TEMPORAL NEURO-FUZZY SYSTEMS IN FAULT DIAGNOSIS AND PROGNOSIS

Transcription

1 TEMPORAL NEURO-FUZZY SYSTEMS IN FAULT DIAGNOSIS AND PROGNOSIS Mahdaoui Rafik, Mouss Hayet Leyla, Mouss Djamel, Chouhal Ouahiba Laboatoie d Automatique et Poductique (LAP) Univesité de Batna, 1, Rue Chahid Boukhlouf Batna, Algéie Cente univesitaie Khenchela Algéie, Route de Batna BP:1252, El Houia, Khenchela Algéie mehdaoui.afik@yahoo.f, chouhal_wahiba@yahoo.f, hayet_mouss@yahoo.f, moussdj@yahoo.f ABSTRACT Fault diagnosis and failue pognosis ae essential techniques in impoving the safety of many manufactuing systems. Theefoe, on-line fault detection and isolation is one of the most impotant tasks in safety-citical and intelligent contol systems. Computational intelligence techniques ae being investigated as extension of the taditional fault diagnosis methods. This pape discusses the popeties of the TSK/Mamdani appoaches and neuo-fuzzy (NF) fault diagnosis within an application study of an manufactuing systems. The key issues of finding a suitable stuctue fo detecting and isolating ten ealistic actuato faults ae descibed. Within this famewok, data-pocessing inteactive softwae of simulation baptized NEFDIAG (NEuo Fuzzy DIAGnosis) vesion 1.0 is developed. This softwae devoted pimaily to ceation, taining and test of a classification Neuo-Fuzzy system of industial pocess failues. NEFDIAG can be epesented like a special type of fuzzy pecepton, with thee layes used to classify pattens and failues. The system selected is the wokshop of SCIMAT clinke, cement factoy of Ain Touta " Batna, Algeia ". Keywods: Diagnosis; atificial neuonal netwoks; fuzzy logic; Neuo-fuzzy systems; patten ecognition; AMDEC. 1. INTRODUCTION The function of diagnosis is a vey complex task and can be only one pat solved by the technique of patten ecognition(pr), the diagnosis by PR can be pesented as an altenative solution at the model appoach since the opeating modes ae modeled, not analytical manne, but by using only one whole of measuements of this modes [8]. Theefoe the human expet in his mission of diagnosing the cause of a failue of a whole system uses quantitative o qualitative infomation. On anothe side, in spite of the esults lagely supising obtained by the ANN in monitoing and pecisely in diagnosis they emain even enough fa fom equalizing the sensoy capacities and of easoning human being. Fuzzy logic makes anothe vey effective axis in industial diagnosis. Also, can we eplace the human expet fo automating the task of diagnosis to 100%, by using the neuo-fuzzy appoach? And How made the human expet to gathe all infomation allowing him to lean its decision? Ou objective consists to make an association (adaptation) techniques of fuzzy logic with the neuonal techniques (a system Neuo-fuzzy), to choose the types of netwoks of neuon, to detemine the fuzzy ules and finally the stuctue of the system Neuo-Fuzzy to automate the maximum of the task diagnosis. In ode to achieve this goal we oganize this aticle thus. The fist pat pesents pincipal achitectues and pinciples Neuo-Fuzzy systems opeation and thei applications. The second pat is dedicated to the wokshop of clinke on the level of cement factoy. Lastly, in the thid pat we popose a Neuo-Fuzzy system fo system of poduction diagnosis. Neuo-Fuzzy systems The Neuo-fuzzy model combines, in a single famewok, both numeical and symbolic knowledge about the pocess. Automatic linguistic ule extaction is a useful aspect of NF especially when little o no pio knowledge about the pocess is available (Bown and Hais, 1994; Jang, 1995). Fo example, a NF model of a non-linea dynamical system can be identified fom the empiical data. This model can give us some insight about the onlineaity and dynamical popeties of the system. The most common NF systems ae based on two types of fuzzy models TSK (Takagi and Sugeno, 1985; Sugeno and Kang, 1988) and Mamdani (1995, 1996) combined with NN leaning algoithms. TSK models use local linea models in the consequents, which ae easie to intepet and can be used fo contol and fault diagnosis (Füssel, et al 1997; Ballé et al 1997). Mamdani models use fuzzy sets as consequents and theefoe give a moe qualitative desciption. Many Neuo-fuzzy stuctues have been successfully applied to a wide ange of applications fom industial pocesses to financial systems, because of the ease of ule base design, linguistic modeling, application to complex and 11

