was utilised to effectively display faults. This process was then repeated on a periodic basis. At the time of writing, this tool has been implemented in approximately 20 large industrial sites in Ireland. Two of the key barriers to success were the availability and experience of onsite personnel and the manual nature of the data entry. This meant that a large amount of time was required to initially set the tool up to reflect the type of system under investigation, which has deterred its widespread adoption to date. Both the DABO (Choinière and Corsi 2003) tool, developed as part of the IEA annex work, and the APAR rule set have also been field tested over the last 5 years. The results of the DABO field tests are detailed in How would an AFDD commissioning tool work?, while the APAR rule set also performed well with a variety of mechanical and control faults detected and diagnosed successfully (House et al. 2001; Schein 2006). A number of other AFDD tools have emerged over the last number of years. Some of these have transitioned from research projects to full commercial ventures (“DABO 2013”), while others are still incubated within the research organisations which initially developed them (CITE-AHU by NIST). Table 2 lists the major AFDD tools which are either in later stage research and development centres or are commercially available. It also attempts to identify the FDD technique
Table 2 AFDD tools in R&D or the open market and the FDD methodologies on which they are based Tool name
Commercial stage
Company/research organisation
Building type
FDD method
DABO
Commercially available
ADMS technologies
Large Commercial
Expert system
Cite-AHU
In development
NIST
Expert system
PACRAT
Commercially available
Facility dynamics
Medium/Large commercial Large commercial
Expert system
SciWatch
Commercially available
Scientific conservation
Large commercial
Artificial neural networks
Panoptix
Commercially available
Johnson controls
Large commercial
Not known
Tririga
Commercially available
IBM
Large commercial
Not known
SkySpark
Commercially available
Skyfoundry
Large commercial
Expert system
FDD Tools
In development
NIST
Large commercial
Artificial intelligence and statistical modelling
Operational Control Spreadsheet
In development
SEAI
Industrial
Expert system
Author's personal copy Energy Efficiency
that each AFDD tool utilises in its analysis where information allows. Effective diagnostics Yang et al. (2008) used the expert rule set developed by House et al. (2001) to develop an AFDD tool. They extended the capabilities of the initial rule set to allow their tool to ascertain the cause of faults using a threestep approach which, Step 1 Tested the outdoor temp sensors for accuracy Step 2 Identified the mode of operation of the AHU Step 3 Applied a sequenced mode specific rule set to the data to more accurately define the faulty component based on a process of elimination between parallel tests involving the same sensors or components A Microsoft Excel-based tool was developed to test the process. This tool successfully diagnosed simulated supply, mixed and return air temperature sensor faults ranging between +4 and −5 K in a real building AHU in modes 3 and 4. A simulator was utilised to test the sequential rule-based analysis tool in modes 1 and 2 due to constraints that the research team had in accessing the real AHU. A fault magnitude of +10 K was introduced to the supply temperature sensor in these modes with the FDD tool successfully diagnosing its occurrence. In order to test the effectiveness of their AFDD toll, the research team then compared its results against those of another AFDD tool (CITE-AHU). A + 4 K error was introduced to the mixed temperature sensor. CITE-AHU detected this fault but was unable to diagnose it with the same level of accuracy as the proprietary tool. The more recent shift to data-driven techniques also shows potential in overcoming the diagnosis issue, though these techniques still have their constraints. Artificial neural networks (ANN) and fuzzy logic approaches both require large quantities of measured data in order to achieve accurate results (Liang and Du 2007). In the case of ANNs particularly, insufficient training data results in inaccurate learned networks and poor diagnostic ability (Zhu et al. 2012). The complicated relationship between fault and fault signal patterns causes wavelet analysis to find it difficult to accurately diagnose localised faults and their relative magnitudes (Zhu et al. 2012).
