A Multi-agent Approach to a Networked Fault Detection System

Mario J.G.C. Mendes and Jose Sa da Costa

Abstract— Nowadays, fault diagnosis systems design shouldhave into account the distribution and complexity of the processand must be able to cooperate and communicate with othersystems to achieve satisfactory performance. To this end anew agent based fault detection system for networked controlprocess is proposed in this paper. A hybrid architecture basedon horizontal layers as fault detection (FD) agents architectureis adopted. The reactive layer of the FD agents is based onneural networks models for residuals generation with adaptivethreshold. The deliberative layers take into account the agentknowledge and goals, and also the process distribution andnetwork. The new agent based FD system has been appliedand tested with good results in a three tank process using astar Ethernet network topology.

I. INTRODUCTIONA networked control system (NCS), is a system whose

sensors, actuators and control units are connected throughcommunication networks (Fig. 1) having a pervasive mixeddata flow with both time-critical data (periodic variablesand events) and non-critical data (messages). Usually, NCSinvolves a hierarchy of local and global embedded con-trol/diagnostic structures that makes the overall system largeand complex.

Like any other system, NCS are subject to faults or mal-functions or simply performance deterioration of equipments,network and process, forcing to interrupt the normal opera-tion and, if not detected in early stages, frequently leadingto expensive repairs. To avoid performance deteriorations orcomponents damage and humans’ risks or, large disasters,faults have to be detected as quickly as possible and actionsthat stop the propagation of their effects have to be taken, i.e.it must have fault tolerance capabilities. These actions shouldbe carried out by appropriate fault tolerant controller equip-ment through a fault diagnosis system to detect, isolate andidentify the kind of fault and its severity, and an appropriatetuning or reconfiguration system of the controller, or of theoverall system itself to adapt to the faulty NCS condition.Thus, NCS require highly sophisticated supervisory controlsystems with distributed fault tolerance to ensure that highperformance can be achieved and maintained under adverseconditions.

Several different methods and techniques to deal with thisproblem can be found in the literature [1], [2], [3]. How-

This work was supported by FCT, through IDMEC, under LAETAand partially supported by the project PTDC/EEA-CRO/102102/2008,co-sponsored by FEDER, Programa Operacional Ciencia, Tecnologia eInovacao 2010, FCT, Portugal.

Mario J.G.C. Mendes is with IDMEC/ISEL-Instituto Superior de En-genharia de Lisboa, Rua Conselheiro Emıdio Navarro 1, 1959-007 Lisboa,Portugal mmendes@dem.isel.ipl.pt

Jose Sa da Costa is with IDMEC/IST, TULisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal sadacosta@dem.ist.utl.pt

ever, these methods are globally and centralized designedwithout attending the distributed and decentralized nature ofnetworked control systems.

On the contrary, industrial processes are becoming morecomplex, physically distributed and heterogeneous, imposinga modular (physical or functional modules) and decentralisedpoint of view. Thus, there exist a pressing need for distributedfault tolerant control methods. A good option makes useof the Distributed Artificial Intelligence (DAI) techniques.Namely, the methodologies based on the multi-agent systems(MAS) are a good choice to create a distributed, modular andcollaborative tolerant control scheme for industrial processes.This paper deals with the first part of fault tolerant control(FTC) systems, the fault detection (FD) task. This taskconcerns with the early detection of faults presence in theprocess and network. Nowadays, the most common FDmethods used are quantitative and qualitative model-basedmethods.

The paper is organised as follows. Section II addresses theadopted agent based fault detection architecture. Section IIIdescribes the proposed micro-level architecture of the FDagents. Section IV describe the benchmark used for testbed and presents the performance results obtained with the

Fig. 1. Networked FD agents - Types of reactive layers.

2010 Conference on Control and Fault Tolerant SystemsNice, France, October 6-8, 2010

FrB3.5

978-1-4244-8154-5/10/$26.00 ©2010 IEEE 916

Fig. 2. FTNC multi-agent system.

proposed FD approach. Finally in section V conclusion aredrawn.

