Computers Elect. Engng Vol. 19, No. 2, pp. 175-183, 1993 0045-7906/93 $6.00 + 0.00 Printed in Great Britain. All rights reserved Copyright © 1993 Pergamon Press Ltd

MODEL FOR THE DIAGNOSIS OF CIM EQUIPMENT

S. M. ALEXANDER, l C. M. VAIDYA 2 and J. H. GRAHAM 2

~Department of Industrial Engineering and 2Department of Engineering Mathematics and Computer Science, University of Louisville, Louisville, KY 40291, U.S.A.

(Received 10 November 1990; accepted in final revised form 2 August 1991)

Abstract--This paper presents a model for the diagnosis of CIM (computer integrated manufacturing) equipment. The model uses the deep knowledge of state transitions in the manufacturing process, for fault isolation and determination of the causal agent. This requires the use of valid sensory information. The model utilizes a sensor validation approach which incorporates diagnostic expectation and exclusion techniques. Furthermore a generic mechanism classification is used to reduce the effort in encoding test procedures for each subsystem. The model is developed in details using a workcell with a SCARA robot performing pick-and-place operations. The implementation of the model is done using Intellicorp's Knowledge Engineering Environment on a Symbolics 3640 computer.

Key words: Intelligent diagnostic systems, sensor validation schemes, diagnostic model, simulation.

I N T R O D U C T I O N

The rapid diagnosis of malfunctioning computer integrated manufacturing (CIM) systems is important because of the high cost of keeping these systems idle [1]. Intelligent diagnostic systems reduce the time for repair by guiding the user towards the goal of identifying the cause(s) of the malfunction. These systems capture the expertise of skilled troubleshooters and serve as source of up-to-date diagnostic knowledge. Hence, they reduce the number of trained maintenance personnel required in the factor. They also enable the diagnosis process to be consistent.

This paper develops and illustrates a model for diagnosis, and suggests future research directions. The next section discusses the strategies, presented in the literature, for diagnosis. Following this section, a model for diagnosis is presented. The model is illustrated through its application to a simulated CIM system. The simulation model is developed using Intellicorp's Knowledge Engin- eering Environment (KEE). The final section focuses on extensions of this research.

There are two dominant approaches for diagnosis presented in the literature. In the first approach heuristic knowledge of the system is used to match observed symptoms to faults. A symptom is defined as behavior which is unexpected or abnormal. Observed symptoms are mapped to faults directly by extrapolation. An advantage of this approach is that it is fast. This is because the knowledge is essentially compiled. Drawbacks of this approach include the possible generation of multiple hypotheses, which are context dependent. These can only be resolved by weak probabilistic methods and further measurements and tests. Also, because of the shallow knowledge used, this approach lacks comprehensive explanation capabilities. Since the symptom-fault matches are context dependent, they are not valid if there are any changes made to the system. Owing to the latter characteristic, symptom-fault matches cannot be extrapolated to other devices having the same components. Hansen [2] reveals another difficulty with this approach to diagnosis, i.e. the problem of dealing with multiple faults occurring simultaneously. The symptoms of multiple faults occurring simultaneously are not necessarily the union of the symptoms of single faults. In fact the symptoms may be totally different from that caused by each fault individually. If this fact is considered in the knowledge base there is a combinatorial explosion of the knowledge required and a corresponding increase in the search space.

The second approach to diagnosis is referred to as diagnosis from first principles [3]. Here no heuristic or experiential knowledge of faults are used. Instead the only information that is used is a knowledge of system structure and observations of system behavior. When the observed behavior of the system does not match the correct operating behavior, then diagnosis involves determining the system components that can explain the abnormal behavior. Reiter defines a diagnosis based on a minimum conflict set and the resulting hitting set. The conflict sets are

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176 s.M. ALEXANDER et al.

determined using a theorem prover [4], constraint propagation, set covering [6] or by using a numerical or qualitative simulation model of the system and running it under various fault assumptions [7]. The advantage of this approach to diagnosis is that it is sound and complete. However, it is less efficient than the first approach. It is also difficult to apply in the multicontextual and the time-varying situations of manufacturing. When using model-based diagnosis for multiple faults the nonmonotonicity issues must also be addressed.