Setup issues In order to minimise false positives and negatives, the selection of detection thresholds for both faults and subsequent actions are key. As the threshold is dependent on the uncertainty associated with the individual measured data, using a single value yields a large number of false positives and false negatives. Though research has been very successful in other areas where issues were highlighted, this area has not yet been fully explored. No consensus has been arrived at with regard to a solution to this issue to date. IEA Annex 25 (International Energy Agency 1996) outlines that the simplest forms of fault thresholds involve range checking where measurements are expected to be within certain fixed bands. Ranges are based on a process displaying a normal distribution and are based on specified statistical confidence levels. Fault detection sensitivity is greatly improved using models of expected performance. A fault is detected when the difference between the predicted and measured values is above a specified threshold value. AFDD systems are classically judged on their sensitivity and false alarm rate (International Energy Agency 1996), both of which are affected by the tolerances of the error thresholds within the system. A subsequent report on this topic, IEA Annex 34 (International Energy Agency 2002), concludes that it is difficult to specify the appropriate fault sensitivities and associated error thresholds to minimise false positives. However, this is a key issue to ensure the future success of the AFDD tools developed. The earliest AFDD methods are described as using alarm limits as fault criteria. During the field testing of the APAR rule set, a list of recommended parameters was developed based on experience with a view to both minimising set up time and reducing the number of false results (Schein 2006). Yang et al. also utilised error threshold values which were selected by “consulting building operators with considerable expertise in the operation of HVAC systems” (Yang et al. 2008). Modern methods use thresholds and statistical criteria applied to sophisticated quantitative, qualitative, and process history-based models (Katipamula and Brambley 2005a). The selection of error thresholds is critical to successful implementation of an AFDD tool (House et al. 2001) as if an AFDD system provides users with alarms for a large number of false positive faults; the users may become frustrated, lose confidence in the system and even disable it completely in response.
Author's personal copy Energy Efficiency
Conversely, a large number of faults may be missed due to false negatives if error thresholds are inadequately set. Fault evaluation is the next rational step once a fault has been detected and diagnosed. Fault evaluation assesses the impact of the fault on the overall system in terms of it either being a hard (such as a breakdown) or soft failure (which would typically relate to on-going performance degradation). This evaluation must take a number of considerations into account in order to be accurate, such as economic loss, comfort impediment, safety concerns, environmental hazard associated with the fault and cost to repair the fault. Generally, as equipment should not be serviced unless the benefit justifies the expense, these criteria must be balanced in order to arrive at decision thresholds for faults (International Energy Agency 2002). Due to the complexity involved in setting appropriate fault detection and evaluation thresholds, these are still typically set in a bespoke, system-specific manner either based on prior data collection or engineering experience of similar systems. Research is active in the area of decision support systems which could be potentially utilised to support this process. An integrated energy systems optimiser has been developed by Marik et al. (2008), while Doukas et al. (2009) developed a decision support tool to assess the energy saving measures proposed for a building. However, a decision support system to carry out diagnosis of internal conditions within a HVAC system with a view to optimising energy consumption has as yet not been developed (Doukas et al. 2007). These set-up issues (detection error thresholds, adequate evaluation criteria, etc.), coupled with the IEA annex 40 (International Energy Agency 2006) conclusion that the wide-scale development of AFDD tools for commissioning is constrained by the difficulty in setting up communication with products from different vendors, means that no plug-and-play AFDD tools currently exist. Existing tools require a significant amount of set-up time prior to first use. The labour associated with this initial set-up time has the effect of significantly increasing the overall cost of the tool. Though many of the questions raised at the beginning of this section have been answered through the research and development activities detailed in Embedding AFDD and Setup issues, some issues still remain. Section six summarises these issues with a view to directing future research towards their resolution.
