Combined Symbolic-Artificial Neural Net Alarm Processing System

R. Khosla T. Dillon

Expert and Intelligent Systems Laboratory Department of Computer Science and Computer Engineering

La Trobe University Bundoora, Victoria-3083, Australia

Abstract

The properties of Artificial Neu-ral Networks(ANNs) like massive parallelism, gen­eralization make them amenable for application in various diagnostic/time-critical problem domains in­cluding alarm processing in power systems. How­ever, enormous size of power systems has inhibited real time use of ANNs. In this paper, we apply an integrated model for real time alarm processing in a real world terminal power station. The integrated model is a combination of a generic neuro-expert sys­tem model, object model, and unix operating system process(UOSP) model. We show how the integrated model helps us to cope with the power system size, real-time system constraints like data variability and fast response time. Keywords and Phrases: Alarm Processing,Neuro­Expert Systems, Power System

1 Introduction

It has been suggested by several authors including the present ones[l,2] that Expert Systems(ESs) can be used for helping an operator with Alarm Process­ing. Several systems have started to find their way into the industry. One of the problem areas that has been identified is that in cases of i) multiple sympa­thetic alarms and, ii) multiple events, the number of combinatorial possibilities becomes very large. This makes development of production rules for each of these cases time consuming and difficult . The large number of rules also makes a suitable real time re­sponse difficult.

The real time use of standalone ANNs in alarm processing has been inhibited due to enormous size of power systems (resulting in an exponential increase in number of variables), their inability to interact with conventional symbolic databases, and need for preprocessing of data before feeding to an ANN. Fur-

259

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thermore, operators do not feel confident taking ac­tion on the generalized result of an ANN especially without any explanation.

The limitation of one technology[3] infact reflects the strengths of the other which has provided us the motivation to integrate the two together. In our re­cent paper [3] we outlined the limitations of the two technologies. We have described in some detail the development of a generic neuro-expert system model which establishes a conceptual and structural basis for integration of ESs and ANNs. In order to realize the strengths of both ESs and ANNs an integrated model has been proposed in that paper. In this pa­per, we concentrate on the application of the inte­grated model in a real-time object-oriented Alarm Processing System(APS) for a terminal power sta­tion. We describe how the integrated model can help in dealing with issues like size of the power system, reliable inference, response time and data variability. However, before describing the application, in order to place things in perspective, we briefly outline some

of the ideas behind the generic neuro-expert system model and the integrated model.

2 Neuro-Expert System Model

The Generic Neuro-Expert system model[3] is shown in Figl. Briefly, a) ANNs have two impor­tant properties, namely, inherent parallelism and dis­tributed representation. At the top level, distributed representation allows representation of microfeatures related to different aggregated classes and inherent parallelism makes a provision for simultaneous inter­action between these microfeatures. These proper­ties makes them suitable for problem decomposition at the top level or near top level (in case we require some prior pre-processing of data). The decomposi­tion can occur in terms of decomposition of aggre­gated classes into individual classes or another in­termediate level,b) problem solving process in many diagnostic systems generally involves different lev­els of abstraction. These different levels of abstrac­tion breakdown the degree of complexity of the prob­lem and levels of classification(from elementary to specific). The number of variables/microfeatures re­quired for elementary classification are generally less than for specific classification. Thus, at the higher abstract levels it is possible to use ANNs in the clas­sification mode (where the ANN is trained for the entire data set), c)ANNs which learn the heuristics can be looked upon as mechanisms for generating goals. They can be used to explicitly tell an ES at a lower level where to start from or activate an ES/ ANN module;d) At lower levels where further de­composition is not possible, ANNs can be used in the generalizing mode(where an ANN is not trained for the entire data set) if required. A symbolic checking mechanism should be used to cross-check the results of an ANN to avoid the side effects of erroneous gen­eralization;d) ESs can be used for interfacing with symbolic data bases, knowledge explanation, check­ing relevancy of data to be input to an ANN, and for classifying novel classes where an ANN may fail.

