20th European Symposium on Computer Aided Process Engineering – ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) © 2010 Elsevier B.V. All rights reserved.
A Distributed Intelligence System for Improving Fault Diagnostic Performance in Large Scale Chemical Processes Sathish Natarajan, Rajagopalan Srinivasan* Department of Chemical and Biomolecular Engg, National University of Singapore 10 Kent Ridge Crescent, Singapore 119260
Abstract Faults in large-scale chemical plants could occur at a process level although, more often, faults occur at the instrument and equipment level. A failure in an equipment could quickly propagate throughout the process resulting in leaks, fires and explosions causing loss of life, capital invested and production downtime. Attempting to monitor the overall process for identifying such instrument and equipment level failures may be futile as deviations in the process are often lagging indicators by which time the plant safety may be compromised. Hence, we propose a distributed process monitoring system which uses multiple FDI methods/agents capable of monitoring the plant at various sections, levels of granularity (tag level to unit level) and on various operating states. The process is divided into multiple scales and multiple operating states and FDI agents are developed at these scales/states. When multiple FDI agents are used they need to effectively interact with one another. Hence, a Process Ontology is developed to explicitly capture the hierarchy of the process. Since different types of faults at different levels of granularity in the plant could occur, a Fault Ontology is also developed and mapped to the process ontology. The proposed approach, called ENCORE, has been implemented as a multi-agent system and its efficacy is demonstrated on an offshore natural gas production platform. Keywords: multi agent systems, ontology, distributed system, fault detection and identification
1. Introduction Fault Detection and Identification in chemical process industries has been an active area of research for over three decades. Early and precise detection of process faults is essential to prevent off-spec products and also in many cases to prevent explosions and other serious accidents. The focus from academia has by and large been on developing a single monolithic monitoring strategy for process industries (Venkatasubramaniam et al, 2003). But the sheer size and nature of chemical processes make the application of these monolithic strategies, which are computationally intensive, to real plants difficult. Moreover, a single method may not be capable of handling all the fault scenarios as each monitoring algorithm has its own inherent advantages and disadvantages. Improvement and robustness in monitoring chemical processes could be achieved by using multiple methods and by combining their results in a meaningful manner. *Corresponding Author Ph: +65 65168041, Email:
[email protected]
Natarajan and Srinivasan. Faults in chemical process industries could occur at the process level, section level or more often at the equipment level. Attempting to monitor the overall process for identifying such instrument and equipment level failures may be futile as deviations in the process are often lagging indicators by which time the plant safety may be compromised. Hence multiple FDI agents capable of monitoring the plant at various sections and varying levels of granularity (tag level to unit level) are developed. For instance, a FDI agent is developed for compressor units (equipment level), another for a separator section and so on, each of which is specialized in its domain. Chemical processes also operate at a multitude of operating states and developing a single method capable of monitoring the process becomes difficult. Therefore, multiple agents are developed, each capable of monitoring the process at a given state. When multiple FDI agents are used they need to effectively interact with one another, hence on Ontology is developed. Ontologies such as OntoCAPE (Morbach et al, 2009) have been developed for process industries to aid in simulation, documentation and other activities but as far as the authors are aware none have been developed with the intent of fault detection and identification. A Process Ontology is developed to explicitly capture the hierarchy of the process. Since different types of faults at different levels of granularity in the plant could occur, a Fault Ontology is also developed and mapped to the process ontology. A key issue in monitoring any complex system using multiple independent methods in parallel is that the individual methods may not always concur and are often in conflict with each other. A suitable coordinator/consolidator agent is used which performs the matching between the results from the various FDI agents by mapping their evidences to the process and fault ontologies and seeking coherence among the various evidences. This multi agent system called ENCORE is described next followed by its application to an industrial offshore natural gas production platform.
2. ENCORE A Multi Agent system is one that consists of a number of agents, which interact with one another. ENCORE agents encapsulate certain knowledge of a process and contain routines/algorithms capable of completing a certain task. Each class of agent is platform independent; they can be initiated and executed on different hosts. Each agent does not necessarily form a complete application but rather as a reusable, self-contained piece of routine that can rationale its own decision based on process operation. 2.1. Java WADE Implementation ENCORE architecture is implemented using Java WADE (Workflows and Agent Development Environment). The agents in WADE (see WADE user guide) are compliant with the FIPA (Foundation of Intelligent Physical Agents) specifications, which enables inter-operability among agents. WADE is a domain independent software platform built on top of JADE (Java Agent DEvelopment Environment) to facilitate the development of distributed application based on the agent oriented paradigm. This architecture provides a distributed runtime environment, the agent and behavior (task performed by an agent) abstractions, peer to peer communication between agents and basic agent lifecycle management (WADE user guide).