Proposed future research activity Based on the research described in this review, it is clear that a number of areas require further research in order to ensure the widespread application of robust commissioning assisting AFDD tools. Any future AFDD tool to support the commissioning process must be a generic, cross-platform solution so that data from any BEMS can be easily accessed. At present, the tools developed either by research institutions or commercially (e.g. PACRAT 2011, DABO (Choinière and Corsi 2003) require substantial set-up in terms of the communication required to access data, and in terms of parsing this data into a format which is accepted by the AFDD tool. No AFDD tool will be implemented on a large scale until this issue is resolved. Jang et al. (2008) agree stating that standard methods of exchanging and storing data for use by third party software applications would encourage the development of interoperable solutions to improve the monitoring of buildings. A solution may be possible either through the use of open protocols, such as BACNET (2011) or LON (2011), or the use of an intermediary transfer language such as XML. This in turn leads to a secondary problem associated with data access and interoperability. No current tools are automatically customisable in order to minimise set-up time. There is a perception in the commercial market that it costs more to implement an AFDD solution than the solution will actually save. Part of this issue relates to the labour cost required to set up current AFDD tools. This is a two-piece set-up issue which relates to both the difficulty in accessing data and the selection of error thresholds based on the specific system under analysis. This is why error threshold analysis in future AFDD tools must be firmly based on robust statistical uncertainty approaches in order to minimise false positives and false negatives. This will alleviate the current delays caused by site or systemspecific data analysis, coupled with a data training period, in order to gain confidence in the application of the AFDD tool. A robust approach to error threshold analysis would ensure that the user could quickly gain confidence in the system. Thus, less time would be misspent second-guessing the faults that are diagnosed by the tool, and more would be spent repairing them. Another critical area for future research is the requirement for any future tools to be capable of supporting both the initial and on-going commissioning processes. As detailed in How can AFDD tools aid the
Author's personal copy Energy Efficiency
commissioning process?, the commissioning process is, broadly speaking, a two-phase process (depending on where the system lies in the HVAC systems lifecycle): initial and on-going commissioning. The faults identified during the initial commissioning process should be remedied prior to conducting on-going commissioning in order for any tool to be effective in this stage of the system life cycle. The data analysis required at both stages is also different, as at the initial stage, the user may require more dynamic monitoring and report development in order to test changes for validity (e.g. system balancing). This is in contrast to the almost “background” operation of the on-going commissioning tool which will only report a fault when detected or periodically requested. The quantity of training data available during the initial commissioning step is also minimal as systems will either be new and hence have no data (in the case of initial commissioning) or in a state of fault with very little usable data against which compare (in the case of re-commissioning). For a tool to be successful during this phase of commissioning, it must be capable of operating without the need to be trained. In contrast, the on-going commissioning phase could be supported by a tool which could operate with or without training data, depending on the AFDD technique employed. Each of the previous areas for further research have centred on the makeup and initial set up of the future AFDD tool to support the commissioning process. There are also a number of areas relating to its operation which must be researched further in order to ensure that any future tool is effective and robust in its operation. Firstly, any future AFDD tool must be capable of identifying the mode of operation of the AHU in order to apply AFDD effectively. This is a complicated issue, as each AHU control system vendor will typically have their own bespoke method of applying logic to the operation of the HVAC unit. This means that it may be more apt to identify the ideal state that the HVAC unit should be operating in based on the conditions of the air entering and leaving the system and the conditions required in the space, and then apply AFDD based on this mode of operation. In this way, both control system inefficiency and faults would be detected. The tool must also be capable of correctly diagnosing faults in order to minimise the knowledge required on the part of the user. Current data visualisation programmes can help users to detect and
diagnose faults on AHUs in a HVAC system, but a large amount of time can be spent to ascertaining the causes of the possible issues (Seem 2007). The experience of the users also varies greatly with new or inexperienced users often finding it difficult to ascertain the cause of identified faults (Seem 2007). This approach is resource inefficient and unreliable, and this can cause users to lose confidence in the software. In order to aid the decision making process of fault repair and to ensure that the most process critical or economically advantageous faults are repaired first, it is critical that the tool be capable of prioritising fault actions based on their relative severity (using a number of criteria as set out in Setup issues). This will ensure that often overstretched maintenance resources are used in the most beneficial and cost-effective manner. The development of a decision support module to assist this process is key to the success of any AFDD solution. Finally, and possibly most importantly, it is critical that tools are tested on a large number of actual AHUs in HVAC systems in a variety of buildings and organisations, with validated energy saving results, in order to build a commercial case for this process. There has been some activity in this area in the recent past (Choinière and Corsi 2003), but not enough to support widespread adoption of the process. If energy savings could be captured using an internationally accepted method (e.g. the IPMVP (EVO 2011)) as demonstrated by Ginestet and Marchio (2010), this information would help to sway procurement departments that must adhere to budget constraints.
