Diagnostic Expert System in the PF LINAC

Isamu Abe, *Masaharu Kitamura and Kazuo Nakahara
Photon Factory, National Laboratory for High Energy Physics (KEK)
1-1, Oho, Tsukuba-shi, Ibaraki-ken 305, Japan*Department of Nuclear Engineering, Tohoku university
Aramaki-Aza-Aoba, Sendai, Miyagi 980
Abstract
A prototype diagnostic expert system (ES) was developed
for the Photon Factory 2.5-GeV electron/ positron LINAC
injector system.
The ES has been on-lined with theconventional linac computer network for receiving real data.
This project was undertaken in an attempt to reduce the linac
operator's mental workload, diagnosis duties, and to explore
Artificial Intelligence (AI) technologies.
The outlook for ES and its problems, and what has beenachieved are outlined in this presentation.

theories for problem solving, an expert system seems to beappropriate. When there are several candidate procedures, oralgorithms, involved in problem solving. Also, ES should be
useful and more efficient than conventional programming.
Since most ES are flexible, if we change the physicalstructure of the accelerator, the K/B can be gradually extended
by adding new knowledge while being refined. Programming
costs will be minimized by using ES. This is the essential
advantage of an expert system.

Ill. Definitions of AI

I. Introduction
Diagnostic problems are relatively well understood both
empirically and theoretically. A variety of shells / tools areavailable on the market to facilitate the implementation ofdiagnostic systems. We have developed several diagnostic ES
for the LINAC and some are now under operation. Having
gained experience through previous projects, we built a newhybrid ES this time. The application described here is an ES
for the injector system of the Photon Factory (PF) LINAc[l4], which is being operated a total of 5000 hours per year,
making injections to the PF storage ring and the 1RIST AN
e+/e- collider. Accelerators (LINAC) are complex devices,using many thousands of components.
We have been
looking for appropriate expert system shells and tools withwhich we can easily and rapidly establish an expert system.
For several years, a small ES based on a personal computer
was used for exploring applications of AI techniques. A
prototype diagnostic system has been built in order todetermine whether or not the various problems can generally
be solved using an ES frame-work; a knowledge base (K/B) for
the accelerator domain and task analysis were also investigatedin this project.

Some people think that "AI by itself can solve all of theproblems." We must be very careful concerning this idea AI
is not magic, and its capability is still limited in solving
practical problems . On the other hand, there are other people
who believe that AI can do nothing worth while at all. We,
thus, need to define AI before any discussion.
We have seen many definitions. People's dreams are big, andthey could have answered that "Artificial Intelligence is thescience of constructing a thinking machine."
Marvin Minsky gave a new definition: "Artificial
Intelligence is the science of making machines do things thatwould require intelligence if done by men".
Today, the abbreviation "AI" is used with the meaning of"Advanced Information processing technology". From thisperspective, AI will certainly become more and more
important in the Accelerator domain.
We discuss here only knowledge-based systems which are asubset of AI technology. In most cases, ES is a rule-based
production system which dispenses with specialized
knowledge of a well-defined domain.
It is said that ES belongs to the most importantdevelopments of a new type of software generation.

II. Why we need ES for the accelerator

IV. History of AI

When any fault or trouble occurs in the LINAC, theoperator is required to recover the system, even at midnight,
even though, he may not be an expert regarding many of thefields required to diagnose the specific trouble.
Diagnosing faults in a complex process is a task that
requires experience and considerable knowledge in many fields.
Thus, any assistance given to the linac operator regarding
diagnosis and operation is extremely desirable.
When human experts are scarce, and when problems must
be solved for which there are no established solutions or exact

It should be mentioned that AI is still very young.
Fundamental AI researches are necessary for continuously
defining and realizing Al's future.
The term "Artificial Intelligence" was invented at theDartmouth Conference in 1956, where John McCathy(Stanford) and Marvin Minsky (from :MIT) were participants.
After that, a new field of research was born.
The feasibility of the first expert system· was demonstrated in
the 1970's under the leadership of Edward Feigenbaum. There
have been many successful ES in the past. If we classify the562

generationsl 6l, from the viewpoint of an ES tool, we candefine three generations asfollows:
In the 1st generation, until 1980, simple tools andlanguageswere utilized;
In the 2nd generation, 1980 to 1985, hybrid tools werecommonly used.
We are now in the 3rd generation, which is characterizedby easy-to--usetools and task oriented; domain-specified tools
are available now. We have established commercial systems,specialized languages, and tools for developing such systems.
Today, the development of the so-called knowledge based
systems hasstarted moving out of the research laboratories ofpure informatics into other disciplines of science.