II. FAULT DETECTION SYSTEM ARCHITECTURE

The fault detection approach for NCS systems presented inthis paper is based on agents modelling theory. Here an agentis a computer system that is located in some environment andis capable of autonomous actions in that environment in orderto meet its design objectives [4]. The FD agents, proposedin this paper, are part of a multi-agent architecture for FaultTolerant Networked Control Systems (FTNCS), that has beenproposed by Mendes et al. and it has been implemented usinga platform [5] and a toolkit for Matlab/Simulinkr [6].

Previous studies on MAS architecture [7] point out themodified federate architecture as a good solution. However,from further research recently emerged a new architecturefor FTNCS design (Fig. 2), where the facilitators deal withthe communication between the different FD agents and withthe fault information to the supervision system agent. In thisapproach the agents are simpler, they have task separationand they can communicate directly with other agents fromthe same cluster. The FD facilitators are also responsible forreceiving the process data and for the fault decision at thedifferent level of the FTNCS. Usually complex processes aredivided in more simple partitions (Fig. 2). To each of thispartitions agents are assigned to perform assigned tasks asmonitoring and/or acting on that specific partition [8].

It is assumed here that the FTC platform, includingMAS architecture and communication infrastructure are alldesigned with the FTNCS-MAS Designer Toolbox [5].

Figure 3 shows a representation of the several doubts andspecifications that are important to define when one needto construct a multi-agent system. It is very important todefine how many agents will communicate with the FDAgents (how many inputs will be active) and it is needed todefine how many values (variables) this agent sends. Usingthe FTNCS-MAS Designer Toolbox [5], the agent moduleis used to design the internal agent architecture, the Micro-Level architecture of the Agent.

III. FD AGENT ARCHITECTURE

After the definition and construction of the multi-agentinfrastructure for FTNCS purpose, the designer attentionshould be directed to the micro-level architecture of theFD agents. These agents should be capable of reactive andproactive behaviour. Also, since these agents are distributedand connected through a network, the natural architecture forthe FD agents is an arrangement by layer, in a horizontal orvertical format. This layered approach should have at leasttwo layers, one for reactive and other for the proactive ordeliberation behaviour. A great advantage of the horizontallayer architecture is the conceptual simplicity, where if anagent needs several different types of behaviour, then itis only necessary to implement the corresponding layers.However, the different layers can compete and, then, tocontrol the agent behaviour a mechanism of control is needed[4]. In the case of this work this problem is solved by giventhe final decision to the deliberation layer.

Figure 4 shows the proposed architecture for the FDagents, for controlling and coordinating the actions of theagents located in dynamic and complex processes. This paperis dedicated to the micro-level architecture, or more specifi-cally, to the reactive layer based in NNARX models. In fact,it is known that the neural networks are also considered anagent architecture, the so called Connectionist architecture.

The proposed approach has some advantages, the agentsare simpler and they are divided by different FD strategies.It is possible to have fully competition and also coordinationbetween the agents to achieve the goal of detect correctly thefaults. It permits a process complexity reduction by applyingseveral agents to the different process partition parts. The FDagents have decision capabilities about the faults. They areable to change the results of the reaction layer taking otheragent results as a base for that decisions.

The reactive layer of the agents are responsible for the first

Fig. 3. Agent block possible configurations.

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Fig. 4. Micro-level agent architecture - The FD A2 example.

reaction to the possible faulty data transmitted by the agentperception. The reactive layer presented in this paper usesa Neural Network (NN) model for residual generation andan adaptive threshold to detect the presence of faults on theprocess. The NNs use the past faulty and faulty free data ofthe process to learn a model of the different partitions, notneeding a mathematical model of the process. The NNs havealso a good generalization capabilities reducing the need ofa huge amount of data for every process setpoint [7].

A. FD reactive layer based in NNARX models

The FD agent reactive layer is based on NNARX modelsfor residual generation. The difference between the proposedapproach and the most common techniques of fault detectionbased on neural network regards the use of partial processand network observers performed by agents distributed overthe process/network with the capability to cooperate toenhance its performance [7].