Conclusions and future work This review paper builds on and updates the previous review paper conducted by (Katipamula and Brambley 2005a, b) in the field of FDD applied to HVAC systems with particular attention to AHUs. Some interesting conclusions are apparent from this review which should be of interest to other researchers in the area. Firstly, research appears to have shifted in recent times from the development of standalone AFDD tools to integrated on-going commissioning solutions using AFDD as an enabler. This shift in philosophy seems to be driven by an
Author's personal copy Energy Efficiency
understanding that in order to carry out AFDD on AHUs in HVAC systems effectively, the systems must initially be returned to their optimal operational performance. This has led to a realisation of the importance of on-going commissioning as a means of ensuring the sustainability of efficient operation and an understanding that automated AFDD tools can be used to achieve this. Secondly, early AFDD tool development centred on the use of a single method of AFDD to detect and diagnose faults. Again, more recent research has focused on merging complementary methods of AFDD such as reduced order analysis with rule-based expert systems, for example, with a view to refining the process. Lastly, data-driven statistical analysis techniques appear to be growing in their adoption in this field of analysis. They are being utilised as a means of identifying faults (through hypothesis testing and statistical process control techniques) as well as in the difficult diagnosis process to minimise inaccurate results (through the use of probabilistic analysis). In order for a commercial commissioning assisting AFDD tool to gain widespread adoption, it must take each of the topics detailed in Proposed future research activity and the conclusions reached in this section into consideration. Most importantly, however, it must be tested and proven on a variety of real-world AHUs and HVAC systems by differing end users. Unless users see demonstrated benefits of this tool, it will be difficult to build a case for its implementation. Both the economic benefit and the resource benefits must be documented and validated in order for a commercial case to be effectively communicated. The authors are currently in collaboration with a large group of industry partners including Pfizer Ireland, Intel Ireland, Analog Devices, DePuy Johnson & Johnson, Boston Scientific and EMC to conduct research in each of these areas. This work aims to develop a generic, cross-platform AFDD tool to support both the initial and on-going commissioning processes on the AHUs in HVAC systems. This tool will be tested on a large number of AHUs across numerous sites. This is the first in a series of papers which will describe the research performed over the course of the project.
Acknowledgments This research was funded by Enterprise Ireland. The authors would like to thank the Innovation for Irelands Energy Efficiency (i2e2) Technology Centre.