Irrespective of the particular strategy used, diagnostic reasoning can be categorized as a sequential hypothesize-and-test cycle. In the context of diagnosis from first principles, diagnostic reasoning can be viewed as a process which assigns credit or blame to parts of the model based on observed behavioral discrepancies [8]. In the implementation of the above diagnostic reasoning strategies, the basic step that must be determined is the specific order of the hypothesis-test cycle, i.e. what hypothesis should be considered next, or what components should be assumed to be faulty. Peng and Reggia [9] suggest a probability based approach such that the most likely hypothesis combinations are considered first. The objective, of course, is to minimize the cost of hypothesis-test cycles used to make a diagnosis. Thus the specific test sequence selected would depend on the cost of the test, the reliability of the components, the physical structure and interconnections of the manufacturing system, maintenance heuristics, etc. We present a plan for establishing this focus, using deep knowledge, for the diagnosis of malfunctions in manufacturing systems in the next section.

A M O D E L FOR THE D I A G N O S I S OF CIM SYSTEMS

The correct operational behavior of manufacturing systems may be characterized by a series of state transitions of the system necessary for the manufacture of a product. These state transitions occur because of the proper functioning of causal agents responsible for the transitions. The state transitions are monitored using sensory information, from multiple sensors, which are sent to a controlling mechanism, such as a programmable controller. If a state transition is incomplete or in error, the tactical plan for diagnosis, suggested by the authors, is to use deep knowledge of state transitions and causal agents. When there is a faulty or incomplete transition the immediate focus is on the causal mechanism responsible for the transition. Coupling the diagnostic system design with the manufacturing system design, in this way, allows changes in the system design to be easily incorporated. It is possible, however, since causal effects are propagated through the system that the initial focus on the causal agent could be in error. This condition could result from erroneous or insufficient sensory information. The probability of the former is minimized by using sensor validation rules in a preprocessor. If the latter situation exists, then shallow knowledge can be used to relate sysmptoms to a set of subsystems, whose malfunction could have cause the observed symptoms. A ranking of these subsystems based on costs of tests and probabilities of failure provides an immediate focus for diagnosis.

The hypothesize-test cycle is repeated to disambiguate the diagnosis to the lowest level necessary for the specific situation. An acceptable level of detail for on-line diagnostic systems, is system replaceable modules. This level of detail is justified, since, replacing a module is more efficient than locating the specific component responsible for the fault.

We now present the details of a simulated implementation of this model for diagnosis. This simulation clarifies the precepts of the model for diagnosis and procedures used for sensor validation.

The model is applied to the diagnosis of a pick and place application of a SCARA (Selective Compliance Assembly Robot Arm) robot. The diagnostic system was developed on a Symbolics machine using Intellicorp's Knowledge Engineering Environment (KEE).

The pick and place operation, using the SCARA, can be described by the task diagram shown in Fig. 1. There are three phases of the robot cell task: the start-up phase, the operation phase and the shutdown phase. The start-up phase places the robot arm in the start state from any arbitrary location. Steps in this phase are identified by rectangular boxes in the diagram. In the operation phase the cyclic "pick and place" operation is performed in the cell. This phase is identified by circles. The shutdown phase resets the robot back to the home position after completion of the operation cycles. The task diagram represents the functional requirements of the system. The states

Model for diagnosis of CIM equipment 177

START UP PHRSE

i / ~ , . J HONE: I IHITIAL ~ / P O $ I T I O H POSITIOH " I POSITIOH WRIST \

/ .~ " - ~ " ~ \ I ~ I \ . / \ ~ NI START / .~".__ 'X~,,,,~ xSHUT OOWH XI,"~ LOCATIOH I

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Fig. 1. Robot arm tasks.

and transitions required for the operational phases are shown in Fig. 2. The 12 states are identified by the values of 10 binary sensors. When a state transition occurs, a subset of the sensor values change. This subset of variables are referred to as transition primary state variables (TPSV). The agent causing the transition is referred to as the transition primary mechanism (TPM). The state variables and states are shown in Tables 1 and 2, respectively. The transition primary mechanism (TPM) and the transition primary state variables (TPSV) for each of the 12 transitions are shown in Table 3.