interlocks and analog data displaying at the local pulser panel.
Of course, ES requires more information for a complete
diagnosis. Most of the same data which is available to checks
locally is transfered to the main computer and as well as to theES. The ES would be triggered to start diagnosis uponreceiving a fault message through the network.
The ES has the following configuration: HP9000/370(development station), 375 (LINAC operator's console), HP·UX, C, NEXPERT-OBJECT (hybrid-tool), Data-View(graphic tool), equipped with a 16MB RAM:,300MB(600MB)
hard disk, and 3.5" floppy disks. There is a pre-process
station (FMR-50 personal computer) which has a 600MB
magnetic optical compact disk (CD), and is connected to theDSLink. There are about 30 personal computers (PC) underthe DSLink network, and each PC has its own purposes orfunctions such as gateway, server, accelerator operator's
console, monitors, development, expert system, and OS/2stations.
The DSLink operator's console network of the LINAC is
Easy-to-use Tool
connected to the conventional LINAC networks through theNexpert Object
gateway (FMR- 70 HX3).
Expert Ease
In addition to rule-like knowledge, waveform information
GURU
is also highly important in carrying out diagnostic tasks.
Task oriented
Pattern recognition using a neural network: is thought to be
CSRL, DSPL,
helpful in this context.(6] A neural network is well suited
SAK.>\SHearsay IT
for pattern-recognition problems, but has a disadvantage thatthe learned knowledge is hidden in the weights of thenetwork's connections. We have thus developed what is calleda hybrid ES by combining a neural network.
The injector ES has a debugging mode for diagnosis
1st Generation 2nd Generation
3rd Generation
Simple Tool
Hybrid Tool
Specialized Tool
which makes it easy for users to carry out simulation at any
I:111.iu:u ►time.
The ES can operate in two modes (AUTO orat the operator's discretion. In the AUTO mode,
1975
1980
1985
1990
when thereceives serial shutdown data from a pre/post
1of Toolprocess station through the network, it automatically andperiodically starts the diagnosis procedure. During a periodic
analysis, ES starts a monitor programme to collect raw data
V. Software tools
for the error diagnosis process during the data-taking phase.
For the most part, the ES tool comprise an inference The interactive debug mode can be invoked if an error in theengine, knowledge-aquisition,a knowledge-base,a explanation run or one of the test modes occurs. Symptoms will bederived and possible repair actions will be proposed. In thesystem and a user interface.
An ES tool should provide at least the following features: debugging mode, the inference action is initiated, or fired,manually by the knowledge engineer or user. Each rule must
• transparency and portability of the knowledge base,be simulated step by step in this MANUAL mode both before• expert-like diagnosis (less questions, learning,)
and after running using empirical knowledge.
• a graphicaluser interface for representationand afunction of explanation
VII. Defining the task and applicationdomain
are very important in knowledge handling. Several operation
modes are also necessary, such as a Run mode, Test modes
In the accelerator domain, ES would be useful forand interactivedebug modes.
Some useful knowledge-aquisition tools are available on the operation support. fault diagnosis, and design work:. We haveinvestigated the task of diagnostic ES for the injector of themarket which are helpful for simple surface knowledge.
LINAC. The following tasks were important for ES:l) On-line data processing.
VI. Hardware/software configuration
2) Finding heuristic methods of diagnosis from humanexpert (surface knowledge).
The LINAC injector control system has several
3) Local checks using oscilloscopes, measurement tools,
microprocessor units (M"PUs)which are connected to a local
and visual checks.
network (LOOP-2).
4) Logbook, chart, and drawing checks.
When a fault occurs in the injector part, the :MPU picks up5) Modeling,and calculations (deep knowledge).
the first reason for the trip down and sends a message to aminicomputer, MELCOM 70, in the subcontrol room These are the fields that an expert carries out during diagnosis;
through LOOP-3, LOOP-2, and CAMAC. There are 50 an expert system should thus duplicate them.
liH

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-----.... ...
______...,.... 1o--------------MINICOMPlITER
MELCOM 70/30#S

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MEL.COM 70/30

FMR70

AUI

Operator'sconsole

Development
stations

fgJ

FMR

C:::3
Gateway

C:::3

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DSLINK#l

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HP375
FMR50

HP-UX HP9000/370

FMR70
FMR70fgJ
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NeuralExpert SystemInf. Shell