References “BACnet Website.” (2011). Accessed December 6. http://www. bacnet.org/ Berton, A., & Hodouin, D. (2003). Linear and bilinear fault detection and diagnosis based on mass and energy balance equations. Control Engineering Practise, 11(1), 103–113. doi:10.1016/S0967-0661(02)00116-8. CEN. “IS EN 16001.” (2010). NSAI. http://www.cen.eu/cen/ News/PressReleases/Pages/EN16001.aspx. Chen, Y., & Lan, L. (2010). Fault detection, diagnosis and data recovery for a real building heating/cooling billing system. Energy Conversion and Management, 51(5), 1015–1024. doi:10.1016/j.enconman.2009.12.004. “DABO.” http://adms-tech.com/ongoing-commissioning software. Accessed 27 May 2013. Choinière, D., Corsi, M. (2003). “A BEMS Assisted Commissioning Tool to Improve the Energy Performance of HVAC Systems.” In ICEBO. http://www.commissioninghvac.org/files/doc/Choiniere__ICEBO.pdf Djuric, N., & Novakovic, V. (2009). Review of possibilities and necessities for building lifetime commissioning. Renewable and Sustainable Energy Reviews, 13(2), 486–492. doi:10. 1016/j.rser.2007.11.007. Doukas, H., Nychtis, C., & Psarras, J. (2009). Assessing energy-saving measures in buildings through an intell i g e n t d e c i s i o n s u p p o r t m o d e l . Bu i l d i n g a n d Environment, 44(2), 290–298. doi:10.1016/j.buildenv. 2008.03.006. Doukas, H., Patlitzianas, K. D., Latropoulos, K., & Psarras, J. (2007). Intelligent building energy management system using rule sets. Building and Environment, 42(10), 3562–3569. doi:10.1016/j. buildenv.2006.10.024. Du, Z., Jin, X., & Wu, L. (2007). Fault detection and diagnosis based on improved PCA with JAA method in VAV systems. Building and Environment, 42(9), 3221–3232. doi:10.1016/j. buildenv.2006.08.011. Du, Z., Jin, X., & Yang, Y. (2009). Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network. Applied Energy, 86(9), 1624–1631. doi:10. 1016/j.apenergy.2009.01.015. EVO. “IPMVP.” http://www.evo-world.org/index.php? option=com_content&view=article&id=272&Itemid= 504&lang=en/. Accessed 14 September 2011. Facility Dynamics. “PACRAT.” http://www.facilitydynamics. com/pacrat.html/. Accessed 13 September 2011 Ginestet, S., & Marchio, D. (2010). Retro and on-going commissioning tool applied to an existing building: operability and results of IPMVP. Energy, 35(4), 1717–1723. doi:10.1016/j. energy.2009.12.024. Gordon, L. (2002). Modelling user acceptance of building management systems. Automation in Construction, 11(6), 695– 705. doi:10.1016/S0926-5805(02)00010-9. Han, H., Gu, B., Kang, J., & Li, Z. R. (2011a). Study on a hybrid SVM model for chiller FDD applications. Applied Thermal Engineering, 31(4), 582–592. doi:10.1016/j.applthermaleng. 2010.10.021. Han, H., Gu, B., Wang, T., & Li, Z. R. (2011b). Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning.
Author's personal copy Energy Efficiency International Journal of Refrigeration, 34(2), 586–599. doi: 10.1016/j.ijrefrig.2010.08.011. Hao, X., Zhang, G., & Chen, Y. (2005). Fault-tolerant control and data recovery in HVAC monitoring system. Energy and Buildings, 37(2), 175–180. doi:10.1016/j.enbuild.2004.06.023. House, J. M., Vaezi-Nejad, H., Whitcomb, J. M. (2001). “An Expert Rule Set for Fault Detection in Air Handling Units.” Ashrae Transactions Part 1 (107). http://www.nist.gov/ manuscript-publication-search.cfm?pub_id=860823 “HVAC Special Working Group – Spin III, 2009.” (2011). Accessed September 13. http://www.seai.ie/Your_Business/ Large_Energy_Users/Special_Initiatives/Special_Working_ Groups/HVAC_SWG_Spin3/ “HVAC SWG - Spin I, 2007.” (2011). Accessed September 13. http:// www.seai.ie/Your_Business/Large_Energy_Users/Special_ Initiatives/Special_Working_Groups/HVAC_SWG_07/ International Energy Agency. (1996). “Real-time HEVAC simulation”. 