In order to reflect ANN associated features of distributed control, parallelism, ESs associated fea­tures like inheritance, and maintenance alongwith the above features, an integrated model shown in Fig 2 has been used. The integrated model encom­passes an object model, an Unix OS Process(UOSP) model, and the neuro-expert system model. The ob­ject model helps to derive the notion of an ANN ob­ject, an ES object, an UOSP object, and a prob­lem domain object . This allows the development of generic data structures and methods and a multiple inheritance structure. More details are given in[3].

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3 Application

Alarm processing relies on status information or measurements gathered at a large number of points distributed throughout the system. A large EMS will typically scan 20,000 to 50,000 points every few seconds and will have the ability of displaying 500 alarm messages per minute. The enormity of the system and rate at which messages are displayed on the monitor increases the complexity of the prob­lem multifold. This complexity leaves a operator in the control centre who has to analyse these mes­sages in a highly constrained time frame suffering from an information overload. The analysis is fur­ther complicated by the characteristics of the alarms like a) Some alarms are needlessly repeated and dis­tract from more important ones; b) Faulty alarms; c) Maloperation of protective equipments; d) Mul­tiple sympathetic alarms for a single event; e) Mul­tiple alarms for multiple events; f) Alarms are not annunciated in order of priority; g) Alarms remain displayed after being acknowledged.

The integrated model has been used by us in de­veloping a real time object-oriented alarm processing system(APS) for the 220kv automated Thomastown Terminal Power Station(TTS) of the State Electric­ity Commission of Victoria(SECV). There are a to­tal of 42 circuit breakers(CBs) resident in TTS it­self and 23 CBs in its 16, 66kv connected substa­tions whose operation is monitored under TTS. The alarms (220KV and 66KV) received by the regional control centre automated TTS and its connected dis­tributed substations are often the only data that in-

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dicates equipment malfunction. The characteristics of the problem are similar to those listed above. The objective of the system is to assist the operator in a) Isolating the event, and b) Isolating the cause of the event and taking remedial action.

3.1 Classification of Alarm Messages

The decomposition of alarms at TTS is shown in Fig 3.

Routine Alarms These are alarm messages that occur on regular basis. They indicate the proper functioning of equipment that has operated as a re­sult of the activity of a timing device or a logic cir­cuit. These alarms must be suppressed in case of correct operation. In case of a maloperation, malop­eration message must be displayed.

Known Fault Alarms These are those alarms which have been identified by the operator as faulty. These alarms are usually single alarms and are re­lated to faulty plant or telemetry equipment. These need to be suppressed.

Emergency Alarms Single building fire alarms are indicative of a station or substation fire and need to be displayed immediately. However, if a fire equip­ment malfunction alarm is associated with building alarm then the message needs to be suppressed.

Alarm Equipment Failure Alarms These are a string of uncorrelated multiple alarms encompassed by DC supply failure alarms or a single DC sup­ply failure alarm.. These uncorrelated alarms are usually communication alarms and ancillary equip­ment alarms. All other alarms except the 'ALARM-

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Fig 4: APS Design Structure

EQPT-DC-FAIL' need to be suppressed . A message is to be displayed indicating alarm equipment failure .

Repeated Alarms As the name suggests, the same alarm repeats itself one or more number of times. Each repetition of the alarm needs to be sup­pressed/ deleted. First occurrence is to be processed and inferenced. If it cannot be inferenced, it is be displayed as faulty. Future occurrences of the alarm in certain time range are to be suppressed. The first occurrence of the alarm is also recorded in a database for future use.

Sympathetic Alarms The alarm or alarms in­dicating actual event are sometimes swamped by alarms from ancillary or related equipment like the motor-generator alarms, oscillograph alarms and communication alarms. These are called sympa­thetic alarms. These alarms indicate a voltage-dip or a communication equipment failure. If the commu­nication alarms occur alongwith the motor-generator alarms and oscillograph alarms the event is classified as a voltage-dip and message displayed and commu­nication alarms are suppressed. Otherwise, depend­ing upon the pattern of communication alarms, the event classification will be loss of a communication channel, microwave equipment failure, communica­tion equipment power supply failure, etc.

Network Circuit Breaker (CB) Alarms Mul­tiple Network CB alarms may indicate multiple

events (e.g., line, bus and transformer faults) or sin­gle events within in the individual terminal station (in our case TTS) . They could also indicate multiple station failure events which could run upstream (i.e. 220kv terminal stations) or downstream( i.e., 66kv substations) . These events have to be identified reli­ably and displayed immediately for urgent operator action.

