Knowledge-based systems for instrumentation diagnosis, system configuration and circuit and system design

EngngApplic. Artif. lntell. Vol. 6, No. 5, pp. 437-446, 1993 0952-1976193 $6.00+0.00 Printed in Great Britain. All rights reserved Copyright ~) 1993 Pergamon Press Ltd

Contributed Paper

Knowledge-Based Systems for Instrumentation Diagnosis, System Configuration and Circuit and System Design

J. G. R O W L A N D University of South Australia, The Levels

L. C. J A I N University of South Australia, The Levels

(Received August 1991; in revised form December 1992)

A number of knowledge-bused systems in the electronic engineering field have been developed in the past decade. These include those that use knowledge-based techniques to diagnose instrumentation, determine system configurations and to aid circuit and system design. This paper reviews the literature on knowledge-bused systems for electronic engineering applications in these areas and reports progress made in the development of a basis for realising electronic systems.

Keywords: Knowledge-based systems, design, diagnosis.

1. INTRODUCTION

The tremendous growth in computer-aided tools in the area of design, testing and diagnosis of electronic systems in the last two decades can be attributed to the availability of cheap, powerful and accessible computers and workstation technology, and to increased understanding of non-procedural programming paradigms and alternative forms of storing and manipulating information. The batch processing of ear- lier generations has given way to dedicated work- stations, interactive use and an interest among system designers in knowledge-based systems and applications of other artificial intelligence techniques and method- ologies. Hence the past decade has seen the wide application of expert systems in the electronic engineering area with typical applications including instrument diagnosis, system configuration and circuit and system design.

This paper surveys a selection of applications of knowledge-based systems for electronic engineering, primarily in the areas of diagnosis of system and circuit faults, system configuration and system and circuit design. It discusses current work in the light of the systems reviewed and reports progress made in the development of a system builder tool for realising electronic systems.

Correspondence should be sent to: Ms J. G. Rowland, School of Computer and Information Science, Knowledge-based Engineering Systems Group, University of South Australia, Levels Campus, The Levels, South Australia 5095.

2. A SURVEY OF KNOWLEDGE-BASED TOOLS IN ELECTRONIC ENGINEERING

This section reviews several knowledge-based systems in the areas of diagnosis of systems and circuits, system configuration and circuit and system design. In each of the ensuing discussions an attempt has been made to present, where available, various characteristics of the system including the name and purpose of each system, implementation details such as languages used, expert system shells used, the size of the system and the environment on which it operates, its genea- logy where appropriate, the knowledge representations used, the reasoning mechanisms used, the inputs to the system, the outputs from the system and future work planned.

2.1. Diagnosis of systems and circuits

ACE (Automated Cable Expertise) 1

ACE was developed at AT&T Bell Laboratories to identify and diagnose faulty sections of the telephone network. It is implemented in Franz Lisp and OPS4 and runs in a UNIX environment. The knowledge base stores production rules for the analysis of historical repair data stored in a remote database. Each evening, ACE contacts the database and maintains a conver- sation with it to extract required data for analysis. The analysis takes up to 4 h after which a text report is produced for examination by the user in the morning. Note that the system is not interactive, but operates

437 [AM IqS-C

438 J .G. ROWLAND and L. C. JAIN: KNOWLEDGE-BASED SYSTEMS

independently of the user, analysing first high-level data to identify possible problem areas and then successively more detailed data as required for the trouble analysis.

The first prototype development began in 1981 as an assistant to cable analysts, and was completed in 1984. A commercial product was released in 1986.

Compass (Central Office Maintenance Printout Analysis and Suggestion Scheme) 2

This system was developed at GTE Computer Science Laboratories to aid the maintenance of telephone switching systems. Telephone switches are complex electronic systems connecting tens of thousands of telephone lines to each other and to external trunks. The system analyses historical records of telephone company central office switching equipment and sug- gests prioritised maintenance actions. It is necessary in such a domain to analyse temporal information as problem patterns appear only by studying a series of messages over a time period.

Compass is implemented using KEE TM from IntelliCorp and InterLisp D on a Xerox Lisp Machine. KEE TM provides several paradigms well-suited to AI applications and Compass utilises forward-chaining rules, frames with demons, object-oriented programming, message passing, inheritance and programming in Lisp to store and manipulate a large amount of diverse knowledge. The knowledge in Compass is divided into 8 separate knowledge bases; one contains structural information of the switches and network (stored in hierarchical frames), another manages software tools to aid the operation of Compass, and the others are directly related to the six processing stages of the system (using frames with inheritance and rules). The processing stages utilise a forward-chaining reasoning paradigm and are described below:

• identification of groups of messages with commonality, analysis of groups of messages together with related information to determine the existence of faults,

• estimation of the likelihood of occurrence of the possible faults,

• determination of possible maintenance actions, • prioritorisation of this list of possible actions,

and • presentation of the resulting suggestions to the

user.

The first version of the system was released for field testing in 1985 while development work continued in parallel.

Disk-Drioe Diagnostician 3 Disk-Drive Diagnostician is a frame-based,

backward-chaining knowledge-based system to aid the diagnostic process in the manufacture of computer high-end 3380-J/K disk drives. During testing to ensure

the machine is operating correctly, the system generates data to be used for diagnostic purposes if a problem is detected. The analysis of this data to determine the failing component is usually performed manually.

Disk-Drive Diagnostician utilises both frames with inheritance to store the knowledge base, and production rules which are incorporated within the frames. The knowledge base is two-tiered; the first level comprises a root frame and the second a collection of sub- frames. The root-frame determines which test pro- cedure has detected a fault and which machine circuits will be analysed, and invokes the correct sub-frame. Sub-frames perform detailed analysis of the problem. Each sub-frame has a specific scope depending on the circuits under test. Backward chaining is used for deduction as the system has to begin with a given symptom and work to identify the cause.