25 International Energy Agency. (2002). “Computer-aided evaluation of HVAC system performance” 34 International Energy Agency. (2006). “Commissioning of Bui l din g H VA C Sys t e m s fo r I m pro ve d E ne rgy Performance”. 40. International Energy Agency. (2010). “Cost-effective commissioning of existing and low energy buildings” 47. ISO - International Organisation for Standardisation, and ISO International Organisation for Standardisation. (2011). “ISO International Organisation for Standardisation”. Text. Accessed September 13. http://www.iso.org/iso/catalogue_ detail?csnumber=51297 “ISO 13381–1:2004 - Condition Monitoring and Diagnostics of Machines – Prognostics – Part 1: General Guidelines.” 2011. Accessed December 6. http://www.iso.org/iso/iso_catalogue/ catalogue_tc/catalogue_detail.htm?csnumber=21841 Jang, W.-S., Healy, W. M., & Skibniewski, M. J. (2008). Wireless sensor networks as part of a web-based building environmental Monitoring system. Automation in Construction, 17(6), 729–736. doi:10.1016/j.autcon.2008.02.001. Karlsson, M. (2011). The {MIND} method: a decision support for optimization of industrial energy systems—principles and case studies. Applied Energy, 88(3), 577–589. doi:10.1016/ j.apenergy.2010.08.021. Katipamula, S., & Brambley, M. R. (2005a). Review article: methods for fault detection, diagnostics, and prognostics for building systems—a review, Part I. HVAC&R Research, 11(1), 3–25. doi:10.1080/10789669.2005.10391123. Katipamula, S., & Brambley, M. R. (2005b). Review article: methods for fault detection, diagnostics, and prognostics for building systems—a review, Part II. HVAC&R Research, 11(2), 169–187. doi:10.1080/10789669.2005.10391133. Le, T. H., Knabe, G., & Henze, G. P. (2005). Fault Detection and Diagnosis for Control Strategies in Air Handling Units. Montreal: In IBPSA. Lee, S. H., & Yik, F. W. H. (2010). A study on the energy penalty of various air-side system faults in buildings. International Conference on Building Energy and Environment (COBEE 2008), 42(1), 2–10. doi:10.1016/j.enbuild.2009.07.004. Lee, S. Uk., Painter, F. L., Claridge, D. E. (2007) “Whole Building Commercial HVAC System Simulation for Use in Energy Consumption Fault Detection.” Ashrae Transactions 113 (Part 2).
Liang, J., & Du, R. (2007). Model-based fault detection and diagnosis of HVAC systems using support vector machine method. International Journal of Refrigeration, 30(6), 1104– 1114. doi:10.1016/j.ijrefrig.2006.12.012. Lo, C. H., Chan, P. T., Wong, Y. K., Rad, A. B., & Cheung, K. L. (2007). Fuzzy-genetic algorithm for automatic fault detection in HVAC systems. Applied Soft Computing, 7(2), 554–560. doi:10.1016/j.asoc.2006.06.003. “LonMark International.” 2011. Accessed December 6. http:// www.lonmark.org/ Mařík, K., Schindler, Z., & Stluka, P. (2008). Decision support tools for advanced energy management. Energy, 33(6), 858– 873. doi:10.1016/j.energy.2007.12.004. Matelli, J. A., Bazzo, E., & da Silva, J. C. (2011). Development of a case-based reasoning prototype for cogeneration plant design. Applied Energy, 88(9), 3030–3041. doi:10.1016/j. apenergy.2011.03.006. Pakanen, J. E., & Sundquist, T. (2003). Automation-assisted fault detection of an air-handling unit; implementing the method in a real building. Energy and Buildings, 35(2), 193–202. doi: 10.1016/S0378-7788(02)00050-6. Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394–398. doi:10.1016/j.enbuild.2007.03. 