In this work, the best results have been achieved withthe neural network auto-regressive with exogenous inputs(NNARX) models based on multi-layer perceptrons (MLP)without global output feedback. The MLP networks arenormally employed for discrete-time modelling of dynamicprocesses existing a nonlinear relationship between the pro-cess input and output. The NNARX nonlinear models, ex-pressed by eq. 1, assume that the dynamic process outputcan be described, at a discrete time instances, by a nonlineardifference equation, depending from the past inputs andoutputs:

y(t) = f(y(t−1), . . . , y(t−n), u(t−1), . . . , u(t−m)) (1)

where n and m are the output and input signal delays,respectively, and f is a nonlinear function (the MLP acti-vation function). Basically, the MLP networks are used toapproximate the f function if the inputs of the network(ui(t)) are chosen as the n past outputs and the m past inputs:

yi(t) = Fi

Nh∑j=1

vijfi

n+m∑k=1

wjkxk(t) + bj0

+ bi0

, i = 1, . . . ,M (2)

where Nh is the number of hidden layer neurons (Nh = 5 inFig. 4), wjk are the first-layer interconnection weights (the

first subscript index referring to the neuron, and the second tothe input), vij are the second-layer interconnection weights,bj0 and bi0 are the threshold offsets (biases), and Fi and fiare the activation functions of the output neurons and hiddenneurons, respectively. So, the modelling process is to find theneural network that fits this ARX model equation. To obtainthe NNARX models some steps have been made, first thedata collection from the process that are representative of agreat part of the process working points, second the modelstructure selection, third the model estimation and fourth themodel validation [9], [10].

Figure 4 (the reactive layer) shows the NNARX modelused to model one partition (1st) of the three tank process[8]. The z−1 is the backward time-shift operator, i.e. x(k −1) = z−1x(k), yi are the NN outputs and xk are the NNinputs. Then, for the three tank process, it has been chosenan architecture, based on a two layer feedforward MLPnetwork, with one input and two outputs for the first partition,and one input and three outputs for the second partition.For the activation functions it has been chosen a nonlinearHyperbolic tangent sigmoid function (eq. 3), this function isa nonlinear, continua and differentiable function, as neededfor backpropagation training algorithms, for the hidden layerand linear functions for the output layer (eq. 4).

Hyperbolic tangent: ζ = tanh

(b+

n∑k=1

wkxk

)(3)

Linear: ζ = b+

n∑k=1

wkxk (4)

In the case of NNARX models, an important aspect in themodel construction is the lag space, i.e., the number ofdelayed signals used as regressors. The wrong choice of lagspace may have a disastrous impact in some models andcontrol applications. A lag space too small implies that theessential dynamic of the system will not be modeled and alag space too large can also be a problem because it canmanifest itself as common factors (hidden modes) on theidentified model [9]. The lag space, in this work, has beenchosen using the Lipschitz quotient:

qij =

∣∣∣∣ y(ti)− y(tj)

φ(ti)− φ(tj)

∣∣∣∣ , i = j, (5)

This quotient is for all combinations of input-output pairs,the || stands for the Euclidean norm. The Lipschitz conditionthen states that qij is always bounded, if the function f0(function realized by the neural network, assuming feedfor-ward structure) is continuous between 0 ≤ ij ≤ L. TheLipschitz quotient is derived based on the assumption thatthe system can be modeled by a noise-free NNARX model,where the prediction outputs are:

y(t) = f0 [φ(t), θ] (6)

where φ(t) is the NNARX regression vector

φT (t) = [φ1, φ2, ..., φz]] (7)

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and θ is the NNARX parameters vector (weights). Then,considering a data set consisting of N input-output pairs ofthe type:

ZN = {[φ(t), y(t)] , t = 1, ...N} (8)

and assuming that the magnitude of the system derivativewith respect to each of the regressors is bounded by somepositive value, B,

|fl| =∣∣∣∣∂f0∂φl

∣∣∣∣ ≤ B l = 1, 2, ..., z (9)

the Lipschitz quotient, expressed by eq. 5, is introduced andalways bounded for all combinations of input-output pairs[9].