The diagnostic system is divided into the following four major knowledge bases (KBs):

1. Operation knowledge base. 2. Assembly knowledge base. 3. Validation knowledge base. 4. Diagnosis knowledge base.

The Assembly and Operation KBs contain knowledge about the robot cell's physical structure and operation, respectively. The Validation KB contains rules for validation of the sensors using exclusion and diagnostic expectations. The Diagnosis KB contains methods for diagnosing suspect subassemblies. The following section describes the various KBs and system control.

The Operation KB contains knowledge about the robot cell operation. The operational information is represented in two units, STATES and TRANSITIONS. The unit STATES contains slots for values of the 10 state variables. This unit is instantiated into 12 operating states (S1-S12) and the current final state (CFS). The unit TRANSITIONS contains slots for expected end state, TPM and time. These slots are END, PRIMARY. MECHANISM and TIME, respectively. The unit TRANSITIONS are instantiated into 12 legal transitions of the cyclic operation and the current transition (CT). The CT has an additional slot TRANS which identifies the attempted transition. The Operation KB, and the slots of CFS and CT are shown in Fig. 3.

The physical structure of the SCARA assemblies are represented in Assembly KB. The SCARA assemblies are classified into three generic classes: positional, end effector and conveyor. The slots, in the generic classes, contain information about the components, component weighting factors (to

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order the tests) and the generic class TEST. METHOD. The weighting factors are proportional to the ratio of failure probability of the components to the cost of tests. The generic classes are instantiated into specific assemblies and their related information. The positional class is instantiated to the shoulder, elbow and the wrist flex assemblies. The end effector class is instantiated to the gripper assembly, and the conveyor class is instantiated to the conveyor assembly. The Assembly KB and the slots of the unit POSITIONAL- CLASS are shown in Fig. 4.

The Validation KB is the sensor validation preprocessor. It contains rules for validating the TPSV, and uses forward chaining to control rule application. The techniques primarily used for sensory validation are exclusion and diagnostic expectations. The validation rules make use of test results to confirm or reject sensor failures. The two methods of validation are illustrated in the following subsections.

Table 1. State variables--binary sensor

Assembly name Sensors

Shoulder ACLS, CLS Elbow ACLE, CLE Wrist flex UL, DL End effector G, BC Conveyor C Photosensor P

M o d e l f o r d i a g n o s i s o f C I M e q u i p m e n t

Table 2. W o r k ce l l - -ope ra t ion states

179

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State A C L S CLS A C L E C L E U L D L G C P BC

SI 0 I 1 0 I 0 0 0 0 0 $2 0 I 1 0 1 0 0 1 0 0 $3 0 I I 0 1 0 0 0 1 0 S4 1 0 1 0 1 0 0 0 I 0 $5 1 0 0 1 1 0 0 0 I 0 $6 1 0 0 1 0 I 0 0 I 0 $7 1 0 0 1 0 1 I 0 I 0 $8 1 0 0 I I 0 I 0 0 0 $9 0 1 0 1 1 0 I 0 0 0 SI0 0 1 1 0 1 0 1 0 0 0 SI I 0 1 1 0 0 1 1 0 0 0 SI2 0 1 1 0 0 1 0 0 0 1

Table 3. W o r k cell t r a n s i t i o n s - - T P M and TPSV

Trans i t ion T P M TPSV

T1 Conveyor C T2 Conveyor C, P T3 Shoulder ACLS, CLS T4 Elbow A C L E , C L E T5 Wrist flex UL, D L T6 Gr ippe r G T7 Wrist flex UL, DL, P T8 Shoulder ACLS, CLS T9 Elbow A C L E , C L E T I 0 Wris t flex UL, D L TI 1 Gr ippe r G, BC T I 2 Wris t flex UL, D L

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Fig . 3. O p e r a t i o n K B .