ServerOS/2 Station

Expert System
Fig. 2ServerHard DiskPrinterExpert Systemand the PF Linac Control console network

VIII. Diagnostic ES and the Problems

inductive.
6) The handling of uncertaintyin knowledge as well as in

The most difficult problems to be resolved have been theacquisition and representation of knowledge, as well as theselection of an inference strategy. When we develop an AIsystem, we must be clear about the following items regarding
the planned applications:1) The diagnosticresolution must be detennined.
2) Necessary informationmust be availableon-line;
otherwise,theoperator must supply a large amount ofdata to the expert system, which is quite unrealistic inpractical situations.
3) The introductionof deep knowledge,such as model
based diagnosis,is desirable.
Without deep knowledge,the numberof requiredrules
canbe quite large.
Domain-specificknowledgecanbe representedin alogical programingsystem, in a frame-basedsystem,in rule-based systems, or in hybrid systems.
4) The strategy for controllingthe inferenceprocedureis
critically irnportanL
This strategy should be made consistent with thecontent of rules in the knowledgebase
5) Inference mechanisms:We must determinewhich
directionof reasoningis appropriate,a forward-directedstrategy from an initial to a goal state, or a backward.
Inferencemethodmay be deductive,abductive,orthe observeddata is also necessary.
7) Appropriateactions should be implemented.

If a new unknown problem occurs, the system should
propose to contacta field expert
The system must assist the expert in updating therelated part of a knowledge.
8) Is a learning system or Case-BasedReasoning (CBR)
important ?

IX. Knowledge Acquisition
Generally, knowledge acquisition can not be based onany model in this kind of diagnosis. Knowledge engineersor
experts must build knowledge~base extracting rules andprocedures using an empirical approach through interviewing
experienced field experts regarding both operation andrepairing.
Here, we have a bottleneckconcerningknowledgetaking.
For making the LINAC injector diagnostic knowledgebase, about one month during the first stage has been spent ininterviewing and knowledge engineering for a shallow target.
As the second stage, we are refining the knowledge base inorder to obtain more knowledge. One hundred expressions
concerning production rules, some frames, and object are
being handled in the ES.

564

X. Conclusions

References

The diagnostic capability is still limited; moreover, wehave started with a shallow target. It was sufficiently fasterthan human (operators)diagnosis regarding execution.
A new K/B(KnowledgeBase) is now being added and tested for
expected phenomena in order to obtain more reliability.
It has become feasible now in the Accelerators field. We
are sure that expert system technology will provide new
possibilities for software systems, especially in futureaccelerators. Expert systems should be used more in ourfield, as in others.
As a final statement, the following will be the areas inwhich we must carry out research:
How to deal with a large domain,(many tasks).
How to model applications, such as task anddomain analysis.
We need portability ofK/B.
Leaming systems and Case BasedReasoning.

Acknowledge,ments
The author would like to thank Dr.S.Ohsawa, M.Yokota
and A.Shirakawa for their help in the present phase ofknowledge acquisition, as well as Professor A.Asami, Directorof the Injector-LINAC division in KEK, and Prof.
K.Sugiyama of Tohoku University for their support to thisproject565[l] I.Abe, H.Hanaki, S.Anami and K.Nakahara"Klystron Modulator Trouble Shooting"
LINAC conference (1989)[2] A.Shirakawa,I.Abe, and K.Nakahara"OSfl AND DSLink FOR THE PF LINAC CONTROL"
16th LINAC conference (1991)[3] I.Abe, M.Fujieda, "DISPLAY SYSTEM FORCONTROL CONSOLE OF KEK
Proceeding of the 6th symposium on Accelerator Scienceand Technology (1987)[4] K. Nakahara, I. Abe, R.P. Bissonnette, et,alNucl. Instr. and Meth.A251(1986) 327-336[5] K.Nakahara, I. Abe, A.Enomoto, Y.Otake, and T,Urano
Nucl. Instr. and Meth.A247(1986) 153-158[6] AI H.akusho
ICOT-JIPDEC AI center[7] Isamu ABE, Masashi Fujieda
Proceeding of the 6th symposium on Accelerator Scienceand Technology(1987)[8] M.Mutoh, T.Oonuma, Y.Shibasak:i,Isamu ABE andK.Nakahara"Development of a Diagnostic system for Klystron
Modulators using a Neural Network", This conference