The system runs using the Expert System Environment (ESE), running under the IBM VM operating system. The first release of Disk-Drive Diagnostician contained 15 frames with 500 rules. The latest version has 25 frames and over 600 rules. Work is continuing on realtime testing data acquisition to configure an integrated environment for computer-assisted testing and diagnosis.

BDS Diagnostic System (Baseband Distribution System) 4

BDS Diagnostic System is a knowledge-based trouble-shooter for BDS, a large signal-switching network which accepts up to 40 baseband input signals and connects them to any of 304 baseband signal output ports.-The system performs corrective maintenance by rapidly isolating the faulty printed circuit board or other part of a chassis-mounted piece that caused the failure.

BDS Diagnostic System was implemented using the LES expert system framework developed at Lockheed Palo Alto Research Laboratory and in many ways is similar to EMYCIN. However, LES's power and flexi- bility result from the ability to store a structural description of the device to be diagnosed, as well as the ability to control the reasoning process. It represents the structure in a frame-based knowledge base. The components of the system are categorised e.g. printed circuit boards, cables, frequency analysers and spectrum analysers. Attributes stored in the frames must be selected carefully to allow LES to use its internal model of signal flow in one direction through a set of nodes to aid its fault diagnosis. If-Then and When-Then rules, expressed in an innovative case-grammar format, determine the processing control. Using the knowledge that a signal flows through devices in one direction, the device knowledge in the knowledge base and a backward chaining reasoning mechanism, LES is able to identify the set of components along which the signal passes and find appropriate points to test for the pres- ence of a signal.

In addition, the system contains user information,

J. G. ROWLAND and L. C. JAIN: KNOWLEDGE-BASED SYSTEMS 439

primarily whether the user is a novice or an expert, to determine the detail of information displayed to the user during a consultative session. The system was installed on a DEC VAX 11/750 in November 1983, and has undergone evaluation and further development.

ESHDD (Expert System for Failure Analysis and Disposition of Hard Disk Drives) 5

ESHDD, developed at the Nanyang Technological Institute in Singapore, analyses hard disk drives for PCs and, similar to Disk-Drive Diagnostician, determines whether the unit is to be repaired or discarded. A working prototype was implemented on an IBM PC for production testing in 1990. It uses PC Scheme (a Lisp dialect) and the PC Plus expert system to implement the expert system environment, dBASE III + as storage for external knowledge and for data management, Dr Halo to support graphics and Turbo C to link program modules. PC Plus uses a combination of forward and backward chaining reasoning mechanisms.

ESHDD's knowedge base uses frame-based storage consisting of a root frame and 6 sub-frames. The root frame identifies the disk drive, identifies the station which has failed (recalibration, DC erased, analog or burn in station), writes the results to a text file and displays the graphical disposition of the disk drive, and then allows the user to decide whether to update the database. The sub-frames allow the user to choose whether to run a consultation, implement the four stations detailed above, or to check the disposition recommended by the station sub-frames. ESHDD is capable of providing consultation for 49 symptoms of failure from the four test stations, including 29 debugging procedures which utilise historical data.

FLES (FLight Expert System) 6

A prototype knowledge-based flight expert system was developed in the mid 1980s to assist airplane pilots monitor, analyse and diagnose faults and to provide support in reducing the mistakes of pilots. It was implemented on a VAX computer using Franz Lisp and a frame-based knowledge representation. The system implements two sub-systems utilising modular decomposition in system design. The Flight Monitor System monitors pitch, yaw, roll and other attributes requiring regular attention and adjustment; a different sub-system is used for monitoring each phase of flight (taxi, takeoff, etc.). The Flight Interrupt Controller monitors interrupts generated in four categories (electrical system, hydraulic system, engine system and fire system) when values occur outside of the normal operating range. Hypotheses are constructed regarding the nature of the fault in the components in question and if there exists enough supporting evidence, it is passed to a diagnostic routine which determines if the source is the component or a side effect of another fault. A blackboard-like data structure is to present hypotheti-

cal information for the fault-verification process. Two frame types are used for knowledge represen-

tation: sensor frames contain information regarding sensors used to monitor the electrical system and fault frames are composed of information about the electrical system components. Future work will incorporate the use of temporal information.

HITEST 7

Hitest is not a diagnosis system per se, but a system which generates tests for combinational and sequential digital circuits. It utilises both algorithmic and heuristic knowledge which represent component knowledge specific to the component and independent of how that component is used in a circuit, and circuit knowledge representing its behaviour and the circuit's intended use. Once more, a hierarchical frame structure is used for knowledge representation.

Hitest has four major aspects:

• a circuit definition language to describe the circuit, and includes a library of previously com- piled sub-circuits for use by the designer;

• a waveform definition language to specify the stimuli applied to the circuit and the expected responses;

• the simulator to provide fast and accurate fault simulation using a method unique to Hitest--known as the Parallel Value List method--which combines parallel processing, concurrent processing and deduction;

• the display sub-system to display results to the user.

Hitest has been used to generate test waveforms for circuits of up to 3000 simulator primitive elements. Future versions of the system include testability analysis to verify the knowledge provide by the designer.

MICON 8

MICON was developed at CMU to synthesise the design of a small single-board computer system from abstract description of the board's functions and design constraints. It uses a modular, bottom-up approach, designing sub-systems initially and then forming a full system from the results. The system consists of an integrated set of programs which prove the design and build and test components. The M1 sub-system produces a logical design from a high-level set of user specifications including required system functions, performance, and resource and reliability constraints. It also produces a list of components required to realise the design, a list of the interconnections of these components, an address map defining where memory and I/O devices are, and a set of graphs detailing the cost of reliability and fault-coverage enhancement techniques investigated during the design. P-CAD takes the design and places and routes a wire-wrap or printed circuit

440 J.G. ROWLAND and L. C. JAIN: KNOWLEDGE-BASED SYSTEMS

board. After it is wired and filled with components it is exercised by a set of custom test programs applied via a custom test jig.

