Knowledge based systems for machine health monitoring Nadakatti Mahantesh1, Parida Aditya2, UdayKumar3 1
Luleå University of Technology, Sweden,
[email protected] Luleå University of Technology, Sweden,
[email protected] 3 Luleå University of Technology, Sweden,
[email protected] 2
Keywords – world class maintenance, knowledge based systems, machine health monitoring, artificial intelligence, condition based maintenance
Abstract
Artificial Intelligence (AI) are relatively newer techniques that have evolved for machinery fault diagnosis and effective plant maintenance (Fink and Lusth, 1987, Basu, et.al., 1991). Advancements in the field of computers and internet has led to increased research interest in the use of Artificial Intelligence (AI) tools like knowledge based systems, expert systems and neural networks (Labib, 1998, Zhou, et.al., 2000). These are now being extensively employed in today’s industrial machinery for effective monitoring of commonly occurring malfunctions to rarely occurring emergency situations. The machine parameters like vibrations, wear debris, lubricant condition, temperature, oil pressure, etc., are measured externally on operating machines contain as much information as to their condition, as, machines in normal condition have a characteristic vibration signature, while most faults change this signature in a well-defined way (Randall, 2008). There are various sources for such machinery health parameters like vibrations and wear related to shaft speed, electrical machines, gears, bearings, etc. The faults manifesting themselves at various frequencies corresponding to these sources could be unbalance, misalignment, cracked shaft, electrical machine faults due to stator problems, gear tooth faults, slips in gears (Malcom, 2008), bearing cracks, excessing wear of balls, rollers, pistons, liners, pumps, fans and blowers, turbine rotors (Bebee, 2003), blades, etc. Corresponding to these faults there are various condition monitoring equipment with suitable sensors to detect the machine signatures. The signature thus obtained could be analysed for potential fault detection and suitable maintenance action could be initiated at appropriate time before the fault develops into alarming levels. During this process lot of data is generated and well-designed / custom built knowledge based systems could be of great advantage to store these data, compare the same with a good machine signature and suggest the potential problems along with suitable remedial
The advances in manufacturing technology and the competition in the market necessitate the continuous availability of machinery for production. This has created a need for effective and efficient maintenance practices resulting in improved plant performance. Instead of the common fragmented approaches to maintenance, an integrated approach is recommended. One such approach to effective maintenance is, Knowledge Based Maintenance Systems for machine health monitoring which can be integrated with plant maintenance. A fault diagnosis system with Knowledge Based System (KBS) is based on computer programs interlinking fault symptoms, faults and remedies. A comprehensive KBS system can be developed for industrial machinery which can monitor the major machinery faults or abnormalities of the machining centres and provide expert maintenance solutions through measurement and analysis of machine health parameters such as vibration, temperature, wear debris, lubricant condition, etc. These solutions are based on published information about permissible machine parameters in handbooks, journals and conferences, past maintenance experiences and from machine expert’s knowledge regarding specific machinery problem and its solution. The objective of the present research paper is, to suggest one such system for machine health monitoring which would be a useful maintenance tool in a majority of small and medium scale manufacturing units. The KBS for machine health monitoring has modules on various aspects of machine health monitoring. Many of these sections are interactive type with easy-to-answer questions, posed by the system. Based on the user’s response and built-in knowledge base, comparisons are made by the system and appropriate maintenance solutions are suggested to the used. 1. Introduction
Knowledge based systems and tools based on -1-
solutions (Zhou, et.al.,2000 and Gang, et.al., 2010). This paper deals with one such approach of developing a machinery maintenance knowledge base with rule-based capabilities to compare the condition monitoring data with allowable standards as per the machine classification or category (ISO 10816:1-5, VDI 2056, BS 4675) and suggest the exact machine problem and a suitable solution. Over last couple of decades more concentrated efforts are made to log the machine failures and more monitoring of relevant parameters but it has been realized that there is a need for either an automatic system which predicts failures before they cause damage and breakdown or a fault tolerant system which is insensitive to failures (Martin, 1994). Machine “health” monitoring is a term used for observation of machinery condition using sensors on either continuous basis (on-line condition monitoring) or off-line basis (off-line condition monitoring). Most of the machines or systems have tolerance on their operating parameters. Just-in-time (JIT) manufacturing methods and increasing pressure to improve the efficiency requires an increased and more predictable availability of manufacturing machinery. Maintenance philosophy has changed from the reactive fix-it-when- broke to predictive, being able to diagnose potential problems. Production losses are minimized by planning machinery shut down and preventing minor problems developing into major ones (Bresser and Griffiths, 1993). The concept of World Class Maintenance (WCM) has emerged which is providing opportunities and challenges to the Operations and Maintenance specialists. The need for WCM programs is receiving top-level corporate attention (Smith, et.al., 2003). WCM has been defined and explained by various researchers to denominate a model which is newer, different and effective for the maintenance function, with a strategic vision and contributions to the results of the business. The function of maintenance with the optics of world-class is interpreted like a strategic capacity that a company has and allows it to compete through a good integral management of equipment throughout the service life (Labib, 1998, Rambababu, et.al., 2009). As highlighted by the classic P-F curve (Moubray,1997) there will be a time during which a fault is generated when the function of the system is still acceptable. When these parameters grow beyond “acceptable” limits, suitable remedial actions can be suggested by the knowledge based systems developed specifically for such machines or plants, making the maintenance
function more effective for improvement of productivity, availability and reducing the downtime and related costs.
