ENGINE AIR PATH FAULT DIAGNOSIS USING ADAPTIVE NEURAL CLASSIFIER. M. S. Sangha, D. L. Yu, J. B. Gomm

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1 ENGINE AIR PATH FAULT DIAGNOSIS USING ADAPTIVE NEURAL CLASSIFIER M. S. Sangha, D. L. Yu, J. B. Gomm Control Systems Research Group, School of Engneerng, Lverpool John Moores Unversty, Byrom Street, Lverpool, L3 3AF, UK Emal: Abstract: Ths paper presents a new method for on-board fault dagnoss for the ar-path of spark gnton (SI) engnes. The method uses an adaptve radal bass functon (RBF) neural network to classfy pre-defned possble faults from engne measurements to report the type and sze of the fault. The RBF fault classfer adapts ts wdths and weghts to model the tme-varyng dynamcs of the engne and dsturbance so that the false alarm rate s greatly reduced. The developed scheme s assessed wth varous faults smulated to a benchmark model and promsng results are obtaned. Copyrght 6 USTARTH Keywords: On-board fault dagnoss, automotve engnes, adaptve neural networks, adaptve fault classfcaton. NOMENCLATURE t n m T a p T m f at T EGR m ap tme (sec) throttle plate angle (degrees) engne speed (rpm/) engne port fuel mass flow (kg/sec) ambent temperature (Kelvn) absolute manfold pressure (bar) ntake manfold temperature (Kelvn) ar mass flow past throttle plate (kg/sec) EGR temperature (Kelvn) ar mass flow nto ntake port (kg/sec) m EGR EGR mass flow (kg/sec) V manfold + port passage volume (m 3 ) R gas constant (here 87 X -5 ) rato of specfc heats =.4 for ar I crank shaft load nerta (kg m ) P f frcton power (kw) P b load power (kw) P p pumpng power (kw) H u fuel lower heatng valve (kj/kg) necton torque delay tme (sec) d. INTRODUCTION Fault detecton, solaton and accommodaton have become one of the most mportant aspects of automoble desgn. Contnuous efforts are beng made to mprove the desgn of the Electronc Control Unt (ECU) n order to ensure early detecton and solaton of engne faults, whch can be hazardous for human lfe or lead to ncreased ar polluton or adversely affect the fuel effcency. OBD-II requres contnuous montorng and fault detecton capablty for all vehcle components whose falures can result n emsson levels beyond.5 tmes of the Federal Test Procedure (FTP) standards. All vehcles sold n the UK after December 3st are requred by legslaton to allow the European On Board Dagnostc (EOBD) protocol. A number of model-based fault detecton and solaton (FDI) technques (Frank et al. 5) for automotve engnes have been prevously nvestgated. Artfcal neural networks (ANN) are well known for good classfcaton propertes. Most ANN methods use a

2 fxed parameter network as the fault classfer. When the data s collected from the real engne to tran the network, the classfer s able to cope wth the modelplant msmatch caused by system complexty and nonlnearty, compared wth the model-based methods. However, after the classfer s put n real use for a perod of tme, engne dynamcs change caused by mechancal ware of the parts, etc. and envronment change wll degrade the FDI performance of the classfer. In ths paper, a new on-lne FDI scheme s proposed for engnes usng an adaptve neural network classfer. It has the followng three features: (a) usng the strong non-lnear mappng (classfyng) ablty of the ANN to cope wth the multvarable, severe non-lnearty of engne dynamcs; (b) the classfer s made adaptve to cope wth the sgnfcant parameter uncertanty, dsturbance and envronment change; and (c) on-lne fault dagnoss whch can be drectly mplemented n an on-board dagnoss system (hardware). Durng operaton, the network classfer learns parameter changes n the engne due to agng or envronment change. It can also adapt to engne-to-engne dfferences wthn a batch of products. Gaussan radal bass functon (RBF) neural nets are used for ths purpose and both weghts and wdths are on-lne adapted. Every sample of engne data s frst tested for a fault and then used to update the neural network. The mean value engne model (MVEM) (Hendrcks et al. ) s used for smulatons. Two component and two sensor faults were smulated on the MVEM. All the four faults are consdered wth four dfferent levels of ntenstes. All the smulated faults are detected and solated clearly wth zero false alarm rates and ther ampltudes are also reported. The robustness of ths algorthm s demonstrated wth smulaton results.. ENGINE DYNAMICS AND FAULTS. Engne Dynamcs To nvestgate the feasblty of the adaptve neural network model based fault dagnoss system for SI engnes, engne smulatons are used nstead of a real engne test bed. The engne