180 S, M . ALEXANDER et al.

Validation by exclusion

This technique rejects sensory information indicating structurally impossible states. Consider a sensor configuration as shown in Fig. 5. Now suppose the sensors UL and DL of the wrist flex assembly indicate 1 as the end of transition T5. This indicates a structurally impossible end state. The validation rules prompt the user to enter the current position of the wrist flex assembly. If the user response is END, then the system concludes that transition T5 was completed correctly, and that sensor UL has failed. The validation by exclusion scheme may seem as infeasible in a large system with a number of sensors owing to the risk of combinatorial explosion. However, since the sensor signals evaluated by this validation scheme are those that are monitoring specific transitions, combinatorial explosions are unlikely.

Validation by diagnostic expectations

This validation method reasons with diverse sensory information, indicating the same event. Consider transition T7, wherein the object is lifted by the wrist flex assembly. If at the end of T7, sensors UL, DL and P are all 0, then the system concludes that the object has been lifted (since P and DL are 0), and that sensor UL seems to be faulty. It should be noted that without validation the system would have suspected the wrist flex assembly.

The Diagnosis KB contains procedures for testing and fault isolation of the assembly components. The KB primarily consists of two classes of methods, GENERIC. DIAGNOSIS. PROCEDURES (GDP) and the individual COMPONENT. TEST. PROCEDURES (CTP). The GDP encodes diagnostic test procedures for a generic class of assemblies. The CTP is instantiated to test procedures for individual components that are used in the diagnostic level of detail. The

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GDP assembles all the CTP in a list, and sorts the list based on component weights. The tests are then conducted in sequence until the faulty component is isolated; if the tests fail to isolate the fault then the system indicates that the fault is unknown. The Diagnosis KB is shown in Fig 6.

The system control methods are distributed over the KBs to localize the control. The system is initiated by a unit in the Operation KB. This unit is an active method which displays the relevant TPSV, when the user enters the transition. Another unit, in the Operation KB, contains a method to set sensor deviation flags and initiate sensor validation. This unit is initiated by a method actuator. The Diagnosis KB, contains a method to initiate diagnosis. This method identifies and assembles the component tests, sorts the list of tests based on the component weights and then tests each component until a fault is isolated. The user interface contains windows for sensor validation

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182 S.M. ALEXANDER et al.

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Fig. 7. The diagnostic system.

messages, diagnostic advices and test procedures. The input panel consists of active images for transitions and TPSV, and method actuators to initiate sensor validation and diagnosis.

In summary, the architecture used for the implementation of the diagnostic model is shown in Fig 7. When the monitoring mechanism indicates a fault the diagnostic system is triggered. Since the same sensory information used for monitoring and control is used for initiating the diagnosis when a symptom is observed, there are no real-time implementation problems. It is assumed that the system ceases to operate when a fault is observed by the monitoring system; the intelligent diagnostic system assists with determining the cause of the malfunction and thus reduces system downtime.

The diagnostic system validates the sensory information, focuses on the causal mechanism of any incomplete transition and seeks a diagnosis. Focussing on the primary mechanism responsible for the faulty or incomplete transition provides a logical starting point for fault diagnosis. The identification of generic classes was found to be beneficial because it reduced the effort in developing individual assembly test procedures.

This diagnostic model was extensively tested in the laboratory with excellent results. A system based on this model is now being implemented in a computer-integrated manufacturing facility.

C O N C L U S I O N S

This paper has discussed the diagnosis of abnormal behavior in computer-integrated manufac- turing equipment. The model developed for diagnosis uses knowledge related to system operation, physical structure and maintenance expertise. The maintenance expertise represents maintenance heuristics and test procedures. The diagnostic model incorporates declarative and algorithmic processing. The sensor validation heuristics result in declarative processing and the component test procedures use algorithmic processing.

The sensor validation schemes were developed for binary sensors. These validations were based on exclusion and diagnostic expectations. In situations where both methods were used, exclusion was followed by diagnostic expectations.

The diagnostic system classified assemblies into generic mechanisms, based on the similarity of fault trees. This generic classification greatly reduced the effort in developing diagnostic procedures for the whole system.