3.2 Design and Implementation of APS

The object-oriented design structure and imple­mentation hierarchy of APS are shown in Fig4 and Fig5 respectively. The UOSP object, AD object, ANN object, ES object in Fig4 are generic classes. BSl, BS2, etc. represent individual bus section ob­jects. TSl, TS2, are transformer sections represent­ing two transformers each. P, NH, etc are different substations fed by TTS.

The Alarm Processing System(APS) is imple­mented as an object-oriented, continuous, con­currently processed distributed system. Nexpert object[6] along with external C routines has been used to implement APS. Nexpert Object provides facilities for representing class and object structure with inheritance of attributes and methods from generic classes. In Fig5, the light dashed lines circles and light dashed line circles with solid line circles inside them represent the alarms which have been categorised as repeated, communication, etc. "A 78" represents an alarm with id 78. The dashed line/s be­tween two domain objects( encapsulated in two differ­ent process objects, e.g, DCPO and NDCPO) shows the domain object which is connected in forking the lower level process object. Fig5 also shows a uni­form communication interface between process ob­jects and encapsulation of problem domain objects.

APS uses alarm data used by control room staff for real time inferencing. No relay status informa­tion is available at the regional control centre at the moment for interpretation of network faults.

The generic classes(Fig4) help us to build hetero­geneous ES-ANN objects with both ES and ANN related attributes and methods. ESs related meth­ods( e.g. Rules)in APS are used for converting sym­bolic data into numeric form and activating a ANN, comparing time differences (obtained from examin­ing past real time data and the operators) between successive alarms, checking relevancy of symbolic data to be input to an ANN, interacting with sym­bolic databases and symbolic explanation of outputs received from ANNs. The process of task decomposi­tion has allowed us to use ANNs in the classification mode at all levels . Independent inter-process com­munication channels provide for continuous, cyclic, and concurrent operation.

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3.3 Interpretation of Alarms by APS

Here we briefly describe the interpretation of alarm messages by different process objects.

3.3.1 Data Sending Process Object (DSPO)

For the purpose of simulation, the DSPO reads 10 alarms every 3 seconds. It filters out all the OFF, RESET, and CLOSE messages which are not to be used for inferencing. It creates an alarm object database with attributes like Id, Time, Date, Loca­tion and, Message. It writes new data in a new data file or forks the DFPO.

3.3.2 Filtering Process Object (DFPO)

Before starting to process the new data, the DFPO asks the DSPO if there is any more new data. The filtering is required to avoid improper classification by the DCPO. The DFPO encompasses four objects. The filtering object activates the three child objects sequentially. The repeated object filters out the re­peated versions of a alarm message if any and stores the first occurrence of that alarm in a database to filter out future occurrences in the next lot within a time difference of 1 minute (derived from operator experience and real time data). The first occurrence is processed for inference.

The DC failure object filters out the alarm equip­ment malfunction alarms by matching them against a list of alarms in the database which are known from experience to go off in case of alarm equip­ment malfunction. This matching, however, is only done if there is a ALARM_EQPT_DC..FAIL alarm in the group of new messages being processed. This 'ALARM_EQPT_DC_FAIL is stored in database and retrieved for the next alarm snap shot. The alarms in the next alarm snapshot are matched against the known list of alarms if the time difference is less than 15 minutes. Otherwise, the operator is prompted for advice on the alarm equipment status before deleting any future alarms. The Known fault object filters out the alarms which have been previously identified by the operator as faulty and are recorded in the known fault database. If the match with the database fails, the alarm is processed as normal.