2. Literature review A lot of knowledge has been gained over the past couple of decades about various causes for machinery problems and their effects on plant performance(Neale, 1979). Some of the major machine problems could be due to excessive vibrations, wear, worn-out bearings, damaged roller bearings, mechanical looseness, faulty beltdrive, unbalanced reciprocating forces and couples, increased turbulence, electrically induced vibrations, misalignment, bent shaft, etc. (Harris and Piersol, 1996). Many researchers have conducted studies to combine the knowledge of condition monitoring with that of information technology and sensors to come up with knowledge based systems for machine health monitoring for better plant performance with higher availability and reliability leading to better productivity and reduced downtime. The literature review has been grouped into four broad categories: emergence of AI based research for maintenance applications, expert systems development for fault diagnosis, AI based models and algorithms for condition monitoring applications and finally the latest maintenance concept of World-Class-Maintenance (WCM). Research on use of Artificial Intelligence (AI) techniques for machinery maintenance purpose can be traced to the mid 80’s. The expert systems for fault diagnosis emphasized on “deep knowledge”. The researchers realized the limitations of such an approach because of available knowledge which can be represented for developing expert systems (Fink and Lusth, 1987). Models were developed for conditionbased health monitoring for rotating machinery using neural networks and finite-element modelling. The model integrated machinery sensor measurements through neural networks specifically trained for responding to the machine element being monitored. The proposed models had the advantage of developments in the neural networks field and finite-element-analysis. (Roemer et.al., 1996). Fuzzy models were used for residual generation and fuzzy logic for residual evaluation. The idea of “Knowledge Observer” was introduced to residual generation. (Frank and Köppen-Sliger,,1997). Further developments tooks place in the field of computers and expert system developments leading to increased research interest in the application of AI tools like Knowledge-Based Systems (KBS), Expert Systems (ES), Fuzzy-Logic, Neural Networks, etc., for machinery fault diagnosis. Models were
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developed for vibration analysis and machine condition monitoring for data interpretation, and analysis which proved to be very useful tools for both laboratory and on-site maintenance departments of large manufacturing and mineral processing plants (Frank and Köppen-Seliger, 1997, Ebersbach and Peng, 2008). Case-BasedReasoning (CBR) and Knowledge-Based Maintenance (KBM) tools were used for developing industrial condition monitoring applications. The models tried to improve the quality by reuse of experience (Nadakatti, et.al., Olsson and Funk, 2009). With wider acceptance of intelligent systems, machinery fault diagnostic models were developed for such important applications like condition monitoring of nuclear power plants. Some models used the data-fusion techniques for extracting vibration trend features. These features were used for generating alarm signals when the threshold limits were exceeded and also for calculating Remaining Useful Life RUL (Gang and Yang, 2010, West, et.al. 2012). Expert systems found their applications for mining and petro-chemical industries for on-line and offline condition monitoring and fault diagnosis. Review papers were published about the developments of expert systems for mining industries in various countries. Models were proposed to explore the possibility of component wear prediction for catastrophic failures (Basu et.al., 1991, Martin, 1994, Peck and Burrows, 1994). Majority of the AI based models were aimed at finding some sort of fault diagnostic solutions for various condition monitoring techniques. Data collected through such models were found to be useful for maintenance functions in major applications like airlines, oil