×

Fault diagnosis by interval-based adaptive thresholds and peak-to-peak observers. (English) Zbl 07843533

Summary: This article investigates actuator fault diagnosis for continuous-time systems with unknown but bounded uncertainties. A novel interval-based adaptive threshold computation approach is proposed for residual evaluation. Meanwhile, peak-to-peak performance is applied to generate robust residuals against system uncertainties. By integrating the designed peak-to-peak residual generator with adaptive thresholds, we can achieve promising fault detection results. Furthermore, based on the general observer scheme, the proposed residual generator and adaptive thresholds can be equally used for fault isolation. Simulation results are given to illustrate the effectiveness and superiority of the proposed fault detection and isolation method.
{© 2022 John Wiley & Sons Ltd.}

MSC:

93B53 Observers
93B07 Observability
93B35 Sensitivity (robustness)
Full Text: DOI

References:

[1] BlankeM, KinnaertM, LunzeJ, StaroswieckiM. Diagnosis and Fault‐Tolerant Control. Springer; 2006. · Zbl 1126.93004
[2] DaiX, GaoZ. From model, signal to knowledge: a data‐driven perspective of fault detection and diagnosis. IEEE Trans Ind Inform. 2013;9(4):2226‐2238.
[3] ZhuJW, YangGH, WangH, WangF. Fault estimation for a class of nonlinear systems based on intermediate estimator. IEEE Trans Automat Contr. 2015;61(9):2518‐2524. · Zbl 1359.93075
[4] ZhuJW, YangGH. Robust distributed fault estimation for a network of dynamical systems. IEEE Trans Control Netw Syst. 2016;5(1):14‐22. · Zbl 1507.93062
[5] DuD, XuS, CocquempotV. Fault detection for nonlinear discrete‐time switched systems with persistent dwell time. IEEE Trans Fuzzy Syst. 2018;26(4):2466‐2474.
[6] DingSX. Model‐Based Fault Diagnosis Techniques: Design Schemes Algorithms and Tools. Springer; 2008.
[7] MaoZ, JiangB, ShiP. Fault detection for a class of nonlinear networked control systems. Int J Adapt Control Signal Process. 2010;24(7):610‐622. · Zbl 1200.93085
[8] WangZ, RodriguesM, TheilliolD, ShenY. Actuator fault estimation observer design for discrete‐time linear parameter‐varying descriptor systems. Int J Adapt Control Signal Process. 2015;29(2):242‐258. · Zbl 1337.93056
[9] WangZ, ShiP, LimCC. H_−/H_∞ fault detection observer in finite frequency domain for linear parameter‐varying descriptor systems. Automatica. 2017;86:38‐45. · Zbl 1375.93047
[10] LiJ, ShenY, WangZ, RodriguesM. Zonotopic fault detection observer for linear parameter‐varying descriptor systems. Int J Robust Nonlinear Control. 2019;29:3426‐3445. · Zbl 1426.93031
[11] ArmeniS, CasavolaA, MoscaE. Robust fault detection and isolation for LPV systems under a sensitivity constraint. Int J Adapt Control Signal Process. 2009;23(1):55‐72. · Zbl 1163.93346
[12] NematiF, HamamiSMS, ZemoucheA. A nonlinear observer‐based approach to fault detection, isolation and estimation for satellite formation flight application. Automatica. 2019;107:474‐482. · Zbl 1429.93258
[13] VidyasagarM. Optimal rejection of persistent bounded disturbances. IEEE Trans Automat Contr. 1986;31(6):527‐534. · Zbl 0594.93050
[14] HanW, WangZ, ShenY. H−/L_∞ fault detection observer for linear parameter‐varying systems with parametric uncertainty. Int J Robust Nonlinear Control. 2019;29(10):2912‐2926. · Zbl 1418.93068
[15] LiJ, WangZ, AhnCK, ShenY. Fault detection for Lipschitz nonlinear systems with restricted frequency‐domain specifications. IEEE Trans Syst Man Cybern Syst. 2020;51(12):7486‐7496.
[16] CoutinhoD, deSouzaCE, KinnaertM, SchonsS. Robust observer design for a class of discrete‐time nonlinear singular systems with persistent disturbances. Int J Adapt Control Signal Process. 2021;35(1):51‐68. · Zbl 1543.93026
[17] YangGH, WangH. Fault detection for a class of uncertain state‐feedback control systems. IEEE Trans Control Syst Technol. 2009;18(1):201‐212.
[18] WangZ, LimCC, ShiP, ShenY. H−/L_∞ fault detection observer design for linear parameter‐varying systems. IFAC‐PapersOnLine. 2017;50(1):15271‐15276.