3.3.3 Data Control Process Object (DCPO)

DCPO uses an ANN to decide which child pro­cesses it has to write to or fork. The ANN at this level uses abstract knowledge in the form of key­words. Just like the operator, DCPO uses keywords like "FIRE", "CB", "COMM", "OSCILLO" etc. in alarm messages to formulate discrete inputs to the ANN. The ANN at this level does rudimentary clas­sification like, which class or classes shown in Fig5

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Fig6: Comparison of Response Times

b. All inferenced alarms are stored in a histori­cal data base by different process objects, thus making a provision for reinferencing. In case of multiple network CB alarms where ANNs are used, we only need to store/retrieve the previ­ous alarm snapshot input/output activation/s and time of the last alarm id in the previous alarm snapshot. This allows for very quick re­inference because of massive parallelism inher­ent in ANN. The present and immediate pre­vious alarm snapshot activations are used for reinferencing at the moment. The maximum time difference between reading of two alarm snapshots from the alarm data file is 3secs. The previous alarm snapshot activations are con­sidered valid if time difference between the last alarm in the previous alarm snapshot and the first alarm of the present alarm shot is less than 1 minute. In the case of conflict between two inferences a revised message will be displayed. Say for example, based on a previous alarm snapshot, inference made was that 3 feeders connected to a 66kv Bus2 are faulty. Based, on existing alarm snapshot and the previous alarm snapshot, now the ANN concludes that 66kv Bus2 is faulty. This revised message will be displayed.

3.4.4 Reasoning Under Time Constraints

A comparison of response time achieved on Sun 4/280 with a previously built ES by us only with rules and the integrated ES-ANN alarm processing system(APS)is shown in Fig6. We expect the re­sponse time of APS to drop appreciably on Sun 4/470 SPARC station on which APS will be installed later. The response time here is the time elapsed from event to displayed inference. The starting points of the two curves are indicative of the time elapsed in loading the knowledge base with rules only and the one with ES-ANN respectively. The curves in Fig6

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are based on the assumption that only lower level objects (e.g. 66kv BS1.. .. BS4 objects) which infer the specific faults have to be reloaded for different sets(measure points in Fig6) of CB messages. The other higher level objects like the Data Sending Ob­ject, Data Filtering Object, etc. are assumed con­tinuously loaded in a real-time continuous, parallel distributed APS. For worst case scenario, that is in case all the objects are freshly loaded, the response time for severe multiple network faults (e.g. 32 CB messages) is 28 sees. This is still below the accept­able response time requirement of 30 sees for severe faults . The corresponding response time for ES with rules only is 58 sees. Because of one step reasoning property of ANN, it is also possible to tell the op­erator in advance, how much time will be taken for inference.

3.4.5 Countering Noise and Fault Prediction

Depending upon the number of CBs in a particu­lar section (i.e, Busl or Bus2) the effect of number of CB malfunctions is taken into account. Like for example, in case of 220kvbusl section where there are 8 CBs, the ANN is trained for any 2 CB mal­functions to classify a bus fault. Another advantage of incorporating these malfunctions is that in case of severe faults, if for any reason the 1/2 CB alarm messages is/are missed out by APS while inferencing with the existing alarm snap shot or actually have malfunctioned it will still infer a bus fault correctly. However, in the absence of relay information, com­prehensive CB malfunction detection is not possible. Since the ANNs are trained in classification mode, all possible TTS network faults excluding a proportion of CB malfunctions are covered.

4 ANN Training and Scalability

ANNs are trained with discrete inputs and out­puts. All ANNs used in APS at the moment are trained with a single hidden layer using backpropa­gation algorithm. The training time has varied be­tween 100 to 20000 cycles for different ANNs. The response time if one uses hash tables will be slightly faster than their ANN counterparts for some of the small sized ANNs in Fig5. However, hash tables be­sides needing large amount of memory space, suffer from collision problem for similar patterns, fixing of upper bound of the array in case of large number of patterns. In alarm processing, where there are a large number of similar patterns, the collision prob­lem can have a detrimental effect on the response time. On the other hand, the size(i.e. the number of weight links) of an ANN is more a function of the number of distinct classes in the patterns than a

(e.g. VDPO, NDCPO, etc) does a new set of alarms belong to. Here, it is ensured that only the alarm data related to a particular child process object is sent to it.

3.3.4 Comms Data Process Object (CDPO)

CDPO is activated/inferences only if there are no voltage dip alarms in new alarm data. It inferences with an ANN and does specific classification into communication failures, like loss of a communication channel, microwave equipment failure and displays inferenced results on the screen. VDPO, EDPO and, RDPO inference based on requirements given in sec­tion 3.1.