2 uncetain systems, inheent non-linea natue, leaning abilities, paallel pocessing and fault-toleance abilities. Howeve, successful implementation depends heavily on pio knowledge of the system and the empiical data (Ayoubi, 1995). Neuo-fuzzy netwoks by intinsic natue can handle limited numbe of inputs. When the system to be identified is complex and has lage numbe of inputs, the fuzzy ule base becomes lage. NF models usually identified fom empiical data ae not vey tanspaent. Tanspaency accounts a moe meaningful desciption of the pocess i.e less ules with appopiate membeship functions. In ANFIS (Jang, 1993, 1995) a fixed stuctue with gid patition is used. Antecedent and consequent paametes ae identified by a combination of least squaes estimate and gadient based method, called hybid leaning ule. This method is fast and easy to implement fo low dimension input spaces. It is moe pone to lose the tanspaency and the local model accuacy because of the use of eo back popagation that is a global and not locally nonlinea optimization pocedue. One possible method to ovecome this poblem can be to find the antecedents & ules sepaately e.g. clusteing and constain the antecedents, and then apply optimization. Hieachical NF netwoks can be used to ovecome the dimensionality poblem by decomposing the system into a seies of MISO and/o SISO systems called hieachical systems (Tachibana and Fuuhashi, 1994). The local ules use subsets of input spaces and ae activated by highe level ules[12]. The citeia on which to build a NF model ae based on the equiements fo faults diagnosis and the system chaacteistics. The function of the NF model in the FDI scheme is also impotant i.e. Pepocessing data, Identification (Residual geneation) o classification (Decision Making/Fault Isolation). Fo example a NF model with high appoximation capability and distubance ejection is needed fo identification so that the esiduals ae moe accuate. Wheeas in the classification stage, a NF netwok with moe tanspaency is equied. The following chaacteistics of NF models ae impotant: Appoximation/Genealisation capabilities tanspaency: Reasoning/use of pio knowledge /ules Taining Speed/ Pocessing speed Complexity Tansfomability: To be able to convet in othe foms of NF models in ode to povide diffeent levels of tanspaency and appoximation powe. Adaptive leaning Two most impotant chaacteistics ae the genealising and easoning capabilities. Depending on the application equiement, usually a compomise is made between the above two. In ode to implement this type of fuzzy peception and exploited to diagnose of dedicated poduction system we have to popose data-pocessing softwae NEFDIAG. 2. NEFDIAG PRESENTATION NEFDIAG is a data pocessing pogam of inteactive simulation, caied out within LAP (univesity of Batna) witten unde DELPHI, dedicated pimaily to ceation, the taining and the test of a Neuo-Fuzzy system of classification of the beakdowns of a dedicated industial pocess. NEFDIAG models a fuzzy classifie F with a whole of classes C = {c1, c2... cm}. Stuctue and taining NEFDIAG can be epesented like a special type of fuzzy peception, with thee layes to use to classify failues. Fig. 1. Neuo-Fuzzy Achitectue NEFDIAG makes it s taining by a set of foms, such as each fom will be affected (classified) towads one of the peset classes. then NEFDIAG geneates the fuzzy ules by a couse of the data optimizes the ules by taining the paametes of the subsets fuzzy which ae used fo patitioned the data " chaacteistic " of the foms with classified and the paametes of the data. NEFDIAG can be used to classify a new obsevation; the system can be epesented in the fom of fuzzy ules: If symptom1 is A1 Symptom2 is A2 Symptom3 is A3 Symptom N is An Then the fom (x1, x2, x3..., xn) is belonged to class «Failue mode 1». Such as A1 A2 A3 An ae linguistic tems epesented by fuzzy sets. This chaacteistic will make it possible to know the analyses on ou data, and to use this knowledge to classify them. The taining of the 12

