Model Based Diagnostics Fault detection and diagnosis applications to automotive engines
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Model Based Diagnostics Fault detection and diagnosis applications to automotive engines Dr. David Antory E-mail: d.antory@warwick.ac.uk © 2006 IARC Presentation Agenda 1. Motivation & Objectives 2. Methodologies 3. Model based diagnostics Principal component model Systems identification models Neural networks model 4. Fault detection & diagnosis applications Automotive diesel engine Diagnose air leaks in the intake manifold Automotive gasoline engine ¾ Engine misfire detection 5. Conclusion ¾ © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 2 Motivation & objectives Increased number of electronics components in modern automotive vehicles. Automotive data can now easily access through its sensors/actuators. The efficiency and benefits of model-based techniques. The objectives of this research: • To optimise the use of automotive data for diagnostics purposes. • The diagnostics models are built using proposed model-based (data-driven modelling) techniques. © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 3 Methodologies Fig. 2: Representation of inter-relationship of the actual plant System can be: • automotive (engine, brake, powertrain, chassis, etc) • aerospace • train • vessels (ship, cruise, etc) • chemical plants • etc. © 2006 IARC Fig. 1: Flow diagram of the proposed diagnostic model Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 4 Automotive area of applications Development & Validation Testing Is the vehicle designed correctly? Design for Testability Testability Simulation Hardware in Loop/Rig On Vehicle Tools Manufacturing Testing Test Effectiveness Is the vehicle built correctly? Software Download (e.g. engine tune) Configuration (e.g. market) Tests (e.g. ECU inputs & outputs connected) Test Efficiency Skills Service Testing How to fix? Organisational Processes © 2006 IARC Diagnostics Test Update software or configuration Fig. 3: Scope of Electrical Test Projects Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 5 input output system nominal actual parameters model Compare & Evaluate fault detection and diagnosis unknown Model based diagnostics Principal component model System identification Neural networks decision making © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 6 Principal component model • Capture redundant (highly correlated) information via eigenvalue – eigenvector decomposition • Matrix transformation (columns ~ measurement signals, rows ~ number of samples) X= t 1pΤ1 + t 2pT2 + L + t k pTk k + E = ∑ t ipTi + E i =1 S xx p i = λi p i e = x − tPT = x[I n − PPT ] t = x new P k T 2 = t T Λ −1t = ∑ i =1 t i2 λi n Q = e e = ∑ e 2j T j =1 © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 7 Linear model based on AR and ARMA methods • Time series analysis • To estimate the parameters required to build a model based on the known output value • Auto-Regressive model na y (t ) = − ∑ a i y (t − i ) i =1 • Auto-Regressive Moving Average model na nc i =1 j =0 y (t ) = −∑ a i y (t − i ) + ∑ e j (t − j ) © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 8 Nonlinear model based on neural networks Linear model (AR/ARMA) parameters are used as a backbone to construct the nonlinear model y (t ) = f (y (t − 1), y (t − 2 ), y (t − 3), y (t − 4 ), y (t − 5)) Nh y = g (u, w ) = ∑ w 2j f (x ) j =1 . Ni x = ∑ w1ij ui + b j i =1 ⎡ y (t − 1) ⎤ ⎢y (t − 2 )⎥ ⎢ ⎥ u = ⎢ y (t − 3)⎥ ⎥ ⎢ ( ) y t − 4 ⎥ ⎢ ⎢⎣ y (t − 5)⎥⎦ Fig. 4: The network architecture of the proposed nonlinear misfire detection model. © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 9 Fault detection and diagnostics applications • Automotive diesel engine • Automotive gasoline engine © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 10 Automotive diesel engine Diagnostics of manifold air leaks bias at the inlet manifold pressure (sensor fault) intercooler inlet manifold temperature manifold plenum chamber small air leaks (process fault) DYNAMOMETER turbine inlet pressure turbine inlet temperature air in exhaust compressor (turbocharger) • • • • turbine exit pressure 4 cyl,1.9 ltr TDI diesel engine 145 kW AC Schenck dynamometer Ricardo control system Time sampling: 10 Hz for 30s © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 11 Detection of manifold air leaks 1210 12091207 1206 1208 40 Conforming samples Non-conforming samples 1205 1212 1211 1204 35 1203 1213 1202 1483 1482 Hotelling's T2 Statistic 30 1485 1484 1201 1481 1214 1486 25 20 1442 1441 1444 1443 1440 1439 1433 1431 1432 1438 1437 1446 