The knowledge base comprises several types of knowledge, representing the different design tasks (specification, selection, part expansion, structure design, evaluation, design constraints and variables and problem solving). Each type resides in knowledge bases which are independent of and separate from the others. The knowledge bases are primarily rule-based, and the operation of the system can be automatic or semi- automatic. The latter mode of operation allows the user to override system decisions as long as the design constraints are not violated.

2.2. System configuration

PIAF (Package for Intelligent and Algorithmic Floor planning) 9

PIAF, from the Systems Engineering and Design Automation Laboratory of the University of Sydney, deals with Very Large Scale Integration circuit placement. It employs an algorithmic strategy combined with a knowledge base to automate the placement and to avoid a combinatorial explosion in the solution space thus minimising searching. Constraint relaxation is also used to successively refine the generated solution space. Five phases are involved:

• communications solving phase: determination of routing interconnections without violation of electrical rules;

• positioning phase: the family of rectangular topologies is generated using a rectangular dua- lisation algorithm;

• feasibility investigation: the feasibility and the efficiency of the representations are investigated;

• block sizing: the minimal sizes of blocks are selected for the selected solution;

• dimensional refinement: considers architectural constraints, the area needed by the wiring blocks and the area needed for interconnection signals between blocks with unusual surrounds.

Multiple knowledge representation schemes were cho- sen to represent diverse knowledge--frames, If-Then rules and procedural and declarative formalisms. Three knowledge bases store domain-specific knowledge (such as objects and relationships between objects, their attributes and properties), common-sense knowledge (e.g. geometric manipulation rules to rotate and mirror rectangles), and system knowledge (including attribute access functions and utilities needed in software development). Contexts are used to represent the system state at any given time. PIAF uses a Prolog-like backtracking mechanism to generate alternative solutions when constraints are violated; however the Prolog

concept of undoing rules was not viable and a dynamic frame system was selected to implement context switching when backtracking.

PSA (Parts Selection Advisor) l°

PSA was developed at Hewlett-Packard at Ft Collins, Colorado. It is a prototype expert system used for component selection in the manufacturing environment, allowing the engineer to produce a more econo- mical product because the system selects parts which fulfil the required functional characteristics, fit the manufacturing process, are supplied by a preferred supplier and maximise component commonality. The user can request justification for a part selection.

The knowledge base maintains five knowledge types: preferred supplier index based on technical, quality, reliability, delivery and business criteria, manufacturing process fit, manufacturing setup cost, purchasing department cost and component commonality.

The system is implemented using C-based routines to access the INFORMIX(R) relational database where knowledge is stored, the Hewlett-Packard AI Development System (which provides a Lisp development environment), the Hewlett-Packard Prolog system and a small Prolog toolset to aid expert-system construction.

WES (WirabiIity Expert System) II

WES is a decision inferencing system which supports VLSI chip designers with reference to the wiring space requirements of a system, deciding the appropriate number of functions that can be placed on the chip by keeping track of unused space. It was developed at Sperry Corporation in Blue Bell and is implemented using KEE TM on a Symbolics 3670 Lisp machine. A frame-based knowledge representation, a semantic network and object-oriented programming provide the knowledge base storage and manipulation framework. The inferencing engine is coupled with a deduction mechanism and provides both forward and backward reasoning processes. Rules are organised into classes and sub-classes, allowing selective application of a class of rules to make reasoning easier and easing the management of large and complex rule bases. The system's knowledge is contained in five knowledge bases: knowledge of devices, of semiconductor technology, of CAD technology, of fabrication capabilities and of chip attributes such as dimension and aspect ratio between horizontal and vertical tracks.

WES responds to a query with the wirability and wiring quality:

• not wirable: too many devices on a chip and too much manual wiring expected;

• marginally wirable: a 10% chance manual wiring is needed after automatic placement and wiring

• wirable: 90% confidence that a chip is automatically wirable;


• very wirable: the chip is slightly underpopulated and is fully wirable by automatic means;

• wastefully wirable: the chip is too big for the designated number of devices.

XCON 12

XCON is a well known knowledge-based system for configuring Digital Equipment Corporation's VAX 11/ 780 computer systems. XCON was developed from the R1 system and determines from customer specifications the configuration of a required computer system. Because XCON is so well known, it suffices to indicate that it was implemented in OPS5 (also developed by Digital) as a rule-based production system, that the first prototype in 1982 had 250 rules, but in early 1985 this had expanded to 4200 rules, and the performance claimed by Digital was 2.3 min to configure an order.

2.3. System and circuit design

BLADES (AT&T Bell Laboratories Analog Design Expert System) 13

BLADES is a comprehensive knowledge-based analog circuit design environment prototype that uses different abstraction levels depending on the complexity of the design task under consideration. The system incorporates both formal and heuristic knowledge, and utilises a divide-and-conquer paradigm as a reasoning strategy. The developers believe that this mimics human reasoning where the required system is iteratively decomposed into sub-systems and then the system is realised by combining the primitive blocks corresponding to the terminal sub-systems. This approach serves to limit the size and complexity of the knowledge base and the rule base.