smulaton model used here s based on the MVEM, a well-known and wdely used benchmark for engne modellng, control and fault dagnoss. It conssts of three sub-models that descrbe the ntake manfold dynamcs ncludng ar mass flow, pressure and temperature and the crankshaft speed... Manfold Fllng Dynamcs T h r o ttle P la te T e m p e ra tu re S e n s o r P r e s s u r e S e n s o r In le t M a n fo ld L e a k a g e E G R V a lv e C y ln d e r V a lv e s C y ln d e r P s to n E x h a u s t Fg. The ar ntake and exhaust system of the engne The engne ar path s schematcally llustrated n Fg.. Its dynamcs are brefly presented as follows and the physcal parameters are defned n the Nomenclature. Hendrcks, et al. (), gves the detals of the dynamcs. The manfold fllng dynamcs n realty s based on an adabatc operaton rather than sothermal. The manfold pressure can be represented by equaton (). R p m apt m atta m EGRTEGR () V The postve terms wthn brackets show the n-flow of gas and the negatve term shows the outflow of gas from the ntake manfold (see Fg. ). Usng the law of energy conservaton a state equaton that descrbes the tme development of the ntake manfold temperature can be gven as, RT m ap ( ) T m at ( Ta T ) T () pv m EGR ( TEGR T ).. Crank Shaft Speed Dynamcs Applyng the law of conservaton of rotatonal energy, the crankshaft dynamcs of an SI engne MVEM are descrbed by equaton (3). n Pf ( p, n) ( Pp ( p, n) Pb ( n) In (3) Hu ( p, n, ) m f ( t d ) In I s the scaled moment of nerta of the engne and ts load and the mean necton/torque tme delay has been taken nto account wth varable.. Fault Smulaton Four faults wth four dfferent fault ntenstes are consdered for ths research. Leakage n the ntake manfold and EGR valve stuck up n dfferent postons are smulated n the Smulnk model as two component faults. The malfuncton of manfold temperature and pressure sensors are smulated as two sensor faults. All the faults consdered are realstc and have been consdered by prevous authors Nyberg and Stuttle, (4) and Caprglone, et al. (3). Detals of the smulaton of these faults are descrbed as follows... No Fault For no fault stuaton, EGR s assumed to be /6 (6.67%) of the total ar mass flow n the ntake manfold. Practcally EGR n a car can be as hgh as % of the total ar mass flow. It s also assumed that all the sensors are workng well and there s no leakage n the ntake manfold. The no fault data s collected for dfferent throttle angle nputs rangng from to 4 degrees (the dlng throttle angle for the engne s 5 degrees) for dfferent operatng ponts regardng load and speed... Ar Leakage Fault To collect the engne data subected to the ar leakage fault, equaton () s modfed to d

3 R P m apt m atta m EGRTEGR l (4) V l s used to smulate the leakage from the ar manfold, whch s subtracted to ncrease the ar outflow from the ntake manfold. The ar leakage levels are smulated as 5%, %, 5% and % of the total ar ntake n the ntake manfold, respectvely...3 EGR Valve Faults The normal value of EGR s about 6.67% of the total ar mass flow and a realstc value of EGR feedback s chosen for the experments. The value of m EGR for dfferent fault ntenstes s regulated as %, 5%, 5%, 75% and % of the total EGR ar mass flow. Where % EGR ar mass flow corresponds to the EGR valve stuck up completely and % corresponds to full EGR ar mass flow,.e. no fault condton...4 Temperature/Pressure Sensor Faults Temperature and pressure sensor faults are consdered n four dfferent ntenstes: Sensors over-readng % or % and sensors under-readng % or % of the normal value. The faulty data for the sensors s generated usng multplyng factors (MFs) of.,.,.9 and.8 for the above over- or under-readng respectvely. Faulty data are generated by the Modfed MVEM wth throttle angle at dfferent values between o and 4 o for all the fault condtons and no fault condton. The 7 states (4 faults * 4 ntenstes + no-fault = 7) wth ther multplyng factors (MFs) are gven n table. The sample tme s chosen as. sec. The engne data for the smulated faults and no fault condton covers almost all transent states of the engne dynamcs. Table : The 7 fault and no fault states and multplyng factors No Fault Name MF No Fault Leakage 5% 3 Leakage % 4 Leakage 5% 5 Leakage % 6 EGR stuck 5% closed 7 EGR stuck 5% closed 8 EGR stuck 75% closed 9 EGR stuck % closed Temp. sensor % over readng MF=. Temp. sensor % over readng MF=. Temp. sensor % under readng MF=.9 3 Temp. sensor % under readng MF=.8 4 Pressure sensor % over read MF=. 5 Pressure sensor % over read MF=. 