[19] HanW, TrentelmanHL, XuB. Distributed
[( {H}_- / {L}_{\infty } \]\) fault detection observer design for linear systems. IFAC‐PapersOnLine. 2020;53(2):688‐693.
[20] GuanY, SaifM. A novel approach to the design of unknown input observers. IEEE Trans Automat Contr. 1991;36(5):632‐635.
[21] ZhengG, BejaranoFJ, PerruquettiW, RichardJP. Unknown input observer for linear time‐delay systems. Automatica. 2015;61:35‐43. · Zbl 1327.93098
[22] MarxB, IchalalD, RagotJ, MaquinD, MammarS. Unknown input observer for LPV systems. Automatica. 2019;100:67‐74. · Zbl 1411.93032
[23] FrankPM, DingSX. Survey of robust residual generation and evaluation methods in observer‐based fault detection systems. J Process Control. 1997;7(6):403‐424.
[24] FrankPM. Fault diagnosis in dynamic systems using analytical and knowledge‐based redundancy: a survey and some new results. Automatica. 1990;26(3):459‐474. · Zbl 0713.93052
[25] WangD, LumKY. Adaptive unknown input observer approach for aircraft actuator fault detection and isolation. Int J Adapt Control Signal Process. 2007;21(1):31‐48. · Zbl 1134.93318
[26] TangW, WangZ, ShenY. Fault detection and isolation for discrete‐time descriptor systems based on
[( {H}_- / {L}_{\infty } \]\) observer and zonotopic residual evaluation. Int J Control. 2020;93(8):1867‐1878. · Zbl 1453.93081
[27] WangY, PuigV, XuF, CembranoG. Robust fault detection and isolation based on zonotopic unknown input observers for discrete‐time descriptor systems. J Franklin Inst. 2019;356(10):5293‐5314. · Zbl 1415.93098
[28] ZhongM, DingSX, DingEL. Optimal fault detection for linear discrete time‐varying systems. Automatica. 2010;46(8):1395‐1400. · Zbl 1204.93078
[29] IngimundarsonA, BravoJM, PuigV, AlamoT, GuerraP. Robust fault detection using zonotope‐based set‐membership consistency test. Int J Adapt Control Signal Process. 2009;23(4):311‐330. · Zbl 1160.93318
[30] XuF, TanJ, WangX, LiangB. Conservatism comparison of set‐based robust fault detection methods: set‐theoretic UIO and interval observer cases. Automatica. 2019;105:307‐313. · Zbl 1429.93129
[31] YangS, XuF, WangX, LiangB. A novel online active fault diagnosis method based on invariant sets. IEEE Control Syst Lett. 2020;5(2):457‐462.
[32] XuF, PuigV, Ocampo‐MartinezC, OlaruS, StoicanF. Set‐theoretic methods in robust detection and isolation of sensor faults. Int J Syst Sci. 2015;46(13):2317‐2334. · Zbl 1333.93153
[33] LiJ, WangZ, ShenY, WangY. Zonotopic fault detection observer design for Takagi-Sugeno fuzzy systems. Int J Syst Sci. 2018;49(15):3216‐3230. · Zbl 1482.93219
[34] HuangJ, WangY, FukudaT. Set‐membership‐based fault detection and isolation for robotic assembly of electrical connectors. IEEE Trans Automat Sci Eng. 2018;15(1):160‐171.
[35] JuY, WeiG, DingD, LiuS. A novel fault detection method under weighted try‐once‐discard scheduling over sensor networks. IEEE Tran Control Netw Syst. 2020;7(3):1489‐1499. · Zbl 07255397
[36] ZhangZ, YangG. Distributed fault detection and isolation for multi‐agent systems: an interval observer approach. IEEE Trans Syst Man Cybern Syst. 2020;50(6):2220‐2230.
[37] IfqirS, DalilI, NaïmaAO, Saı̈dM. Adaptive threshold generation for vehicle fault detection using switched T-S interval observers. IEEE Trans Ind Electr. 2020;67(6):5030‐5040.
[38] RamiMA, TadeoF, HelmkeU. Positive observers for linear positive systems, and their implications. Int J Control. 2011;84(4):716‐725. · Zbl 1245.93026
[39] MeslemN, MartinezJ, RamdaniN, BesançonG. An
[( {H}_{\infty } \]\) interval observer for uncertain continuous‐time linear systems. Int J Robust Nonlinear Control. 2020;30(5):1886‐1902. · Zbl 1465.93079
[40] EfimovD, RaïssiT, ChebotarevS, ZolghadriA. Interval state observer for nonlinear time varying systems. Automatica. 2013;49(1):200‐205. · Zbl 1258.93032
[41] ZhouM, WangZ, ShenY. Fault detection and isolation method based on
[( {H}_- /\[ {H\] · Zbl 1386.93213
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.