The rule base contains If-Then type production rules and the Lisp-based OPS5 production system is used to implement the rule base. The system maintains the modular and hierarchical architecture used in the circuit design in its own design, and comprises 5 components:

• the expert system manager which checks the consistency of the user's input data and deduces the questions it needs to ask, creates a circuit topology and reports the result to the user;

• a subcircuit design expert to maintain a library of frequently used and fully parameterised subcircuit structures for various I/O requirements;

• a subcircuit knowledge base which maintains procedures and functions for formal knowledge and frames for components and other objects;

• a test generation facility, responsible for testing the correctness of the circuit;

• the circuit design consultants provide circuit simulation as a final step in the design process

and insure the correctness of the BLADES design for the user.

LDA (Logic Designer's Apprentice) '4

LDA is a knowledge-based tool for designing digital circuits and aiding the testing process. It is a prototype, implemented using OPS5 on a VAX environment initially and then on a Symbolics 3670. LDA is one of the first tools to attempt to include maintainability features in the design of the circuit by assuring adher- ence to proper design practices.

LDA does not employ deep knowledge strategies, beginning at a high level of abstraction and working down to an implementation. Component knowledge is limited to top-level function attributes, and the system cannot reason about the internal architecture of components. Similar to other systems, the user is prompted for parametric information, and LDA responds with a description of the characteristics of the components it is seeking. Appropriate selections are made by the system from a component library: where two or more components suit the stated requirements the system, presents its selection rationale to the user for confirmation. Upon completing selection the system interconnects the components, including test points, and presents its reasoning to the user as it proceeds.

Further enhancements planned for the system include a broader library of circuit elements, the generation of test programs and the incorporation of cost, manufacturing and reliability considerations into the design process.

NECTAR '5

NECTAR was designed by the University of California to overcome convergence and user-interface problems with SPICE 2, incorporating knowledge gleaned from both novice and expert SPICE users and system designers. The inference engine, similar to the OPS family, works in three stages: determination of the relevant rules (If-Then type rules) for the SPICE data available, determination of the execution order of these rules, and execution of the rules.

A variation of the Rete match algorithm is employed which provides fast and efficient pattern matching. Hence when NECTAR infers reasons for non- convergence its goal is to recognise patterns that lead to the problems and then suggest alternative strategies to overcome the problem or automatically correct the input file and re-run SPICE. NECTAR also provides a circuit design aid by allowing the user to activate a class of rules involving circuit design constraints. Thus novice designers particularly are assisted.

NECTAR is a rule-based system implemented as a production system in Common Lisp (VAXLisp) on VAX machines such as VAXSTATIONS under the ULTRIX operating system. It contains 2500 lines of Lisp and 2000 lines of C for the user interface.

442 J .G . ROWLAND and L. C. JAIN: KNOWLEDGE-BASED SYSTEMS

PEARL (Power-supply Expert Assisted Rule-based Layout) 16

Pearl was developed by the CAD Systems Engineering Group at Digital Equipment Corporation to provide intelligent assistance to printed wiring board layout designers and has been enhanced to provide etch routing assistance as well. Unlike digital circuit layout where interconnections are simple and uniformly spaced, power-supply layouts contain discrete components that are not regularly shaped and thus component placement is difficult. It was first released in 1985 and extension of its knowledge base and routing facilities progressed in parallel.

PEARL is implemented in OPS5 and BLISS. The system is structured in such a way that a top layer comprises OPS5 rules for control (user interface, placement control strategies and an explanation facility) and a second layer provides efficient algorithms and primi- tives for placement implementation stored as BLISS routines. Power supply constraints are handled by heuristics and are contained in the top layer.

OASYS 17

OASYS is a hierarchically-structured analogue circuit synthesis tool prototype developed at CMU. The first version was implemented as a rule-based system with 100 rules. A later version comprises 7500 lines of Franz Lisp code running under UNIX on a VAX 11/ 785 machine and using planning mechanisms with rule execution. The system generates alternative circuit topologies based on user-specified performance specifications, selects a topology which is still at a high abstraction level, and finally translates this abstraction into a specification and performs device sizing.

Several knowledge representations are used. Design knowledge is stored as static plans with design style templates as default plans (rather than construct plans dynamically which is the usual style of operation of planning systems). Plans are implemented as rules together with an ordering mechanism to reduce the non-determinism of rule-based systems, and are exe- cuted iteratively when required. Plans incorporate heuristic knowledge, formal knowledge expressed as computational algorithms and equations, and refinement knowledge to select and translate sub- components of a design plan. Selection knowledge uses structural discrimination and generate-and-test meth- ods to select a circuit block style, together with a small amount of heuristic knowledge, all of which are gener- ally stored as constraints. Plan-Fixers, or strategies for coping with a plan that does not meet the design goals, are coded as If-Then rules.

This section has reviewed several of the large number of knowledge-based tools in the electronic engineering area. Further references which cover additional such

systems reported in the literature are provided in the bibliography.

3. DISCUSSION OF CURRENT KNOWLEDGE-BASED TOOLS

There are various advantages in using artificial intelligence in system design and diagnosis [29]. Knowledge derived from the expertise and experience of the most competent designers can be made available to all those who wish to create or to test a specific circuit. In addition, it is possible to handle uncertainty in the information acquired, and to provide guidance when a decision has to be made between several different but feasible solutions. Explanation facilities can be incorporated, including the ability to answer how and why style questions from the user.

However, most of the tools discussed can be used only by a person with some knowledge of electronics. These tools are of little help to non-electronic engineers or to application-domain people who wish to build their own circuits. The current tools also lack an interface to the application domain knowledge required in electronic engineering applications, and indeed in applications in other areas.