6 Pressure sensor % under read MF=.9 7 Pressure sensor % under read MF=.8 3. RBF NETWORK CLASSIFIER A Gaussan RBF network s chosen n the research for the fault classfcaton. There are several tranng 3 algorthms. Those used for ntal off-lne tranng and on-lne updatng of the RBF classfer s presented. 3. The RBF Network Structure The network conssts of three layers; nput, hdden and output layer. The nput layer smply receves the x network nput vector, and passes the nputs to each node n the hdden layer. The hdden layer conssts of n h nodes that process the nput vector. The th node n the hdden layer contans an ndvdual centre vector c of the same dmenson as x and a scalar wdth. The Eucldean dstance between the nput and the centre vectors s calculated, z d ( x c)... ( xn cn ) x c (5),..., nh, and passed through a non-lnear bass functon to produce the hdden node outputs. Several choces of bass functon are avalable, e.g. thn plate splne, Gaussan functon, etc. Gaussan bass functons provde a local exctaton of the node wth an output near zero for nputs far from the centre and near one for nputs close to the centre. Ths s especally sutable for classfcaton applcatons and s therefore used n ths work. The Gaussan bass functon s defned as exp, z (6) Fnally the network outputs are computed as a lnear weghted sum of the hdden node outputs: y W (7) qn q h y s the output vector, W s the output layer weght matrx wth element w connectng the th hdden node to the th output, and s a vector contanng the hdden node outputs. 3. Tranng Algorthms Tranng an RBF network means optmsng the parameters of centres, wdths and weghts n the network. For off-lne tranng, the K-means, the P- nearest neghbours and the batch least squares (BLS) algorthms are used. When the classfer s used on-lne, the centres reman fxed, as they have been chosen dstrbuted n the whole operatng space, whle the wdths and weghts are adapted to mnmse the classfcaton error caused by any tme-varyng dynamcs and model uncertanty. The wdths are adapted usng a gradent descent algorthm and the weghts are adapted usng the recursve Least Squares (RLS) algorthm. These algorthms are revewed or derved below. (a) K-means Algorthm The centres are set by the K-means clusterng method whose obectve s to mnmse the sum squared dstances from each nput data to ts closest centre so that the data s adequately covered by the actvaton functons (t). The K-means clusterng method s documented n Chen, et al. (99).

4 (b) Gradent Descent method for the wdths When the RBF network s used to model a non-lnear mappng or a dynamc system, the wdth n each hdden layer node s usually chosen as a constant usng the P- nearest rule, or all wdths are ust chosen equal to the same value, as t s beleved that the modellng error s not senstve to the wdth. However, when the RBF network s used as a classfer, the classfcaton s strongly senstve to the Gaussan local functon, whch s manly charactersed by the wdth. Therefore, a gradent descent algorthm s derved to on-lne adapt the wdths to acheve a mnmal obectve functon gven as follows. q e J ˆ e y y s the th classfer output error and y s the th tranng target. Then, accordng to the gradent method, the th wdth can be adapted as J ( k ),,, nh (9) s a learnng factor and < <. The gradent can be easly derved from equatons (5)-(7) and (8), p J J e yˆ... e yˆ () and the four partal dervatves are derved and substtuted nto () gvng, J 4 3 x c p e w Puttng ths back n equaton (9), we have (8) x( k) c ( k ) 4.. e w 3 () Ths tranng algorthm can update the wdth parameter to mnmse the sum of squared error defned n (8). (c) Recursve Least Squares Algorthm Ths algorthm s wdely used for off-lne tranng. If the RBF network has d nputs, q outputs and n h hdden N q nodes, the output matrx wth N samples ( Yˆ ) can be wrtten as Y ˆ ( X ) W N d X s the nput matrx, q N nh ( X ) s the nh q matrx of actvaton functon outputs and W s the matrx of weghts. The RLS method s used for on-lne tranng. It helps to construct on-lne system models that should be more accurate and flexble than off-lne fxed models. The RLS algorthm s gven by Lung (999). 