3 netwoks of atificial Neuo-Fuzzy is a phase which makes it possible to detemine o modify the paametes of the netwok, in ode to adopt a desied behavio. The stage of taining is based on the descent of gadient of aveage quadatic eo made by netwok RNF. System NEFDIAG can stats with a base of knowledge patial of the foms, and can then efine it duing the taining, o it can stat with an empty base of knowledge. The definite use the initial numbe of the functions of membeship fo patitioning fields of the data input. And it is also necessay to specify the numbe K, a numbe maximum of the neuons of the ules which will be ceated in the hidden laye. The pincipal steps of algoithm of taining ae thus pesented. The algoithm of taining detects (calculates) all the antecedents of the ules and then ceates the list of the antecedents. In the fist time this list is empty, o contains antecedents of ules of knowledge a pioi. The algoithm selects then consequent to seek antecedent A and to ceate the basic list of ule candidates. The best ules will be selected base of the ules candidates, in base of measuement of pefomance [7]. In this case some classes (mode of failue) would not be epesented in the base of ules, if the ules fo this mode of failue to a value of vey small pefomance. Taining of the Functions of Membeship Fo the taining of the membeship functions, a simple eto popagation is used. It depends on the eo of output fo each unit of ules. Each ule changes its membeship functions by the change as of the thei suppots «fig. 7». It is necessay that the eo of each ule is calculated [5]. τ is fulfillment of a ule. Afte the appeaance of anothe new mode of failue in the phase of taining ou system is the degee Neuo-fuzzy will make an adaptation o a eoganization of the system to be adapted has the new situation «fig. 2». Fig. 2. Neuo-Fuzzy Fault detection. Initialization: fo each data esulting by sensos thee is an input unit, fo each mode of failue thee is an output unit. Fo each input unit an initial fuzzy patition is specified «exp: A numbe of the of tiangula membeship functions Taining of the ules System NEFDIAG can stats with a base of knowledge patial of foms, and can then efine it duing the taining «Fig. 3», the ule will be ceated by eseach (fo a given fom F) the combination of the functions of membeship such as each poduced enty the geatest function of membeship «fig.3». If this combination is not identical fo the ules exist in the ules base, and numbes of ules is not maximum, then ules will be ceated and added to the ules base «Fig. 1», 1 ε = τ (1 τ ) m m j= 1 ( (2ν j) ( t j ) 1) E j (1) Fig. 3. V_R_F befoe taining Fig. 4. V_R_F afte taining 13

4 Initially layes of ules (o ules bases) add all ules of the mode of failue detected. Then in the laye of the modes of failue, anothe node will be connected to the Neuo-Fuzzy netwok «fig.7» Fig. 5. The taining of membeship function 3. THE WORKSHOP OF CLINKER Ou application is illustated on an industial pocess of manufactue of cement. This installation belongs to cement factoy of Ain-Touta (SCIMAT) ALGERIA. This cement factoy have a capacity of t/an " Two funaces " is made up of seveal units which detemine the vaious phases of the manufactuing pocess of cement. The wokshop of cooking gathes two funaces whose flow clinke is of 1560 t/h. The cement cushing includes two cushes of 100t/h each one. Fowading of cement is caied out stating fom two stations, fo the tucks and anothe fo the coaches «Fig.6, ". the installation. Fo the analysis of the dysfunctions we adopted the method of analysis of the modes of failue, thei effects and thei citicality (AMDEC). While basing itself on the study caied out by [6], on the wokshop of cooking, we woked out an AMDEC by consideing only the most citical modes of the failues (citicality >10) and this fo easons of simplicity [6]. Theefoe we have a system Neuo-fuzzy of 27 inputs and fou outputs which wee ceated to make a diagnosis of ou system. The ules which ae ceated with the system ae knowledge a pioi, a pioi the base of ule. Each vaiable having an initial patition will be modified with the length of the phase of taining (a numbe of sets fuzzy fo each vaiable). The easoning fo the diagnosis is descibed in the fom of fuzzy ules inside ou Neuo-fuzzy system. The pincipal advantage of the use of the base of fuzzy ules lies in its modulaity and its facility of extension (suppession o addition of othe ules). The initial ules base to establish the diagnosis of the failues is built by exploiting the model woked out in phase s dysfunction of ou system (AMDEC). Indeed, this analysis makes it possible to establish the bonds of causes fo puposes between the components failing and the symptoms obseved. These bonds will be epesented in the foms of fuzzy ules building the knowledge base which will be taining late and then tested, to cay out the fuzzy easoning necessay and to lead to the esults expessing the function of diagnosis. Fig. 6. Alame message 4. NEURO FUZZY DIAGNOSIS 4.1 Dysfunctions analyse This step has an objective of identification the dysfunctions which can influence the mission of the system. This analysis is lagely facilitated by the ecognition of the models stuctual and functional of Fig. 7. The diagnosis by NEFDIAG. Then the detection of the anomalies is epesented in the fom of alam message intended to announce to the opeato (use) the appeaance of an anomaly (o anomalies) and makes it possible to identify the component esponsible using a base fo data which 14