3.3.5Network CB Data Control Process Object

As can be seen, the network CB alarms are decom­posed into Trans section, Bus sections(220kv and 66kv), Sub Stn section alarms. An ANN is used here to decide which section/sis/are to be activated. Here, also a set of keywords like, "B-T", "66", "220", "TR", etc. are used to activate one or more of Bus/Trs/Sub Stn section/s objects. These objects simply fork their child process objects which are a further decomposition of the individual sections (see Fig5). This decomposition is brought about by sec­tionalsing the CB network[3] . This sectionalising reduces the degree of complexity of the CB network, increases the reliability of inference and reduces the need for using the ANNs in the generalization mode. The network at TTS is designed with one and a half breaker system. The line faults are covered by the ANNs for respective decomposed bus sections. There are in all 28 ANNs in APS.

The child process objects under each section ob­ject are run as parallel distributed process objects. The inferences made by the child process objec~s at the lowest level are written back to their 4 section objects for pruning respectively. This is to take care of the overlaps between different sections and to in­fer CB malfunctions correctly. For example, sup­pose that 66kv Bus3 section object inferences that 66kv Bus3 is faulty with no CB malfunctions, the 66kv Bus2 and 66kv Bus4 infer that interconnecting bus-tie CBs have malfunctioned. Then the pruning ANNs in the 66kv Bus section object will prune out these superfluous bus tie CB malfunctions reported by 66kv Bus2 and 66kv Bus4 child process objects and display only 'TTS Bus3 is Faulty' to the opera­tor.

3.4 Features of APS

In this section we describe features of APS which help it to cope with the size of the power system, real

time constraints like continuity, cyclicity, data vari­ability, and response time, and other fault diagnosis features like noise, fault prediction.

3.4.1 ANNs at different levels of abstraction

Fig5 shows the different levels of abstraction in the solution process. Fig5 also shows the number of pat­terns at that level and also the number classified by ANN correctly. It also shows the number of rules in each process object at different levels(e.g, in DCPO, 256 P indicates 256 Patterns, and 5 R indicates 5 Rules) thus indicating rule substitution. Rather than merging the different levels of abstraction and using one or two large ANNs, APS uses ANNs at different levels for elementary classification(e.g. DCPO level) into different classes, and specific classification (e.g. 220kv BSl process object, etc. )into single/multiple network(line, bus, transformer, feeder) faults and CB malfunctions. ANNs can also be seen as controllers (e.g DCPO, NDCPO) and providing problem decom­position and activating objects at the lower level.

3.4.2 Continuous Operation and Cyclicity

To ensure continuity and cyclicity of operation, all process objects look for new data after processing existing messages through inter process communica­tion channels with their parent process object.

3.4.3 Data Variability

It is an important aspect in alarm processing that inference made on an existing alarm snap shot can become wrong or invalid in the light of the new data. APS adopts a two fold strategy to minimize the pos­sibility of wrong inference namely

a. Before making inference on a particular alarm snap shot (which is fixed at the moment to 10 alarms at the DSPO level for simulation pur­poses), all process objects look for more mes­sages, if any, from their parent process object. This is possible, because all process objects which are executed concurrently can commu­nicate independently with their parent process object. Thus, DFPO can take 20 alarms (or the 20th alarm) before starting inference, DCPO can take 40 alarms (or the 40th alarm), ND­CPO, VDPO, CDPO, EDPO and, RDPO can take 80 alarms (or the 80th alarm).

It is better to limit the size of the alarm snap shot rather than keeping it arbitrary. If it is arbitrary then big spurt of alarms can cause a huge processing overload on DSPO, DFPO, and DCPO. As a result inferencing of impor­tant alarms like emergency, network CB alarms can be unnecessarily delayed.

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function of number of patterns. For example, some of the ANNs with 256 patterns have been trained by us with a 8-3-3 configuration/topology and a rep­resentative data set of 31 patterns only. The rep­resentative data set is chosen based on the classes of patterns and the sparseness among patterns. the The memory used by such an ANN is very small. The 3 classes, line fault, bus fault and malfunction will remain the same for alarm processing for data from different levels of protection, i.e the CB level, relay level, and analog level. However, the number of patterns will increase manifold.