3.3 RBF Network Classfer The RBF network, as the fault classfer wll receve all possble and relevant sgnals contanng fault nformaton, and has 7 outputs wth each ndcatng one of the nvestgated states n Table. The nformaton flow for the fault dagnoss s llustrated n Fg.. 4 Throttle angle nput Component Faults Data Condtonng MVEM Nose Neural Network Manfold Pressure Fg. Informaton flow of the fault dagnoss Manfold Temperature Crankshaft Speed Fault Dagnoss Accordng to the engne ar path dynamcs, four varables are chosen as the network nputs: the throttle angle, the manfold pressure, the manfold temperature and the crankshaft speed as shown n Fg.. Two levels, and, are used as the output targets of the classfer. Thus, the target matrx s a unty dagonal matrx of dmenson 7 (when there s one tranng pattern for each fault) wth each column beng used as the classfer-tranng target vector. A successfully traned network wll therefore dagnose the fault ntensty as well as the fault type. 4. ON-LINE RBF CLASSIFIER ADAPTATION The man contrbuton of ths paper s that when the fault classfer dagnoses faults on-board, the classfer s adapted on-lne so that the model-plant msmatch, parameter uncertanty and especally the tme varyng dynamcs caused by mechancal wear of components and envronment change can be modelled. In ths way the classfcaton error and consequently the false alarm rate can be greatly reduced. In fact, these effects are man drawbacks for the fxed parameter neural network to be used practcally. The fault classfcaton and on-lne adaptaton are mplemented as follows. Frstly, the measurements are read nto the electronc control unt (ECU). Then, the data s fed nto the classfer to dagnose faults. After ths, the target wll be modfed accordng to whether a fault or several faults are detected. If a fault s detected the correspondng output of the target vector wll be changed from to. Then the measurements and the modfed target are used to update the classfer. In the adaptaton, the wdth n each hdden node s adapted usng the gradent descent algorthm n () and the centre locaton s remaned fxed as prevously descrbed. Ths s followed by adaptaton of the weghts usng the RLS algorthm. NF. 6

5 5. SIMULATION & RESULTS 5. Data Collecton & Normalsaton The smulaton s run for dfferent throttle angle nputs, o, 6 o, 3 o, 34 o and 38 o for no fault condton and data for manfold pressure, temperature and crankshaft speed s collected. In the same way the data for each fault condton lsted n table s collected for all the dfferent throttle angle nputs. The smulaton of dfferent faults has already been explaned n Secton. A nose of normal dstrbuton wth zero mean and unty varance s then added to the collected data. The ampltude of the nose s set to about % of the average of the sgnal ampltude to smulate the measurement nose expected n a real engne system. The maxmum nose ampltude consdered s about 5%. To cover dfferent engne workng states, a number of data sets are collected for dfferent ntal and fnal values of the throttle angle as shown n Table. Table : Detals of data sets collected for tranng and testng of RBF networks Intal Throttle Fnal Throttle Angle Angle Constant Acceleraton Retardaton Speed 6, 3, 34, , 3, 34, , 38, , 4, 6, , 4, 6, 3 Table can be read n the way that for example, the frst lne has 5 data sets, one s wth constant throttle angle and the other four wth the throttle angle ncreased from to 6, 3, 34 and 38 respectvely. Every data set contaned 34 samples, n each of whch samples were collected for no fault condton and for each fault condton. Three engne-operatng modes when a fault occurs whle drvng a car on the road are consdered: (a) Car s runnng at a constant speed and a fault occurs (b) Car s acceleratng and a fault occurs (c) Car s deceleratng and a fault occurs Total 7 dfferent sets of such data are collected to ensure that all the possble combnatons of engne operaton, for dfferent ntal and fnal throttle angle values are taken nto account, for the experment wthn a partcular range of engne operaton. Intal tranng has been done before the on-lne fault dagnoss wth adaptve tranng. The data s normalsed by subtractng the steady state values and then scaled to the range of [ ], x nor x x x, x, are measurements, ther mean and standard devaton respectvely. By normalsaton there 5 wll not be any nput data havng a much greater numercal value than others and ntendng to domnate the tranng. 5. Network Tranng The network nput varables are chosen accordng to the experence n engne modellng as the four varables shown n Fg.. The target matrx X o has ones n the frst column up to the th row and all the other entres are zeros, the second column has ones from the st row to the 4th row, and so on. The last column has ones from the 3 st row to the 34 th row. Ths s shown as follows. Rows X o ~ ~4 4~6 3~34 Thus, one of the 7 columns n the target matrx s assocated to a fault condton. Wth the chosen nput varables and the target, two RBF networks were traned wth the tranng data set, the same centres chosen usng the K-means clusterng algorthm were used for the two networks. The wdths were chosen usng the p-nearest algorthm, and the weghts were traned usng the Batch Least Squares algorthm. 