007. Piette, M. A., Kinney, S. K., & Haves, P. (2001). Analysis of an information monitoring and diagnostic system to improve building operations. Energy and Buildings, 33(8), 783–791. doi:10.1016/S0378-7788(01)00068-8. Salsbury, T. I., & Diamond, R. C. (2001). Fault detection in HVAC systems using model-based feed forward control. Energy and Buildings, 33(4), 403–415. doi:10.1016/S0378-7788(00) 00122-5. Schein, J. (2006). “Results from Field Testing of Embedded Air Handling Unit and Variable Air Volume Box Fault Detection Tools”. NIST. http://fire.nist.gov/bfrlpubs/build06/art033.html Schein, J., Bushby, S. T., Castro, N. S., & House, J. M. (2006). A rule-based fault detection method for air handling units. Energy and Buildings, 38(12), 1485–1492. doi:10.1016/j. enbuild.2006.04.014. Seem, J. E. (2007). Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1), 52–58. doi:10.1016/j.enbuild.2006.03.033. Sikorska, J. Z., Hodkiewicz, M., & Ma, L. (2011). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), 1803–1836. doi:10.1016/j.ymssp.2010.11.018. Song, Y.-h., Akashi, Y., & Yee, J.-J. (2008). A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems. Energy and Buildings, 40(2), 71– 82. doi:10.1016/j.enbuild.2007.01.011. Wang, H., Chen, Y., Chan, C. W. H., & Qin, J. (2012a). An online fault diagnosis tool of VAV terminals for building management and control systems. Planning Future Cities-Selected Papers from the 2010 eCAADe Conference, 22(March), 203– 211. doi:10.1016/j.autcon.2011.06.018. Wang, H., Chen, Y., Chan, C. W. H., Qin, J., & Wang, J. (2012b). Online model-based fault detection and diagnosis strategy for VAV air handling units. Cool Roofs, Cool Pavements, Cool Cities, and Cool World, 55(December), 252–263. doi:10.1016/ j.enbuild.2012.08.016.
Author's personal copy Energy Efficiency Wang, H., Chai, T.-Y., Ding, J.-L., & Brown, M. (2009). Data driven fault diagnosis and fault tolerant control: some advances and possible new directions. Acta Automatica Sinica, 35(6), 739–747. doi:10.1016/S1874-1029(08)60093-2. Wang, S., & Xiao, F. (2004). AHU sensor fault diagnosis using principal component analysis method. Energy and Buildings, 36(2), 147–160. doi:10.1016/j.enbuild.2003.10.002. Wang, S., Zhou, Q., & Xiao, F. (2010). A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energy and Buildings, 42(4), 477–490. doi: 10.1016/j.enbuild.2009.10.017. West, S. R., Yinh Guo, X., Wang, R., & Wall, J. (2011). Automated Fault Detection and Diagnosis of HVAC Subsystems Using Statistical Machine Learning. Sydney: IBPSA. Wu, S., & Sun, J.-Q. (2011). Cross-level fault detection and diagnosis of building HVAC systems. Building and Environment, 46(8), 1558–1566. doi:10.1016/j.buildenv.2011.01.017. Xiao, F., & Wang, S. (2009). Progress and methodologies of lifecycle commissioning of HVAC systems to enhance building sustainability. Renewable and Sustainable Energy Reviews, 13(5), 1144–1149. doi:10.1016/j.rser.2008.03.006.
Yang, H., Cho, S., Tae, C.-S., & Zaheeruddin, M. (2008). Sequential rule-based algorithms for temperature sensor fault detection in air handling units. Energy Conversion and Management, 49(8), 2291–2306. doi:10.1016/j.enconman. 2008.01.029. Yoshida, H., & Kumar, S. (2001). Development of {ARX} model based off-line {FDD} technique for energy efficient buildings. Renewable Energy, 22((1–3), 53–59. doi:10.1016/ S0960-1481(00)00033-1. Yoshida, H., Kumar, S., & Morita, Y. (2001). Online fault detection and diagnosis in VAV Air handling unit by RARX modelling. Energy and Buildings, 33(4), 391–401. doi:10. 1016/S0378-7788(00)00121-3. Yu, B., Dolf, H. C., Paassen, V., & Riahy, S. (2002). General modelling for model-based FDD on building HVAC system. Simulation Practise and Theory, 9(6–8), 387–397. doi:10. 1016/S1569-190X(02)00062-X. Zhu, Y., Jin, X., & Du, Z. (2012). Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal. Energy and Buildings, 44(January), 7– 16. doi:10.1016/j.enbuild.2011.09.043.
