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Artificial Intelligence Review

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10-11-2020

A review on fault detection and diagnosis techniques: basics and beyond

Authors: Anam Abid, Muhammad Tahir Khan, Javaid Iqbal

Published in: Artificial Intelligence Review | Issue 5/2021

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Abstract

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.

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Abad MRAA, Moosavian A, Khazaee M (2016) Wavelet transform and least square support vector machine for mechanical fault detection of an alternator using vibration signal. J Low Freq Noise Vib Active Control 35(1):52–63

Abaei G, Selamat A (2014) A survey on software fault detection based on different prediction approaches. Vietnam J Comput Sci 1(2):79–95

Abbasi AR, Mahmoudi MR, Avazzadeh Z (2018) Diagnosis and clustering of power transformer winding fault types by cross-correlation and clustering analysis of FRA results. IET Gener Transm Distrib 12(19):4301–4309

Abid A, Khan MT, de Silva CW (2018) Layered and real-valued negative selection algorithm for fault detection. IEEE Syst J 12(3):2960–2969

Abid A, Khan MT, Lang H, Silva CWD (2019) Adaptive system identification and severity index-based fault diagnosis in motors. IEEE/ASME Trans Mechatron 24(4):1628–1639

Abid A, Khan MT, Khan MS (2020b) Multidomain features-based GA optimized fault detection. IEEE Trans Syst Man Cybern Syst 50(1):348–359

Abid A, Khan MT, Haq IU, Anwar S, Iqbal J (2020a) An improved negative selection algorithm-based fault detection method. IETE J Res, pp 1–12

Abid A, Khan MT, Silva CWD (2015) Fault detection in mobile robots using sensor fusion. In: 10th international conference on computer science and education (ICCSE 2015). Cambridge University, UK, pp 8–13, July 22–24, 2015

Abid A, Khan MT (2017) Multi-sensor, multi-level data fusion and behavioral analysis based fault detection and isolation in mobile robots. In: IEEE 8th annual information technology. Electronics and mobile communication conference (IEMCON). Vancouver, Canada, pp 40–45

Abid A, Khan MT, Ullah A, Alam M, Sohail M (2017) Real time health monitoring of industrial machine using multiclass support vector machine. In: 2nd International conference on control and robotics engineering, vol 2, pp 77–81

Ahmed HOA, Nandi AK (2019) Three-stage hybrid fault diagnosis for rollin bearings with compressively sampled data and subspace learning techniques. IEEE Trans Ind Electron 66(7):5516–5524

Ballal MS, Khan ZJ, Suryawanshi HM, Sonolikar RL (2007) Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Trans Ind Electron 54(1):250–258

Ben Hmida F, Khémiri K, Ragot J, Gossa M (2012) Three-stage Kalman filter for state and fault estimation of linear stochastic systems with unknown inputs. J Franklin Inst 349(7):2369–2388MathSciNetMATH

Benbouzid MEH, Vieira M, Theys C (1999) Induction motors’ faults detection and localization using stator current advanced signal processing techniques. IEEE Trans Power Electron 14(1):14–22

Benmoussa S, Djeziri MA (2017) Remaining useful life estimation without needing for prior knowledge of the degradation features. IET Sci Meas Technol 11(8):1071–1078

Benmoussa S, Bouamama BO, Merzouki R (2014) Bond graph approach for plant fault detection and isolation: application to iIntelligent autonomous vehicle. IEEE Trans Autom Sci Eng 11(2):585–593

Bennacer L, Amirat Y, Chibani A, Mellouk A, Ciavaglia L (2015) Self-diagnosis technique for virtual private networks combining bayesian networks and case-based reasoning. IEEE Trans Autom Sci Eng 12(1):354–366

Bharathi A, Natarajan AM (2010) Cancer classification of bioinformatics data using ANOVA. Int J Comput Theory Engi 2(3):369–373

Bighamian R, Mirdamad HR, Hahn J-O (2015) Damage identification in collocated structural systems using structural Markov parameters. J Dyn Syst Meas Control 137(4):041001–041009

Bin J (2006) Model-based fault tolerant control for hybrid dynamic systems with sensor faults. Acta Autom Sin 32(5):680–685MathSciNet

Blödt M, Chabert M, Regnier J, Faucher J (2006) Mechanical load fault detection in induction motors by stator current time-frequency analysis. IEEE Trans Ind Appl 42(6):1454–1463