5.3 Fault Dagnoss After tranng wth the tranng data set, the two networks are used to dagnose faults wth the test data set. The frst network s used to classfy the faults n the on-lne mode wth network adaptaton. The forgettng factor for the RLS algorthm was chosen a constant value of. 99. The dagnoss result s shown n (f) (h) of Fg.3. For comparson, the second network s traned off-lne wth the BLS algorthm and s not adapted on-lne. Ths network s also used to classfy the faults and the outputs are dsplayed n (a) (e) n Fg.3. The dagnoss test result n Fg.3 (h) s also shown n Fg.4 wth each output n a fgure to dsplay separately so that any msfre can be seen clearly. Fg.4 ndcates that all the 7 outputs have correct fault detecton results for the test data. It can be seen that the dagnoss by the adaptve network s much better than that by the fxed parameter network. The fault dagnoss result wth the non-adaptve classfer n (a) (e) have too much msclassfcaton and s not successful except for (c) whch s traned and tested for fxed throttle angle condtons (when the car s runnng on a constant speed and fault occurs). When the sze of the nonadaptve network s ncreased to centres chosen by the K-means method; the dagnoss result s shown n (b) of Fg.3. Though the performance s mproved wth an ncrease of centres, t stll has many msclassfcatons and s stll not as good as the adaptve method. In addton, centres greatly ncrease the computng load and are more dffcult to mplement n practce. All the fault dagnoss s conducted wth nosy data as explaned n secton 5..

6 B L S a lg o : T r a n t h r o t t le 6-3 4, T e s t 3 4-6, R B F N o d e s = (a) 3 B L S a lg o : T r a n t h r o t t le 6-3 4, T e s t 3 4-6, N o d e s = (b) B L S a lg o : T r a n t h r o t t le - 3 4, T e s t 3 8-6, R B F N o d e s = (c) B L S a lg o : T r a n t h r o t t le 6, T e s t 3 4, R B F N o d e s = 6 Adaptve algorthm: Test throttle angle 6-3, RBF Hdden Nodes= Left and rght columns show results for state No.,3,5,7,9,,3,5,7 and,4,6,8,,,4,6 respectvely Fg4: All the 7 faults n Fg.3 (h) are shown separately for clarty (d) B L S a lg o : T r a n t h r o t t le 3 4-4, T e s t 6-3, R B F N o d e s = (e) A d a p t v e a l g o : T e s t t h r o t t l e 3-6, R B F N o d e s = (f) A d a p t v e a l g o : T e s t t h r o t t l e 3, R B F N o d e s = (g) A d a p t v e a l g o : T e s t t h r o t t l e 6-3, R B F N o d e s = (h) Fg.3: Fault classfcaton test results wth nosy data It can be seen that these faults can stll be classfed when the data s contamnated by measurement nose. The classfcaton wth adaptve learnng uses much smaller sze of network and acheves a much hgher successful rate. The on-lne learnng utlses the fault detecton result so that faults are not learned as dynamcs changes. Therefore, ths scheme s stll workng after one fault occurs CONCLUSIONS A new adaptve RBF algorthm for on-board fault dagnoss of ar path of an automotve engne s developed. The smulaton results for a model wth fxed parameter classfer and an adaptve parameter classfer are compared. The results of adaptve classfer are much better than non-adaptve classfer and are also better than classfer wth much more hdden layer nodes. The adaptve algorthm s found to be tolerant to any parameter changes n the engne due to envronment or agng. It s also ndependent of engne-to-engne changes caused by batch products. The developed method may not be senstve to very slowly developed ncpent faults, as these faults would be treated as system uncertanty and be learned by the network. 7. REFERENCES Caprglone D., Lguor C., Panese C. and Petrosanto A. (3), On lne sensor fault detecton, solaton and accommodaton n automotve engnes, IEEE Transactons on Instrumentaton and Measurement, vol. 5, No. 4, August, pp Chen (99), Recursve hybrd algorthm for nonlnear system dentfcaton usng radal bass functon networks, Int. J. Control, vol.55 No.5, pp.5-7. Frank K., Schwarte A. and Isermann R. (5), Fault detecton for modern desel engnes usng sgnal and process model-based methods, Control Engneerng Practce, vol. 3, pp Hendrcks E., Engler D. and Fam M. (), A Generc Mean Value Engne Model for Spark Ignton Engnes. Insttute of Automaton, Denmark Techncal Unversty, Lung L. (999), System Identfcaton---Theory for the User, nd edton, Prentce-Hall, pp Nyberg M. and Stutte T. (4), Model based dagnoss of the ar path of an automotve desel engne, Control Engneerng Practce, vol., pp