The determination of the number of hidden neurons hasbeen made by an iterative process. Based on the MSE (Meansquare error) of the training process, the number of neuronsin the hidden layer was iteratively reduced to maintain themodel performance and to be the less possible to ensuregeneralization capabilities and avoid overfitting. The algo-rithm that has been chosen for train the NNARX networksmodels has been the Levenberg-Marquardt algorithm [11]because is the fastest algorithm, and with better results, onfunction approximation problems. In general, on functionapproximation problems, for networks containing up to afew hundred weights, the Levenberg-Marquardt algorithmwill have the fastest convergence (but, on the other hand,it needs much more memory).

The FD reactive layers based on NNARX models generateresiduals (the comparison result between the process signalsand the NNARX models outputs), which are associatedwith thresholds. This information is provided to the agentdeliberation layer to produce a fault detection decision. TheFD agents should decide if faults are detected or not, andshare that information with the other FD agents.

B. Deliberation Layer of the agents

The agent deliberation layer (see Fig. 4) will be responsi-ble for keeping the agent plans and deliberations about theresponse to the FD situation, what to do in case of fault(s),do nothing, inform or “ask for help” to another agent. Thislayer should be also responsible for the management ofthe communication between the agents. It is also this layerthat should be responsible for the coordination/cooperationbetween agents of the same task.

The coordination in the agents is achieved through coor-dination planning using a commitment rules. To create thebase of heuristic rules, a production system (or rule-basedsystem) architecture was used. Although there is a widevariety of syntaxes for the definition of production rules,they are generally given in the following form:

if (list of conditions) then (list of actions)

When a rule can validate each of the conditions on its list,it executes the corresponding actions.

IV. VALIDATION

The benchmark used to test the proposed FD system iscomposed of the AMIRA DTS200 three tank process [12]and four hosts connected through an Ethernet network instar topology (Fig. 5) [6]. The AMIRA three tank processconsists of three plexiglas cylinders T1, T2 and T3 with crosssection A. They are connected serially with each other bypipes with cross section Sn. Located at T3 is the outflowvalve. It also has a circular cross section Sn. The outflowingfluid (usually distilled water) is collected in a reservoir T0,which supplies the pumps B1 and B2. Hmax denotes thehighest possible liquid level. In case the liquid level of T1or T2 exceeds this value the corresponding pump will beswitched off automatically. Q1 and Q2 are the flow rates ofthe pumps B1 and B2. At the present research work, the ninefaults that have been considered in the AMIRA benchmarkwere: F1 - Leak in tank T1, F2 - Clog in branch with VL1,F3 - Clog in branch with VL2, F4 - Leak in tank T2, F5 - B1pump fault, F6 - B2 pump fault, F7 - h1 level sensor fault,F8 - h2 level sensor fault and F9 - h3 level sensor fault.

The fault detection results achieved with the FD A2 agent(Fig. 4) and with the FD A4 agent.

To model the reactive layer of these agents, it has beenused training data representing different operation points, ina total of 18000 points of train data. This data has not beenfiltered but has been scaled between [0,1] (the outputs) andbetween [-1,1] (the inputs). This data normalisation generallyimproves the models and the training algorithm has a fasterconvergence.

As it can be seen from Fig. 4, to model one processpartition, it has been chosen the model architecture, a twolayer feedforward MLP network, with one input, the supplyvoltage of pump B1 (U1) and two outputs, the levels of thetanks T1 and T3 (levels h1 and h3). The model of the FDA2 agent reactive layer is a SIMO (single input multipleoutput) model. These have been the chosen variables basedon the graph partition [8], which demonstrate the relationshipbetween these variables and their dependency.