The systems do, however, exhibit several architectural similarities. For example, frames are commonly used as the data structure in the knowledge base. Frames are widely available in expert system shells and in Lisp, and can be simulated in Prolog. Their per- ceived advantage lies in the fact that they allow an object-oriented style of implementation incorporating attribute hiding, inheritance and the ability to store processing constraints and other rules within the frame. Particular knowledge-based systems retain a single knowledge-representation paradigm, citing simplicity of implementation and processing as the rationale, while others select multiple paradigms, believing that the additional power achieved with different representations for diverse knowledge types outweighs the additional complexity of the knowledge base. Similarly, several systems divide the knowledge base into a collection of smaller sub-bases to simplify the processing and to improve performance. The division can be achieved along functionality lines with each knowledge base independent of the others, or using an hierarchical division where processing selects the appropriate knowledge base path to pursue. Systems that utilise a multi- paradigm knowledge-representation approach are more likely to incorporate multiple (sub) knowledge bases.

Increasingly the developers of knowledge-based electronic systems are borrowing from the computer science-based discipline of software engineering, employing strict processes in the development of the knowledge-based systems. This is exemplified by the modular and hierarchical structure of several of the


surveyed systems and the definite phases of processing in achieving the system's goal.

Continuing with the software engineering analogy, software designers have long embraced the notion of designing software for maintenance. It has become evident that the design of circuits and electronic systems should consider the ultimate diagnostic tests to be performed. None of the circuit and system design tools surveyed or (to the knowledge of the authors) reported in the literature incorporates a knowledge- based diagnostic capability in the design phase. At present, nearly 80% of equipment maintenance time is spent on fault diagnosis and repair; this facility would reduce maintenance time considerably. Thus, the incorporation of intelligent design with diagnostic components will result in more robust circuits and systems, significant financial benefits and will lead eventually to reduced lead time in product development. This area of engineering remains in a naive stage with most systems realised as prototypes, and work continues in this direction internationally.

It is clear that there is a need for fully integrated and intelligent tools which incorporate diagnostic knowledge and select and interconnect required signal condi- tioning blocks without significant user intervention. There would be an advantage in having a framework on which to easily implement and trial extensions and new facilities. For example it would be useful to have the user requirements translated into system specifications which could be subsequently used to select the appropriate circuit building blocks. Such a testbed could trial and benchmark various approaches to this problem, enabling an appropriate implementation decision to be made for incorporation into a final system.

A knowledge-based electronic system builder prototype testbed is under development at the University of South Australia which designs systems from operational requirements supplied by the user. The system generates a full system design for a moderately complex electronic system. The work is new in the sense that it presents a framework or testbed on which further facilities can be implemented and trialed. The next section describes the system, presenting the framework which operates with minimal user intervention.

4. A KNOWLEDGE-BASED ELECTRONIC SYSTEM BUILDER

A knowledge-based electronic system builder tool prototype which will act as a testbed is under development at the University of South Australia. It incorporates many aspects described in the previous section and designs systems from operational requirements supplied by the user. The user receives a system design for a moderately complex electronic system. It is not necessary to have a professional engineer use the system builder tool; whether the system designer is trained or not, the method of use is identical. The

system selects appropriate electronic circuit building blocks and provides block diagram schemata; the dimensioned building blocks are interconnected in the form of a circuit diagram.

The tool framework divides the design of the requested system into two separate phases:

• the calculation of the component values based on well-known design algorithms;

• the selection of the required components and component configuration.

The latter aspect is most important as it relates to the actual implementation of the circuit in practice. However, implementation in procedural languages (e.g. Pascal, C, Fortran) becomes difficult and complex. Although procedural languages are well-suited for numerical operations and well-defined algorithms, this aspect involves heuristics for the selection of the appropriate configuration. Thus it is intrinsically a sym- bolic processing operation which can be handled with ease in an environment such as Prolog which has a capability to represent the rules in a compact and simple manner. In addition its built-in back-tracking and unification mechanisms make it extremely flexible for data and knowledge manipulation. Indeed, the back-tracking mechanism provides an additional advantage over Lisp and Lisp dialects which also can represent knowledge simply and compactly and in an abstract form, are flexible for data manipulation and are also widely used for applications requiring artificial intelligence techniques. Hence Prolog has been selected for the prototype implementation. The prototype is implemented on an IBM PC.

Various design phases are incorporated into the prototype tool. Consider the selection of a suitable transducer for temperature measurement. Four transducers are used in the study, as illustrated in Table 1. The user is requested for the following information:

• Lower Limit of the desired temperature range (oc)

• Upper Limit of the desired temperature range (oc)

• Accuracy (%) • Vibration and Shock (mm) • Linearity (%) • Highest frequency of interest (Hz) • Resolution (Low, Medium, High) • Repeatability (Poor, Medium, Good) • Hysteresis (Low, Medium, High) • Time Response (Slow, Medium, Fast) • Sensitivity (Low, Medium, High) • Cost estimate (Low, Medium, High) • Physical size (Small, Medium, Large)

and the transducer selection phase selects a suitable transducer matching the constraints specified by the user. Table 2 shows the partial knowledge base for selecting the Resistance Temperature Detector

444 J. G. ROWLAND and L. C. JAIN: KNOWLEDGE-BASED SYSTEMS

Table 1. Temperature transducers

Parameters

Resistance temperature Semiconductor

detectors Thermocouple Thermistor sensor

Temperature range °C - 263-850 - 253-4000 - 60-300 - 55-150 Resolution Low or medium Not high Medium High Accuracy (%) />0.1 i>0.2 />0.01% >10.2 Repeatability Poor or medium Good Not good Good Hysteresis Medium or high Not low Not low Not low Time response Not fast Not fast Not considered Not fast Cost Not low Not low Low Not low Sensitivity Low Not high Medium Not high Vibration and shock <2 mm <2 mm <2 mm <2 mm Linearity (%) 0.4 /> l />5 I> 1 Physical size Not small Not small Small Small Highest frequency ~< 5 ~< 5 ~< 100 ~< 100 of interest in Hz

and Thermocouple tempera ture transducers in the prototype.