refineries and breweries, etc., (Bresser and Griffiths, 1993). Online and off-line condition monitoring and fault diagnosis models based on AI technology were developed for integrating into a closed-loop diagnostic tool for complex systems in transport systems like trains and aircraft engines (Vingerhoeds, et.al., 1995, Ming, et.al.,1998). Along with developments in the field of AI, research also continued for performance analysis of steam turbines (Bebee, 2003), machinery condition monitoring (Randall, 2008, Malcom, 2008), condition-based-maintenance by data fusion and reliability-cantered maintenance (Gang, et.al., 2010). The proposed methodologies aimed at optimizing maintenance cost, improve condition monitoring, health assessment and prognostics. Knowledge-driven approach was explored for aerospace condition monitoring (Phillips and Diston, 2011). Studies were conducted for machinery diagnostics and prognostics by data acquisition, data processing,
and maintenance-decision-making. Researchers faced difficulties in integration of data from multiple sensors (Jardine , et.al., 2006). Over last two decades rapid developments took place in the manufacturing field leading to complex and expensive machinery. Any downtime with such systems could mean huge losses to the organization because of the reduced production and very high maintenance costs. This resulted in the development of integrated condition monitoring and fault diagnosis techniques (Zhou, et.al., 2000). In order to overcome the various shortcomings of fragmented approaches to maintenance leading to huge losses, the present day manufacturers are aiming at World-Class-Maintenance (WCM) which can enhance maintenance efficiency, reliability and reduce the downtime and hence production losses. (Labib, 1998, Smith, et.al., 2003, Rambabu, et.al., 2009). With the increase in maintenance demands, models are being developed for 24 / 7 connectivity, data security, and knowledge-sharing (Christos, et.al, 2009). As can be seen from the literature review, over the last two to three decades, lot of research has been carried to use the AI techniques for maintenance purposes. While some of the methodologies were for some particular application like mining, petro-chemicals, manufacturing, etc., some others were for machinery in-general. Most of the models aimed at extracting more and more machinery knowledge for better fault diagnosis using various AI techniques.
3. Knowledge based system machine health monitoring
for
With the advent of AI, a whole range of new technologies have been developed to aid various applications. Machine health monitoring is one area in which many AI tools like expert systems, knowledge-based systems, fuzzy-logic, neural networks, etc. have been employed to extract machine health data: on-line and off-line basis. This data is compared with standard / ideal data for various health parameters of that machine (ISO 10816: 1-5 1995, VDI 2056, ISO 2372 & BS 4675) and analysis is made. The system gives the condition of machine with possible remedial actions. Advanced knowledge based machine health monitoring systems are integrated with online monitoring of the critical machines, so that for critical parts, maintenance actions are initiated well in time before any catastrophic failure occurs. Some of the applications of KBS for maintenance are : industrial automation / automated monitoring of manufacturing processes, detecting and diagnosing localized defects in bearing systems, plant monitoring, process fault diagnosis, auto-
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MAINTENANCE. KNOWLEDGE
DATABASE (Maintenance DB) DATA
MAINT. KNOWLEDGE BASE (RULES, FACTS,ETC.)
1: Knowledge Representation Component
3: Support Environment
INFERENCE ENGINE
2: Problem Solving Mechanism
DATA ALGORITHM
USER INTERFACE Inputs by User: M/c Problem specific information.
USER
Outputs from System: Advise, Diagnosis, Help & Explanation for M/c problems
Figure Fig.No. 1 1 Knowledge Based System for Machine Health Monitoring body assembly process, bearing maintenance schedule, on-line fault detection, real-time expert systems for plant-wide maintenance management, etc.