1445 1434 1435 1430 1429 1436 1448 1447 1428 1480 1241 1215 1487 1242 1240 1216 1479 1488 1449 1489 1456 1455 1454 1450 1490 1453 1457 1452 1477 1478 1451 1459 1458 1460 1491 R4 R3 1461 1476 1475 1492 1427 15 10 1462 1493 1426 1419 1418 1417 1416 1494 1474 1415 1420 1421 1425 1464 1463 1414 1422 1424 1423 1347 1413 1348 1412 1497 1496 1473 1368 135113501349 1369 1498 13671500 1499 1495 1352 1355 1354 1411 1366 1353 1465 1356 1466 1381 1382 1467 1471 1470 1469 1410 1380 1379 1365 1383 1472 1468 1409 1370 1384 1358 1357 1364 1385 1360 1359 13631362 1361 1378 1408 1371 1386 1377 1407 1387 1372 1376 1389 13881390 1374 1373 1375 1406 1405 1403 1402 1404 1391 1401 1400 1399 1392 1398 1394 1393 1397 1396 1395 1244 1243 1217 1245 1239 1315 1310 1311 1280 1314 1313 1297 1279 1312 1278 1299 1300 1298 1296 1219 1218 1272 1295 1294 1277 1316 1293 1220 1274 1273 1309 1282 1270 1269 1276 1271 1275 1268 1267 1317 1292 1302 1301 1281 1284 1221 1283 1260 1266 1318 1307 1308 1222 1259 1262 1261 1246 1265 1323 1327 13261304 1263 1223 1258 1257 1303 1264 1230 1324 1325 1320 1319 1228 1229 1285 1238 1248 1247 1306 1305 1291 1225 1224 1321 1322 1227 1226 1328 1231 1286 1290 1256 1232 1237 1329 1249 1289 1287 1233 1330 1288 1255 1234 1250 1331 1236 1235 1332 1333 1254 1334 1251 1335 1336 1337 1338 1253 1252 1339 1341 1340 1342 1344 1343 1346 1345 R1 R2 5 0.1 0.2 0.3 0.4 0.5 0.6 Q Residual Statistic © 2006 IARC Fig. 5: A joint diagnostics plot of Q and T2 statistics Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 12 Automotive gasoline engine Engine misfire detection V8 cylinder, 4.2 litre gasoline engine Crankshaft angular velocity measurements Test-cell environment Two different types of tests were conducted Types of faults: continuous & intermittent misfire How fault is introduced: • disabling the ignition signal to a specific cylinder • at a frequency of 1 in 100 firings • generating a misfire every 12 to 13 ignition events • firing order: 1A-1B-4A-2A-2B-3A-3B-4B © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 13 Linear approaches (a) AR approach 3 2.5 2 1.5 1 0.5 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 3500 4000 4500 5000 (b) ARMA approach 2 1.5 1 0.5 0 0 500 1000 1500 2000 2500 3000 Fig. 6: Illustration of intermittent misfire detection using standard linear system identification approaches. © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 14 Nonlinear approach (a) Residual evaluation of intermittent misfire data 2 1 0 -1 -2 -3 -4 -5 -6 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 4000 4500 5000 (b) Illustration of intermittent misfire detection 40 30 20 10 0 500 1000 1500 2000 2500 3000 3500 Fig. 7: Residual evaluation (a) and illustration of intermittent misfire detection (b) using a proposed nonlinear extension approach. © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 15 Concluding summary Automotive data contains useful information about vehicle’s characteristics throughout its life cycle Data-driven model based diagnostics can be used to extract the required information. The diagnostics models were derived entirely from the (sensor/actuator) measurement signals. The proposed approach is simple and straight-forward. No assumptions are required. The techniques discussed are applicable to support the diagnostics process for non-automotive applications © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 16 Status & next steps Completion of case studies • Data-driven diagnostic methods • Applications to automotive engines • Evaluation by partners Define and implement mechanisms for technology transfer • Appropriate training Final report on application of technique • Available to partners • Publications (Internal Report, International Journal/Conference) Develop recommendations for further research © 2006 IARC • Definition of new areas where this technique could be applied (automotive and non-automotive) • Extending current research to testing & validation platform (dSPACE Hardware-In-the-Loop, CANalyzer/CANoe, LabVIEW) Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 17 Thank you for your kind attention Contact details: Dr. David Antory Project Engineer (Diagnostics) Electrical Test for Advanced Architectures Project Premium Automotive R&D Programme International Automotive Research Centre (IARC) Warwick Manufacturing Group, University of Warwick Coventry, CV4 7AL, West Midlands, United Kingdom Phone E-mail Webpage : +44 (0)24 7657 5441 (direct) : d.antory@warwick.ac.uk : www.iarc.warwick.ac.uk © 2006 IARC Model Based Diagnostics - Fault detection and diagnosis applications to automotive engines engines 18