The unique aspect of the system lies in its design which facilitates the incorporation and trialling of new or extended facilities. It is an integrated system in the software engineering sense, implying that it has been developed in a modular fashion that allows various existing modules such as the knowledge-base or processing rules to be easily replaced or extended, and new modules such as a new and independent processing model to be incorporated. For example, circuit and

Table 2. Partial knowledge base for selecting temperature transducers

transducer_is (Temperature, Accuracy, Vibration, Linearity, Resolution, Repeatability, Hysteresis, TimeResponse, Sensitivity, Cost, Physical_Size, Highest_frequency

component, rtd):-

range(Temperature, - 263, 850) AND OR_(Resolution, low, medium) AND Accuracy 1>0.1 AND OR_(Repeatability, poor, medium) AND OR_(Hysteresis, medium, high) AND NOT(TimeResponse = fast) AND NOT(Cost = low) AND Sensitivity = low AND Vibration <2 AND Linearity/>0.4 AND NOT(Physical_Size = small) AND Highest_frequency_component ~< 5.

transducer_is (Temperature, Accuracy, Vibration, Linearity, Resolution, Repeatability, Hysteresis, TimeResponse, Sensitivity, Cost, Physical_Size, Highest_frequency

component, thermocouple):-

range(Temperature, - 253, 4000) AND NOT (Resolution = high) AND Accuracy ~> 0.2 AND Repeatability = good AND NOT(Hysteresis = LOW) AND NOT(TimeResponse = fast) AND NOT(Cost = low) AND NOT(Sensitivity = HIGH) AND Vibration <2 AND Linearity ~> 1 AND NOT(Physical_Size -- small) AND Highest_frequency_component ~< 5.

system diagnosis plays an important part in modern electronic and electrical system design. Research at the University of South Australia indicates the merits of integrating design with diagnosis and the approach means the user need only know which circuit is to be implemented. It has been already tested for simple combinational digital circuits with the aim of trialling the integration of diagnostic tests with the design of sequential and analogue circuits.

5. FUTURE W O R K

It is planned to incorporate pressure, displacement and flow measurement in the scheme as described below:

• It is planned to incorporate the following pressure transducers: capacitive sensors, differential transformers, piezoelectric transducers, potenti- ometers , unbonded strain gauges, bonded foil strain gauges, thin film strain gauges, diffused semiconductors and variable reluctance transducers.

• It is planned to introduce the following flow transducer types: orifice, venturi, flow nozzle, pilot tube, rotameter , electromagnetic, turbine meters, ultrasonic, vortex and anemometers . The parameters to be used to select the transducers are flow in Cu m/s, rangeability, linearity, repeatability, accuracy, pressure loss, immunity to viscosity effects, cost, physical size and response speed.

• It is planned to introduce the following displacement transducers: bonded strain gauge, induc- tive reluctance transducer, LVDT, semiconductor transducer, and wire wound potent iometer . The parameters to be used to select the transducers are displacement in m m linearity, highest frequency of interest, operating tempera ture range, humidity range, accuracy, resolution, hysteresis, long life and robustness and shock and vibration.

The second development phase will involve the design


of necessa ry s ignal cond i t ion ing b locks which will be ca r r i ed ou t by A J I T A . ls A J I T A p rov ides a l ib ra ry of p r o t o t y p e signal cond i t ion ing b locks tha t a re d imen- s ioned by user in t e rac t ion for each app l ica t ion . T h e a im is to r ep l ace this use r i n t e rac t ion wi th the knowledge - b a s e d rules . O n c e a su i tab le t r ansduce r tha t ma tches the speci f ica t ions o f the use r is se lec ted , the necessa ry s ignal cond i t ion ing b locks will au toma t i ca l ly be p i cked up and i n t e r c o n n e c t e d using circui t connec t iv i ty rules for d i sp lay on the sc reen .

• C o n c u r r e n t l y , as ind ica ted , the a im is to inc rease the a m o u n t o f sys tem d iagnos t ic infor- m a t i o n and tests i n t e g r a t e d in to the des ign o f sequen t i a l and a n a l o g u e circuits . Some p rogress has b e e n m a d e t owards this and a mul t i - l ayer c lass i f icat ion s cheme for circuits has been dev ised .

• W o r k is in p rogress to t r ia l the i n c o r p o r a t i o n of p r e f e r r e d va lues of c i rcui t c o m p o n e n t s , calcu- la t ion o f a p p r o x i m a t e cost o f the c o m p o n e n t s , e s t ima t ion of re l iab i l i ty , and p rov i s ion of r ap id ly access ib le supp l i e r and o rde r ing infor- ma t ion .

flight expert system. Proc. Second Conf. Artificial Intelligence Applications, pp. 49-54 (1985).

7. Bending M. J. Hitest: a knowledge-based test generation system. IEEE Design Test Comput. 1, 83-92 (1984).

8. Birmingham W. P. and Siewiorek D. P. MICON: a knowledge- based single board computer designer. 21st Design Automation Conf., pp. 565-571 (1984).

9. Jabri M. A. Knowledge-based system design using Prolog: the PIAF experience. Knowledge-Based Syst. 3, 72-79 (1989).

10. Praizler D. and Fritz G. A parts selection expert system to increase manufacturability. 24th A CM/IEEE Design Automation Conf., pp. 706-712 (1987).