A distributed system for improving fault diagnostic performance in chemical processes.
Ontology
Agents Plant Operators
DCS
Process Data Monitoring/ Diagnosis Agent
Data Beliefs Data Beliefs
Monitoring/ Diagnosis Agent
Beliefs
Data Manager Agent Data Beliefs
Monitoring/ Diagnosis Agent
(a)
User Interface Agent
Beliefs Data
Consolidator Agent
ENCORE
(b)
Figure 1. ENCORE Architecture – (a) Agent structure, (b) Ontology
2.2. Architecture ENCORE contains the following classes of agents: Data Manager Agent acts as a data repository where all the data from the other agents are collated and stored. Monitoring/Diagnosis Agent contain the algorithms necessary for performing fault detection and identification within their scope of applicability. Consolidator/Decision Fusion Agents perform mapping of the evidences from the various Monitoring/Diagnosis agents using techniques such Bayesian probability, DempsterShafer or other fusion strategies to arrive at meaningful results. User Interface Agent is the agent which queries the Data Manager Agent and displays the results requested by the user. Figure 1(a) shows the structure of ENCORE A key feature of this architecture is the ability to start and stop agents on a need basis. For instance a specialized compressor monitoring agent can be active only when a compressor is in operation. Whenever a monitoring or diagnosis agent is started it subscribes to the data manager agent for process data. Similarly a consolidator agent subscribes for monitoring/diagnosis beliefs from the data manager agent. The sequence of actions when a new process sample from a DCS is available is as follows: The data manager agent notifies the monitoring/diagnosis agents of the availability of new process data which is then sent to these agents. These agents use their trained models to map this new process data to process condition. The monitoring beliefs, either process fault or normal is sent to the data manager agent where it is logged and stored. The data manager notifies the consolidator agent of the availability of these monitoring/diagnosis beliefs which is then sent to the consolidator agent. The consolidator agent maps these various evidences based on the Process and Fault Ontology to arrive at a meaningful consolidated belief for the particular process sample. This belief is in turn logged and stored by the data manager agent. The user could request the user interface agent to query the data manager agent at any stage and retrieve the relevant beliefs. Since ENCORE uses a variety of agents, each specialized in a particular domain and applicable at different levels of granularity an ontology is necessary for efficient interaction between them. Ontology consists of a semantic knowledge representation which can be reused and shared across domains and by different users. Ontologies are seen as means to effectively build knowledge – based systems. A Process and Fault
Natarajan and Srinivasan. Ontology is developed to explicitly capture the hierarchy of the process and the faults. Figure 1(b) shows this ENCORE Ontology. The key features of this ENCORE architecture are the ability to start and stop agents on a need basis, use of a consolidator agent to map the various different pieces of evidences and the fact that ENCORE is not dependent on a dedicated server. The application of this architecture to an offshore natural gas production platform is demonstrated next.
3. Application to offshore natural gas production platform A dynamic model of an offshore natural gas production platform was developed. It included three wellheads consisting of the normal combination of X-mas valves, production header, separator header, test separator and a compressor section. Maintenance activities common on offshore platforms were also simulated and the transients captured (see Natarajan and Srinivasan, 2009 for full model details). The data from this dynamic model was used to train monitoring/diagnosis agents. An instance of the ENCORE ontology for this case study is created. The hierarchical relationship is captured by describing a Master Valve isPartOf Wellhead. Master Valve isConnectedTo Wing Valve and so on for all the components in the case study as shown in Figure 2. The application of ENCORE in two different scenarios is described next: 3.1. Illustration 1:Temperature controller fault In this scenario, ENCORE uses three Principal Component Analysis based monitoring agents, a) monitoring the overall process, b) monitoring the separator section and c) monitoring the compressor section (Figure 2) along with a Bayesian probability based consolidator agent (Natarajan et al, 2009). The three monitoring agents and the consolidator agent are active throughout this case study. A test separator temperature controller fault is introduced at sample 100 in which during separator operation heat input to the separator was reduced resulting in temperature drop by 2%. Being specially suited to detect abnormalities around the separator, the separator monitoring agent detects and diagnoses the temperature controller fault with a delay of only 4 samples (2 minutes), while the process monitoring agent detects with a delay of 16 samples (8 minutes) and the compressor monitoring agent does not detect this fault. Upon consolidation with Bayesian probability based consolidator agent, a fault is flagged with a delay of 4 samples (2 minutes) as shown in Figure 3. By using a consolidator, we obtain a 50% increase in the fault recognition rates (samples correctly classified) over the individual agents (Natarajan et.al, 2009). Thus by using multiple agents at different levels of granularity and by using a suitable consolidator agent faults could be detected and diagnosed at their inception and the overall robustness is improved.