Besides above, the alarm processing system(APS) for TTS has been built up with an open ended ap­proach keeping in view simultaneous constraints like fast response time, reduced memory requirements, distributed parallel processing, scalability, maintain­ability, reusability, and the possibility of scaling up some of the ANNs from classification mode to gener­alization mode. Although, we could have combined small sized ANNs to form fewer large sized ANNs, this has deliberately not been done for reasons of scalability and cost effectiveness as explained below.

At the moment only circuit breaker information has been used for inference in NDCPO(Fig5) and all the objects below it. When relay and/or sensor measurements are used the size of small sized ANNs would increase manifold. These changes will also be reflected upwards in DCPO. The fact is that APS will be able to accomodate these changes with much more ease than it would have, had it been designed with few large sized ANNs and/or large sized ESs. By allowing the changes to flow through the archi­tecture of APS, we are making provisions for scala~ bility. By allowing, the expansion of existing ANNs and ESs in different objects we are showing reusabil­ity. By being restrictive on the size of ANNs and their use in different modes, we are minimizing the risk of erroneous generalization especially for those ANNs which are presently being used in the classi­fication mode and will be used in the generalization mode as a result of scaling up. Thus, it is a good idea to use only a representative data set for train­ing of those ANNs for which all the data set is avail­able at the moment and achieve a generalization of 100%. This will facilitate incremental learning with increase in number of patterns and transition to gen­eralization mode. When ANNs are used in the gen­eralization mode, the distributed and modular APS structure is restrictive also on the size of the fault propagation model which may be used as a symbolic cross checking mechanism. Indirectly, we are try­ing to minimize the detrimental effect on reliability of the existing system as a result of change. H any new ES-ANN objects are to be added at various lev­els, the distributed parallel processing nature of the APS will minimize the effect on the existing respons~

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time. Further, with hierarchical and modular use of ANNs, the training time of ANNs and consequently the development time of APS will be sharply reduced for large scale systems with the integrated ES-ANN architecture.

The diagnostic threshold for classifying a alarm pattern into a particular class is a function of over­lap between patterns belonging to different classes and the characteristics of the data set in general. In the experiments carried out by us, the threshold has varied from 0.5 in some ANNs to between 0.2 to 0.8 in some others. 5 Conclusion

In this paper we have shown how an integrated model combining ES and ANNs can be effectively applied in an alarm processing system. We have shown that how the process of decomposition into hi­erarchical and distributed heterogeneous objects can work effectively in a non-trivial real world power sta­tion. Through ES-ANN modeling, parallel process­ing, two level communication, we have tackled more closely the real time issues in alarm processing like size and scalability in power systems, data variabil­ity, real time response, multiple CB alarms, memory requirements, etc. We have also shown that the pro­cess of task decomposition and sectionalising can ob­viate the need for using ANNs in the generalization mode, thus providing reliable inference. The notion of UOSP, ES, ANN and , domain objects help us to develop a system with, among other properties increased modularity, structured representation, in­heritance, ease of maintenance and, reusability. References

[1] CIGRE International Task Force WG38-06-02, Convenor - T. Dillon, "Survey on Expert Sys­tems in Alarm Handling", Electra , No. 139, pp. 133-147, 1991.

[2] M. Munneke and T. S. Dillon, "An Object­Oriented Approach to Alarm Handling", Expert Systems Applications in Power Systems, Pren­tice Hall, London, Chapter 10, pp343-382, 1991

[3] R. Khosla and T. Dillon, "A Neuro-Expert System Approach to Power System Problems", 4th International Symposium on Expert System Applications to Power Systems, pp8-15, Mel­bourne, Australia, Jan. 1993.

[4] F. Eickhoff, E. Handschin and W. Hoffmann, "Knowledge Based Alarm Handling and Fault location in Distributed Networks", Proc. of the Conference on Power Industry Computer Appli­cations, Baltimore, pp495-498, 1991.

[5] A. G. Jongepier, H. E. Dijk and L. V. D. Sluis, "Neural Networks . Applied to Alarm Process­ing", Proc. 3rd ESAPS, Tokyo, Japan, pp615-620, April 1991

[6] Nexpert Object Manuals, Neuron Data Inc. CA,1989.