11. Soong N. and Patel A. Wirability expert system. Comput. Aided Design 18, 497-501 (1986).

12. McDermott J. R1: a rule-based confignrer of computer systems. Artif. lntel. 19, 39-88 (1982).

13. EI-Turky F. and Perry E.E. BLADES: an artificial intelligence approach to analog circuit design. 1EEE Trans. Comput.-Aided Design 8, 681)-692 (1989).

14. Moskowitz L. The LDA--an integrated diagnostic tool. IEEE AES Mag. 1, 22-26 (1986).

15. Kelessogiou, T. M. and Pederson P. O. NECTAR: a knowledge- based environment to enhance SPICE. IEEE J. SSC 24, 452-457 (1989).

16. Dejesus E. J. and Callan J. P. PEARL: a knowledge-based expert assisted CAD tool. Proc. Second Conf. Artificial Intelligence Applications, pp. 258-263 (1985).

17. Carley L. R. and Rutenbar R. A., How to automate analogue IC designs. IEEE Spectrum 2,5, 26-30 (1988).

18. Jain L. C. and Bowden B. S. A framework for designing electronic circuits and systems. Measurement, J. Int. Measurement Confed. In press.

6. C O N C L U S I O N

K n o w l e d g e - b a s e d sys tems have been ve ry successful in solving eng inee r ing p r o b l e m s w h e r e t r ad i t i ona l p a r a - d igms and a p p r o a c h e s p r o v e d complex and unwie ldy . F a u l t d iagnos i s , sys tem conf igura t iuon and circui t and sys tem des ign a re a reas w h e r e k n o w l e d g e - b a s e d sys tems have m a d e a cons ide r ab l e con t r ibu t ion . This p a p e r has br ief ly r e v i e w e d the l i t e ra tu re on knowledge - b a s e d sys tems for e l ec t ron ic eng inee r ing appl ica t ions . T h e charac te r i s t i cs o f such sys tems and some des i rab le charac te r i s t i cs o f e l ec t ron ic eng inee r ing app l i ca t ions in gene ra l were d iscussed. T h e p rog res s m a d e in the d e v e l o p m e n t of a t e s t b e d for t r ia l l ing e lec t ron ic sys tems des igns was also r e p o r t e d .

AcknowledgementsnProfessor P. Sydenham and Professor V. K. Jain have made useful contributions in the development of this work. Thanks are also due to Mr P. Pourbeik for implementing the prototype of the design reported in this paper using Prolog. Reviewers have contributed significantly in improving the quality of this paper.

R E F E R E N C E S

1. Siegfried E. M. and Wright J. R. ACE: taking an expert, system from prototype to product. Expert Systems and Knowledge Engineering (Beruold T., Ed.), pp. 121-131. Elsevier Science, North-Holland (1986).

2. Goyal S. K. et al. Compass: an expert system for telephone switch maintenance. Expert Syst. 2, 112-124 (1985).

3. Favilla J. R. Jr A knowledge-based system for disk-drive diag- nostics. Engng Applic. Artif. InteU. 3, 282-287 (1990).

4. Laffey T. J. et al. Reasoning about fault diagnosis with LES. IEEE Expert 1, 13-20 (1986).

5. Gob W. L. and Chart C. K. ESHDD--An expert system for hard disk drives. Engng Applic. Artif. Intell. 3, 186-197 (1990).

6. Ali M. and Scharuhorst D. A. Sensors-based fault diagnosis in a

B I B L I O G R A P H Y

1. Abadir, M. S. and Breuer M. A. A knowledge-based system for designing testable VLSI chips. IEEE Design Test Comput. 2, 56- 58 (1985).

2. Bielawski L. and Lewand R. Expert systems Development Building PC-Based Applications. QED Information Sciences Inc., Massachusetts (1988).

3. Bowman R. J. A rule based approach for adapting analog macrocells to specified circuit contexts. 30th Midwest Symposium on Circuits and Systems, 1300-1303 (1988).

4. Birmingham W. P. and Kim J. H. DAS/LOGICrU: a rule-based logic design assistant. Proc. Second Conf. Artificial Intelligence Applications, pp. 264-268 (1985).

5. Bennett J. S. and Hollander C. R. DART: an expert system for computer fault diagnosis. Proc. Seventh International Joint Conference on Artificial Intelligence, pp. 843-845 (1981).

6. Bolc L. and Coombs M. J. (Eds) Expert System Applications, pp. 257-308. Springer, Berlin (1988).

7. Brown J. S. et al., SOPHIE: a sophisticated instructional environment for teaching electronic troubleshooting. Bolt Beranek and Newman, Inc., Report 2790 (1974).

8. Crawford T. M. and Marton V. A machine learning approach to expert systems for fault diagnosis in communications equipment. Comput.-Aided Engng J. IEE 4, 31-38 (1987).

9. De Geus A. J. and Cohen W. A rule-based system for optimizing combinational logic. IEEE Design Test of Comput. 2, 22-32 (1985).

10. De Man H. J. et al., DIALOG: an expert debugging system for MOS VLSI design. IEEE Trans. CAD CAD-4, 303-311 (1985).

11. Fink P. K. and Lust J. C. Expert systems and diagnostic expertise in the mechanical and electrical domains. 1EEE Trans. Syst. Man Cybernet. SMC-17, 340-349 (1987).

12. Fischler M. A. and Firschein, O. Intelligence, the Eye, the Brain and the Computer. Addison-Wesley, New York (1987).

13. Frenzel J. R. L. E. Crash Course in Artificial Intelligence and Expert Systems. Howard W. Sams, U.S.A. (1987).

14. Giarratano J. and Riley G. Expert Systems. PWS-Kent, U.S.A. (1989).

15. Handreck KI. An expert system for transformer impulse test diagnosis. Engng Applic. Artif. Intell. 4, 11-21 (1991).

16. Harmon P. and King D. Expert Systems. Wiley, New York (1985).

446 J .G. ROWLAND and L. C. JAIN: KNOWLEDGE-BASED SYSTEMS

17. Hart A. Knowledge Acquisition for Expert Systems. McGraw-Hill, New York (1986).

18. Jain L. C. et al., Power supply design in Prolog environment. Special issue on electronic instrumentation. IE. In press.