Figure 2. ENCORE application – hierarchical structure of offshore natural gas production.
A distributed system for improving fault diagnostic performance in chemical processes.
Evidence of Temperature Controller Fault
Agent results Process Monitoring Agent Separator Monitoring Agent Compressor Monitoring Agent Bayesian Fusion 1
Fault Detected Sample 104
0.8
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Fault Detected Sample 104 Fault Detected Sample 116
0.4
0.2
0 Bayesian Fusion 120
Compressor Monitoring Agent
115 110
Separator Monitoring Agent
105 100
Process Monitoring Agent
95
Fault Introduced Sample No. (x30 seconds)
Figure 3. ENCORE application –Illustration 1 – all agent results for temperature controller fault
3.2. Illustration 2: Valve Leak detection during different operating states Offshore natural gas production platforms undergo many maintenance activities on a frequent basis. These may be Corrosion Inhibitor (CI) Injection operation or Kinetic Hydrate Inhibitor (KHI) injection operation or Pig Launching operation. Developing a single agent capable of monitoring the platform during these multitudes of operating states becomes difficult. Hence, state based multiple agents are developed, each capable of monitoring the platform during CI, KHI ramp up and normal operation. These agents are deployed into ENCORE and are activated based on determining the state of the system. The state of the system is determined by using k-means clustering of the process data.
Start CI operation
CI Process Monitoring Agent Normal Process Monitoring Agent
Normal Process Monitoring Agent
Figure 4. ENCORE application – Illustration 2 – A CI monitoring agent becomes active as the platform undergoes a CI Injection operation and valve leak fault is detected through the different operating states
Natarajan and Srinivasan. A leak in the master valve of a flow line was simulated in which the flow through the line dropped by 15% as a step change during an instance of the CI injection operation. Figure 4 shows the characteristics of the master valve flow and the total platform flow with this leak fault. In Figure 4, the first 897 minutes correspond to the normal CI injection operation and the currently active agent was CI monitoring agent. The leak fault was introduced at sample 898. The CI monitoring agent flagged the fault at t = 899. The fault continued to be detected even as the platform went through the remainder of the transition and returned to the subsequent normal operation. To demonstrate the robustness of the proposed strategy, the same fault was also introduced during the other two operating states – KHI injection and ramp up operation (not shown). ENCORE activated the corresponding agent for each of these states and the valve leak fault was detected promptly.
4. Conclusion A distributed multi agent based process monitoring system called ENCORE has been developed in the Java WADE environment. Each monitoring algorithm is considered as an agent. Since the agents may exist at varying levels of granularity in the process, a hierarchical process and fault ontology is developed. This hierarchical scheme was implemented in the offshore natural gas production case study. It was demonstrated that by using multiple agents at different levels of granularity and by using a suitable consolidator agent which does evidence mapping using a shared ontology, faults could be diagnosed at their inception and the overall robustness and performance of the monitoring system is improved. This architecture allows for the agents to be started and stopped on a need basis. This feature was demonstrated by implementing a state based monitoring scheme to the offshore natural gas case study. Being FIPA – compliant ENCORE agents could co-exist and co-operate with agents developed in other implementation packages. This implementation is inherently multi threaded; hence can seamlessly scale out to additional processors. The architecture is not dependent on file based message transfer and does not require a dedicated server. ENCORE is ideally suited for deployment as a remote distributed process monitoring system.
Acknowledgement The authors are grateful for the financial support from Maritime and Port Authority of Singapore.
References [1] V. Venkatasubramanian., R. Rengaswamy., S.N. Kavuri. 2003, A review of process fault detection and diagnosis , Computers and Chemical Engineering, Vol 27,3. [2] WADE user Guide. http://www.jade.tilab.com/wade/doc/WADE-User-Guide.pdf [3] J.Morbach., A.Wiesner., W.Marquardt., 2009, OntoCAPE – A (Re)Usable ontology for computer aided process engineering, Computers and Chemical Engineering, Vol 33, 1546 – 1556. [4] S.Natarajan., K.Ghosh., R.Srinivasan., 2009, Collaborative Multi Agent based process monitoring system for offshore oil and gas production, 10th International Symposium on Process Systems Engineering, PSE-2009. [5] S.Natarajan., R.Srinivasan., Multi model based process condition monitoring of offshore oil and gas production process, Article in Press, Chemical Engineering Research and Design, doi: 10.1016/j.cherd.2009.10.013. [6] Y.S. Ng., R.Srinivasan., 2010, Multi agent based colloborative fault detection and identification in chemical processes, Engineering application of Artificial intelligence (under review)