A knowledge-base can be built from these faults and the remedial actions done over the years, from the machine manuals, machinery handbooks, vibration handbooks, or from condition monitoring handbooks (Neale 1979, Harris and Piersol, 1996, Higgins, et.al.,2008, Piersol and Paez, 2010, ISO 10816,1995, VDI 2056, ISO 2372 and BS 4675). These references will give the permissible values for various machine parameters like vibration limits, speed, velocity, displacement, acceleration, volume, temperature, etc. Standards are available for majority of these parameters like ISO 10816: 1-6 (ISO 10816, 1995) which specify allowable / permissible limits for various parameters applicable to different varieties of machines as per their classification. Using the background information, separate modules can be built using if-then-else statements linking machine parameters with allowable critical parameters and what action is to be initiated when these parameters are exceeded. Many of the times machinery manufacturers will mention the necessary action in their machine manuals or an expert’s advice is sought in solving the problem. This knowledge is stored (“Knowledge Acquisition” in KBS terminology) in the system for future reference in the master database or central server systems. Over a period of time, sufficient knowledge base can be built and all the modules are integrated to make one complete knowledge based system for machine health monitoring. One such model for knowledge based
3.1 Machine health monitoring: Condition monitoring of machines or plants is also referred as machine health monitoring since the machin are monitored for any abnormalities in their critical parameters. Any major deviation from permissible limits is addressed either by self-monitoring systems or by maintenance crew so that machinery problem is resolved before it can cause any major damage or catastrophic failure, besides making the machine available for production. 3.2 Knowledge based systems (KBS): The motivation behind any KBS is to capture expertise in a particular application domain, represented in a modular concept for easy transfer to and from the end-user. The general framework used in such systems is based on rule based expert systems. (Nadakatti, et.al, 2008). 3.3 Knowledge based systems for machine health monitoring: The maintenance knowledge base mainly consists of rules describing the relationship between machinery problems and corresponding symptoms. Some of the problems which have been identified over the years with most of the machinery are: unbalance, bent rotor, stator problems in motors, eccentric shaft, wornout bearings, oil-whirl, mechanical looseness, etc.
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Figure 2 KB Machine Health Monitoring System
Figure 3 Output from the KB Machine Health Monitoring System 3.4.3 Inference Engine: This module compares the field data or operational data of critical parameters of each machine with that of the permissible values as stored in the Knowledge Base. A signal is sent out to the control device which could be on / off switch, warning alarm, automatic shut-off system, flashing light or a mail to maintenance personnel, whenever the values either exceed or fall below the permissible values.
machine health monitoring is proposed in the Figure 1. 3.4 Knowledge based machine health monitoring model: A knowledge based machine health monitoring model is schematically developed and shown in Fig. No.1. It has five components: 3.4.1 Database (Maintenance database) 3.4.2 Knowledge Base (Maintenance KB) 3.4.3 Inference Engine 3.4.5 User Interface 3.4.5 Algorithm Each of these components is briefly explained below:
3.4.4 User Interface: This is the interface between the maintenance personnel or machine operator and Knowledge Based Machine Health Monitoring System. In this module data about operating parameters is either fed-in or received from the system. Also, the decisions by the knowledge-based systems about machine health, remedial actions taken / suggested, and either on-line or off-line basis are communicated to the user; a maintenance personnel or machine operator. Based on these inputs, a suitable action is initiated at appropriate time by the maintenance department or by automatic systems so that machine problem is attended before it gets developed into a major failure.
3.4.1 Database (Maintenance database): This is the back-end information centre which contains vast information about machinery parameters like list of all the machines or only critical machines across the plant, critical parameters for each of the enlisted machines like temperature, pressure, volume, speed, vibrations, noise levels, humidity, etc. The data can be either from machine manuals supplied by the manufacturers or from the handbooks like Shock and Vibration Handbook (Harris and Piersol, 1996), Handbook of Condition Monitoring (Rao, 1996), Maintenance Engineering Handbook (Higgins, et.al., 2008), etc. The data from these sources is fed to respective machine modules in the system for comparison from the actual operational data.
3.4.5 Algorithm: This is the module in which algorithms are written for comparing the actual / field values of critical parameters with that of stored data in the Knowledge Base. Most of the times the comparison is based on if-then-else statements with which maintenance decisions are arrived at depending on whether these parameters are either exceeding or falling below the threshold limits. In emergency cases, automatic signals are sent out for shutting off the system so that catastrophic failures are prevented. As and when new fault develops for which no prior knowledge base regarding solution exists, an expert’s opinion is sought to rectify the
3.4.2 Knowledge Base (Maintenance KB): This is the module in which operational data of machine’s critical parameters are received and stored for further analysis by the inference engine of the KBS. It also receives inputs from Database module about the permissible values for all the listed critical parameters for each machine.