5 stock all the infomation povided by the AMDEC (mode of failue, possible causes, equipment, effects on the system). Fig. 8. diagnosis mode1 Afte appeaance of an anomaly, a message of alam makes it possible to the opeato to detect the dysfunction and also to locate the esponsible component. The " fig. 2, " illustates the system of cooking with the pesence of a dysfunction. Let us note that in this study, the anomalies o dysfunctions indicate functional anomalies. Afte the posting of the message, the opeato can consult this last fo moe infomation o to emove it " fig. 6, ". Then NEFDIAG makes inteventions to contol the vaiables which ae the oigin of the cuent failue "Fig. 8, ". 5. Conclusions In this aticle we pesented a new tool fo diagnosis by Neuo-Fuzzy systems following appoaches AMDEC, we detailed the implementation of an example of industial application by the development tool NEFDIAG. We illustated use of ou tool of assistance to the pediction diagnosis in the fom of a pototype NEFDIAG to install on a PC.We appoached the vaious stages to be followed fo the development of assistance system to the diagnosis stating fom the methods of classification and fuzzy ecognitions of the foms. NEFDIAG is epesented like a special type of fuzzy pecepton, with thee layes used to classify failues, by using the neuo-fuzzy system of the type 3. NEFDIAG makes taining with two phases. A taining of ules, and geneates the fuzzy ules by the couse of data and optimizes the ules by taining of the paametes of the fuzzy sets which ae used fo patitioning the data of the foms to classify and the paametes of the data. In spite of geat impotance of fuzzy neual netwoks fo solving wide ange of eal-wold poblems, unfotunately, little pogess has been made in thei development. we have discussed ecuent neual netwoks with fuzzy weights and biases as adjustable paametes and intenal feedback loops, which allows captuing dynamic esponse of a system without using extenal feedback though delays. In this case all the nodes ae able to pocess linguistic infomation. As the main poblem egading fuzzy and ecuent fuzzy neual netwoks that limits thei application ange is the difficulty of pope adjustment of fuzzy weights and biases, we put an emphasize on the RFNN taining algoithm. REFERENCES [1] H.K. Kwan and Y.Cai, A fuzzy neual netwok and its application to patten ecognition IEEE Tansactions on Fuzzy Systems, 3 pp [2] J.-S. Roge Jang ANFIS: Adaptive-netwokbased fuzzy infeence system,ieee Tans. Syst., Man, and Cybenetics, 23(1993) [3] J.M. Kelle and H.Tahani Implementation of conjunctive and disjunctive fuzzy logic ules with neual netwoks Intenational Jounal of Appoximate Reasoning,6(1992) [4] G. Zweingelstein Diagnostic des défaillances, théoies et patique pou les systèmes industiels Col. Taité des nouvelles technologies, séies diagnostic et maintenance, Hemès,1995. [5] L.-X wang and J.M Mendel Geneation fuzzy ules by leaning fom examples IEEE tans. Syst., Man, and Cybenetics, (1992) 22(6): [6] D. Mouss Diagnostic et conduite des systèmes de poduction pa appoche a base de connaissances Thèse de doctoat Univesité de Batna, [7] D. Nauck Neuo-fuzzy systems: eview and pospects Euopean congess on intelligent technique and sift computing (EUFIT 97), Aachen, sep.8.11, (1997), [8] D. Racoceanu contibution à la suveillance des systèmes de poduction en utilisant les techniques de l intelligence atificielle [9] M. Ochad and G. Vachtsevanos, A paticle filteing-based fame-wok fo eal-time falut diagnosis and faillue pognosis in a tu-bine engine, Mediteanean Confeence on Contol and AutomationMED 07, Athens, Geece, July [10] A. Al-Ghamd and D. Mba, A compaative expeimental study onthe use of acoustic emission and vibation analysis fo beaing defectidentification and estimation of defect size,, Mechanical Systems andsignal Pocessing, vol. 20, pp , [11] B. Li, M.-Y. Chow, Y. Tipsuwan and J. Hung, Neual-netwok-based moto olling beaing fault diagnosis, IEEE Tans. IndustialElectonics, vol. 47, no. 5, pp , Oct [12] Ochad, M., A Paticle Filteing-Based Famewok fo On-Line Fault Diagnosis and 15

6 Failue Pognosis, Ph.D. Thesis. Electical and Compute Engineeing, Geogia Institute of Technology, Atlanta, [13] Ochad, M., Wu, B. and Vachtsevanos, G., A Paticle Filte Famewok fo Failue Pognosis, Poceedings of WTC2005, Wold Tibology Congess III, Washington D.C., USA, [14] Rafik Mahdaoui, Mouss Industial dynamics monitoing by Tempoals Neuo-Fuzzy systems: Application to manufactuing system GVIP