It is clear to assume that components of manufacturing systems will not fail is unrealistic. It is important, therefore, to minimize the possibility of failure as much as technologically and economically feasible, and to develop intelligent diagnostic systems to reduce downtime because of failure. The diagnostic system proposed is for this purpose. It is the opinion of the authors that a diagnostic system, such as the one proposed, can be developed at the design stage. At the design

Model for diagnosis of CIM equipment 183

stage, when functional requirements are met by the planned behavior of designed physical structures, diagnosis of abnormal behavior must can also be planned.

R E F E R E N C E S

1. E. S. Eneyo, M. M. Barash and J. J. Talavage, A prototype expert system for diagnosing machine tools. Int. l iE Conf. Proc., pp. 439-444 (1988).

2. T. Hansen, Diagnosing multiple faults using knowledge about malfunctioning behavior. A C M 29-36 (1988). 3. R. Reiter, A theory of diagnosis from first principles. Artif. Intell. 32, 57-95 (1987). 4. M. R. Genesereth, The use of design descriptiors in automated diagnosis. Artif. Intell. 24, 411-436 (1984). 5. R. Davis, Diagnostic reasoning based on structure and behavior. Artif. lntell. 24, 347-410 (1984). 6. J. A. Reggia, D. S. Nau and Y. Wang, Diagnostic expert systems based on a set covering model. Int. J. Man Mach.

Stud. 19, 437-460 (1983). 7. B. Kuipers, Qualitative simulation. Artif. Intell. 29, 289 338 (1986). 8. J. de Kleer and B, C. Williams, Diagnosing multiple faults. Artif. lntell. 32, 333-339 (1987). 9. Y. Peng and J. A. Reggia, A probabilistic causal model for the diagnostic problem solving part II. IEEE Trans. Syst.

Man. Cybernet. 392-406.

A U T H O R S ' B I O G R A P H I E S

S. M. Alexander--Suraj M. Alexander is currently Professor of Industrial Engineering at the University of Louisville. Dr Alexander also served as a faculty member in the IE & OR department of Virginia Polytechnic Institute and State University and as an equipment design engineer at Corning Glass Works. Dr Alexander received a B.S. in ME, and an M.S. and Ph.D. in IE. & OR in 1970, 1972 and 1976, respectively. He is a registered professional engineer, a senior member of liE and SME, and a member of AAAI, TIMS and ASQC. He serves as the current director of the Quality Control and Reliability Engineering Division of liE. His research interests include manufacturing and process control, process and equipment diagnostics and manufacturing decision support.

C. M. Vaidya---Chandrashekhar M. Vaidya was born on 2 October 1959 in Pune, India. He obtained his secondary education at Kendriya Vidyalaya, Magpur, India, where he was graduated in 1976. In July 1977, he entered the University of Madra, India, and recieved the degree of Bachelor of Technology in chemical engineering in June 1982. In September 1982, he joined the Hindustan Petroleum Corporation at their lubricants refinery in Bombay, India, as a process engineer. In August, 1985, he entered the graduate program at the University of Louisville, and received his Master of Science degrees in chemical engineering and computer science in August of 1987 and 1989, respectively. The author is an elected member of Phi Kappa Phi and a member of ACM, AAAI and AIChE.

J. H. Graham--James H. Graham is currently a Professor of Engineering Mathematics and Computer Science at the University of Louisville. He has previously served as a faculty member at Rensselaer Polytechnic Institute, and as a product engineer with General Motors Corporation. He received his M.S. and his Ph.D. from Purdue University in 1978 and 1980, respectively. He is a senior member of the Institute of Electrical and Electronics Engineers, a member of the Association of Computing Machinery, a senior member of the Society of Manufacturing Engineers, a member of the American Association for Artificial Intelligence and a registered professional engineer. He has organized two IEEE Workshops on special computer architectures for robotics and automation, and is the editor of a book on this topic. He is also the editor of Safety, Reliability and Human Factors in Robotic Systems, published by Van Nostrand Reinhold in 1991. His research interests include robotics, artificial intelligence and distributed computing.