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Figure 5 Output from KB Health Monitoring System
Figure 4 Condition Monitoring Module of Health Monitoring system same. This new knowledge regarding an unknown problem of a particular machine is stored in database for future reference. Over a period of time, the algorithms can be strengthened to develop a comprehensive Knowledge Based Machine Health Monitoring System.
assessed almost instantaneously. During the initial testing stage for the system’s effectiveness, the decisions made by the system are cross verified with the experts. Any major deviations from the expert’s opinion with the knowledge based system is observed and suitable modifications are made in the software. Once the system efficiency reaches to acceptable standards, the Knowledge Based Machine Health Monitoring System is integrated into the plant’s maintenance system. Another sub-module under Condition Monitoring of Machineries in the Knowledge Based Machine Health Monitoring system can predict the condition of turbines based on Bebee’s criterion (2003). The knowledge base for turbines has three components based on whether the turbine velocity is measured, for a frequency of 50 Hz or if the frequency is less than 10 Hz. Accordingly the measurements collected are RMS velocity (mm/s), peak-to-peak displacement (µm) and maximum displacement (µm). Consider a case where the RMS velocity is measured and entered in the system as 19 mm/s, the system flashes a message that, turbine is not running satisfactorily, as, its RMS velocity is greater than allowable limits of 11.2 mm/s. As can be seen in Fig.No.4 and 5, the allowable limits as per selected criterion are already stored in the form of series of if-then-else statements in the system. When a machine data is received by the system, the system compares those values with stored values (“Knowledge Base”) through “Inference Engine” and suggests the machine health. In advanced systems, even the reasons are given as to how such a decision is arrived at, like, in the present example, the system says turbine condition is “Unsatisfactory: Since allowable RMS Velocity > 11.2 mm/s”. Suitable remedial actions are initiated to rectify this problem and when the new values of RMS Velocity are fed to the system, the condition is re-assessed.
4. Model validation. The model developed based Knowledge Based Systems Model for Machine Health Monitoring has many modules based on various maintenance aspects like Preventive Maintenance, Condition Based Maintenance, etc., with each module having sub-modules containing information about detailed aspects of machine maintenance. The Condition Monitoring Module has vibration knowledge base. This consists of standard allowable vibration limits based on machine vibration standards (Rao, 1996, Higgins, et.al., 2008, ISO 10816 : 1995). Typical screen shots of a Condition Monitoring sub-module for the Knowledge Based Machine Health Monitoring. The screen outputs show one case where machine condition is assessed based on the directly measured velocity. As can be seen from the display screen-shot, when the user enters directly measured velocity as 1 mm/s, the system, not only gives machine classification, but also the machine condition whether machine is running smooth, is wellbalanced or not and is it aligned, etc. In the present case, for a velocity of 1 mm/s, the machine is classified as D category machine and is running “smooth, well-balance and well-aligned” These decisions are based on the standard inputs from vibration standards (ISO 10816,1995, VDI 2056, ISO 2372 and BS 4675). The field vibration data like displacement, velocity, acceleration are acquired from suitable vibration sensors placed at strategic locations on the machines. The data is fed to the system by the maintenance personnel and the machine health is
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5 Conclusions An application based on AI technique of Knowledge Based Systems, is developed for assessing the Machine Health Monitoring. Various modules and sub-modules were embedded in the application to suit individual maintenance needs of various types of machines. The permissible limits for vibrational parameters were added from the standards available [ISO 10816]. The results obtained by system were verified with permissible values from the vibration standards and were found to be acceptable. The system could be made more efficient by adding more and more data regarding machine vibrations, problems and their solutions so that whenever such problems occur in future, immediate solution can be retrieved from the system. There is enough scope for further enhancement of the system. The present system is developed for off-line conditions, in which, machine vibration data is fed to knowledge system by the user manually. On-line knowledge based system could be developed so that machines are monitored on continuous basis and remedial maintenance actions are initiated so that performance is enhanced, downtime decreased and overall performance is enhanced.
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