The lag space has been chosen using the Lipschitz quo-tient. Thus, using this quotient it has been observed that a

Fig. 5. Multi-agent platform with 4 hosts and respective agents assignment.

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Fig. 6. FD A2 NNARX model outputs with train data.

lag space equal to two is sufficient to described the modeldynamic. The number of input neurons is six, one inputand two outputs with two regressions each (see Fig. 4) andthe number of hidden neurons (five) has been made by aniterative process.

The FD A2 and FD A4 agents also have a deliberationlayer. These agents always actualise the decision state of theother agents and/or other information about the environmentthat is needed for fault detection purpose. This layer has alsoa fault database (objectives of the agent), which representthe capabilities of each agent. The deliberation layer decideswith this knowledge and with simple if-then rules that takeinto account the reactive layer decision and then the agentachieve the final decision and communicate that to the otheragents and facilitator.

The next figures presents the FD A2 model results (Fig. 6)and residuals (Fig. 7) with the training data.

Fig. 7. FD A2 residuals with train data.

Observing Fig. 7, it can be concluded that the FD A2model as a good accuracy for h1 and h3 residuals, butin the case of h3 residuum, at the time of 800 s, this

residuum increase a little bit, typically due to noise in thelevel variable.

Observing Table I for test data, it can be notice thatthe FD A2 model as a good accuracy for level output h3,but in the case of output h1 the residuum is quite worst(VAF = 98, 10% and RMS = 0, 0282). This accuracyproblem with the NNARX model output h1 can be solvedtraining the model some epochs more or incorporating somepart of this test data as train data. In the case of level outputh3 residuum the same problem exists with noise appearance.

In order to measure modeling accuracy of the NNARXmodels, this work used the Variance Accounted For (VAF)and the Root Mean Square (RMS) Error. The VAF is aperformance parameter computed as

V AF =

[1− σ2(y − y)

σ2(y)

]· 100% (10)

where y is the vector of expected outputs (collecting suc-cessive time-varying values of y) and y the vector of NN’soutputs; and the RMS is another performance parametercomputed as

RMS =

√√√√√√p∑

k=1

(yk − yk)2

p(11)

where p is the number of points (18000 in this case).Table I presents these results, for the FD A2 and FD

A4 NNARX models with the training and test data. Thepresented performance values have been obtained for alloutput variables, h1, h2 and h3. Looking to the VAF andRMS performance values, in general the results present agood accuracy for FD A2 model.

In general, the FD agents reactive layers presented in thissection, show good performance results. But, the results pre-sented can also be improved if the noise in the measurementswas treated, specially the noise in the input variable (U1).This problem can be solved using the multi-scale variablesinformation for the models design, namely the approximationdecomposition, which is a filtered signal.

Figure 8 presents an example of the FD A2 results usingNNARX models and the residuals with the correspondingadaptive wavelet threshold and limit checking.

All the simulated faults can be considered incipient faults.When the faults are simulated with the automatic valve mode(which has been the case), the proportional valves take about8 s to be fully open and because of that the fault symptoms

TABLE IVAF AND RMS OBTAINED WITH THE NNARX MODELS.

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Fig. 8. FD A2 - FD example of F1, F2, F5 and F7, with NNARX residuals.

are delayed. This means that the detection times could bebetter (faults detected sooner) if the manual valve mode wasused, achieving a faster fault simulation and a faster faultysymptoms appearance.

Using this fault detection technique (residuals generationwith NNARX models), times to times, also some false alarmsappears. This is due to the variables measurement noiseand changes in the level setpoints. Some adjustments in thedeliberation layer rules are needed and should be sufficientto eliminate the majority of false alarms.

For the example of Fig. 8, in the agent FD A2, thethreshold constant (Θ) used was: Θ = 2.7 to the adaptivethreshold of level residuals rh1(t) and also Θ = 2.7 to theadaptive threshold of level residuals rh3(t). These valuesrevealed a good trade-off between the prevention of falsealarms and the detection of true fault symptoms in propertime [7].