19. Jain L. C., Rowland J. and Bowden B. S., ASHLESHA: a knowledge-based filter design system. Prog. Int. Conf. Instrumentation, Measurement and Control, 11-21 (1991).

20. Jain L. C. and Harpas P. PC software for electronic circuit design in printed circuit form. Comput.-Aided Engng J. lEE 5, 148-152 (1988).

21. Janac G et al., A knowledge-based GaAs design system. VLSI System Design Vll , 68-75 (1987).

22. Joobbani R. and Talukdar S. N. An expert system for understanding expressions for electric circuit analysis. Proc. Ninth Int. Conf. Artificial Intelligence, Los Angeles, pp. 23-25 (1985).

23. Joobbani R. and Siewiorek D. P. WEAVER: a knowledge- based routing expert. IEEE Design Test Comput. 3, 12-23 (1986).

24. Joshi R. C. and Khandelwai V. K. Expert VOR fault diagnostician. Proc. Third Annual Workshop on Robotics and Expert Systems, pp. 283-287 (1987).

25. Kelly V. E. The Critter System: automated critiquing of digital circuit design. 21st Design Automation Conf. pp. 419-425 (1984).

26. Kim J. M. et al., Exploiting domain knowledge in the IC cell layout. 1EEE Design Test Comput. 1, 52-64 (1984).

27. Kowaiski T. J. and Thomas D. E. The VLSI design automation assistant: an IBM system/370 design. IEEE Design Test Comput. 1, 60-69 (1984).

28. Kowalski T. J. and Thomas D. E. The VLSI design automation assistant: what's in a knowledge-base. 22nd Design Automation Conf. (1985).

29. Kowalski T. J. et al., The VLSI design automation assistant: from algorithms to silicon. IEEE Design Test Comput. 2, 33-43 (1985).

30. Liebowitz J. Introduction to Expert Systems. Mitchell Publishing, U.S.A. (1988).

31. Lin Y. L. and Gajski D. D. LES: a layout expert system. IEEE Trans. CAD 7, 868-876 (1988).

32. Lindsay S. Practical Applications of Expert Systems. QED Information Sciences, U.S.A. (1988).

33. Lirov Y. and Melamed B. Expert design systems for telecommu- nications. Expert Syst. Applic. 2, 219-228 (1991).

34. Luger G. F. and Stubblefield W. A. Artificial Intelligence and the Design of Expert Systems. Benjamin-Cummings, U.S.A (1989).

35. Mittal S. et al., PRIDE: an expert system for the design of paper handling systems. IEEE Comput. 19, 102-114 (1986).

36. Pipitone F. The FIS electronics troubleshooting system. IEEE Comput. 19, 68-76 (1986).

37. Prevost M. P. and Laffey T. J. Knowledge-based diagnosis of electronic instrumentation. Proc. Second Conf. Artif. Intell. Applic. pp. 42-48 (1985).

38. Purcell E. G. Fault diagnosis assistant. IEEE Circuits Devices Mag. 4, 47-59.

39. Rauch-Hindin W. B. A Guide to Commercial Artificial Intelligence. Prentice-Hall, Englewood Cliffs, NJ (1988).

40. Russel G. et al., (Eds) CAD for VLSI. Van Nostrand, U.K. (1985).

41. Samardar M. et al., EXACT: expert in analogue circuit topology. Expert Syst. Applic. 2, 137-152 (1991).

42. Saucier G. et al., ASYL: a rule-based system for controller synthesis. IEEE Trans. CAD CAD-6, 1088-1097 (1987).

43. Savell D. V. et al.--Scheduling semiconductor wafer production: an expert system implementation. IEEE Expert 4, 9-15 (1989).

44. Savory S. (Ed.) Artificial Intelligence and Expert Systems. Ellis Horwood, London (1988).

45. Scholz J. B. A real-time knowledge-based system for frequency management in communications. A1 '88 Australian Joint Artificial Intelligence Conference, pp. 23-32 (1988).

46. Scown S. J. The Artificial Intelligence Experience: An Introduction Digital Equipment Corporation, Maynard, MA (1985).

47. Sell P. S. Expert Systems--A Practical Introduction. Macmillan, London (1985).

48. Shah V. and Buckner G. D. Potential defence applications of expert systems. IEEE Aerospace Electronic Systems Mag. 3, 15- 21 (1988).

49. Shaw R. RESCUE-on-line real-time artificial intelligence. Comput.-Aided Engng J. IEEE 4, 29-30 (1987).

50. Simoudis E. A knowledge-based system for the evaluation and redesign of digital circuit networks. IEEE Trans. CAD 8, 302- 315 (1989).

51. Subrahmanyam P. A. Synapse: an expert system for VLSI design. IEEE Comput. 19, 78-89 (1986).

52. Tsareff C. et al., An expert system approach to parameterized module synthesis. IEEE Circuits Devices Mag. 4, 28-36 (1988).

53. Wilkinson A. J. MIND: an inside look at an expert system for electronic diagnosis. IEEE Design Test of Comput. 2, 69-77 (1985).

54. Waterman D. A. A Guide to Expert Systems. Addison-Wesley, New York (1986).

55. Wolfgram D. D. et al., Expert Systems for the Technical Professional. Wiley, New York (1987).

Documents

Knowledge-based systems for instrumentation diagnosis, system configuration and circuit and system design