In Table II, the “x” means “detectable” and the “-” means“not detectable”. In the FD results table presented, for a ξ= 100 % and for h1 = 0.8 and h2 = 0.5 (these valuesare normalised between [0,1] and Hmax = 600mm), allthe faults are detectable by the agents except the fault F4,F6 and F8 which are not detectable by the agents FD A1and FD A2 because they are agent of the first partitionand they have only a partial access to the process variables.In this case, these agents don’t have access to the variableh2 and the effects/faults propagation in the process are notsufficient to turn these faults detectable. The detection timeshave been achieved with the supervisor, at Host H4. Theresults of Table II could be improved, namely the detectiontimes, since they have been achieved with automatic faultssimulation (using the valve controller), which turn incipientthe symptoms of F1 to F4 faults. If the faults simulation ischanged to manual, then the symptoms will be more abruptand the faults are detected sooner.

TABLE IIFAULTS DETECTABILITY AND DETECTION TIMES (ξ = 100 %).

V. CONCLUSIONS

This paper proposed a new approach to fault detectionsystems using FD hybrid agents based on Neural Networkswith adaptive thresholds, where the concurrency and com-munication between different kinds of FD agents is possible.Validation results show that the use of this type of FD systemintegrated within a multi-agent is a good option to constructand integrate fault tolerant networked control systems due tothe complexity and distributed layout of the modern industry,and also the need of integration of the different processcomponents, managing and supervision systems.

REFERENCES

[1] J. Korbicz, J. M. Koscielny, Z. Kowalczuk, and W. Cholewa, FaultDiagnosis, Models, Artificial Intelligence, Applications, 1st ed. Berlin,Germany: Springer, 2004.

[2] R. Isermann, Fault-Diagnosis Systems, An Introduction from FaultDetection to Fault Tolerance. Berlin, Germany: Springer, 2006.

[3] Z. Mao, B. Jiang, and S. X. Ding, “A fault-tolerant control frameworkfor a class of non-linear networked control systems,” InternationalJournal of Systems Science, vol. 40, no. 5, pp. 449–460, May 2009.

[4] M. Wooldridge, An Introduction to Multiagent Systems. Chichester,England: John Wiley & Sons, LTD, February 2002.

[5] M. J. G. C. Mendes, B. M. S. Santos, and J. Sa da Costa, “Multi-agentplatform and toolbox for fault tolerant networked control systems,”Journal of Computers, vol. 4, no. 4, pp. 303–310, April 2009.

[6] ——, “A matlab/simulink multi-agent toolkit for distributed networkedfault tolerant control systems,” in 7th IFAC symposium on faultdetection, supervision and safety of technical processes (SAFEPRO-CESS’2009), Barcelona, Spain, June 2009, pp. 1073–1078.

[7] M. J. G. C. Mendes, “Multi-agent approach to fault tolerant controlsystems,” Ph.D. dissertation, Instituto Superior Tecnico, TechnicalUniversity of Lisbon, Lisbon, Portugal, 2008.

[8] J. Sa da Costa and M. J. G. C. Mendes, “Design of distributed faulttolerant control systems,” in Proceedings of the 17th IFAC worldcongress on Automatic Control (IFAC’08). Seoul, Korea: InternationalFederation of Automatic Control, July 6-11 2008, pp. 13 575–13 580.

[9] M. Norgaard, O. Ravn, N. Poulsen, and L. Hansen, Neural Networksfor Modelling and Control of Dynamic Systems. London, UK:Springer, 2000.

[10] D. P. Mandic and J. A. Chambers, Recurrent Neural Networks for Pre-diction - learning algorithms, architectures and stability. Chichester,England: John Wiley & Sons, LTD, 2001.

[11] D. Marquardt, “An algorithm for least squares estimation of nonlinearparameters,” SIAM J. Appl. Math, vol. 11, pp. 431–441, 1963.

[12] AMIRA, AMIRA DTS200 Laboratory Setup Three - Tank - System,AMIRA GmbH, Duisburg, Germany, May 2002.

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