Bolchini C, Cassano L, Garza P, Quintarelli E, Salice F (2015) An expert CAD flow for incremental functional diagnosis of complex electronic boards. IEEE Trans Comput Aided Des Integr Circuits Syst 34(5):835–848

Boudiaf A, Moussaoui A, Dahane A (2016) A comparative study of various methods of bearing faults diagnosis using the case western reserve university data. J Fail Anal Prev 16(2):271–284

Boudinar AH, Benouzza N, Bendiabdellah A, Khodja MEA (2016) Induction motor bearing fault analysis using a root-MUSIC method. IEEE Trans Ind Appl 52(5):3851–3860

Boulkroune B, Gálvez-carrillo M, Kinnaert M (2013) Combined signal and model-based sensor fault diagnosis for a doubly fed induction generator. IEEE Trans Control Syst Technol 21(5):1771–1783

Burns DJ, Danielson C, Zhou J, Di Cairano S (2018) Reconfigurable model predictive control for multievaporator vapor compression systems. IEEE Trans Control Syst Technol 26(3):984–1000

Camarena-Martinez D, Osornio-Rios R, Romero-Troncoso RJ, Garcia-Perez A (2016) Fused empirical mode decomposition and MUSIC algorithms for detecting multiple combined faults in induction motors. J Appl Res Technol 13:160–167

Capisani LM, Ferrara A, Alejandra Ferreira DL, Fridman ML (2011) Manipulators fault diagnosis via higher order sliding mode observers. IEEE Trans Ind Electron 59(10):3979–3986

Chehade A, Bonk S, Liu K (2017) Sensory-based failure threshold estimation for remaining useful life prediction. IEEE Trans Reliab 66(3):939–949

Chen X, Yan X (2013) Fault diagnosis in chemical process based on self-organizing map integrated with fisher discriminant analysis. Chin J Chem Eng 21(4):382–387

Chen W, Chen W-T, Saif M, Li M-F, Wu H (2014) Simultaneous fault isolation and estimation of lithium-ion batteries via synthesized design of Luenberger and learning observers. IEEE Trans Control Syst Technol 22(1):290–298

Cheng H, Tikkala VM, Zakharov A, Myller T, Jämsä-Jounela SL (2011) Application of the enhanced dynamic causal digraph method on a three-layer board machine. IEEE Trans Control Syst Technol 19(3):644–655

Cheng G, Cheng YL, Shen LH, Qiu JB, Zhang S (2013) Gear fault identification based on Hilbert–Huang transform and SOM neural network. Meas J Int Meas Confed 46(3):1137–1146

Cheng F, He QP, Zhao J (2019) A novel process monitoring approach based on variational recurrent autoencoder. Comput Chem Eng 129:1–14

Conatser R, Wagner J, Ganta S, Walker I (2004) Diagnosis of automotive electronic throttle control systems. Control Eng Pract 12(1):23–30

Cordoneanu D, Nitu C (2018) A review of fault diagnosisin mechatronics systems. Int J Mechatron Appl Mech 3:228–235

Costa Silva G, Palhares RM, Caminhas WM (2012) Immune inspired fault detection and diagnosis: a fuzzy-based approach of the negative selection algorithm and participatory clustering. Expert Syst Appl 39(16):12474–12485

Dai X, Gao Z (2013) From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans Ind Inf 9(4):2226–2238

Dai Y, Zhao J (2011) Fault diagnosis of batch chemical processes using a dynamic time warping (DTW)-based artificial immune system. Ind Eng Chem Res 50(8):4534–4544

Diao Y, Passino KM (2002) Intelligent fault-tolerant control using adaptive and learning methods. Control Eng Pract 10(8):801–817

El Bouchikhi EH, Choqueuse V, Benbouzid M (2014) Induction machine faults detection using stator current parametric spectral estimation. Mech Syst Signal Process 52:447–464

Fass L (2008) Imaging and cancer: a review. Mol Oncol 2:115–152

Feng Z, Ma H, Zuo MJ (2016) Vibration signal models for fault diagnosis of planet bearings. J Sound Vib 370:372–393

Feng Z, Zhou Z, Hu C, Yin X, Hu G, Zhao F (2017) Fault diagnosis based on belief rule base with considering attribute correlation. IEEE Access 6:2055–2067

Gadsden SA, Song Y, Habibi SR (2013) Novel model-based estimators for the purposes of fault detection and diagnosis. IEEE/ASME Trans Mechatron 18(4):1237–1249

Gajanayake C, Bhangu BS, Foo G, Zhang X, Tseng KJ, Vilathgamuwa MD (2013) Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives. IET Electr Power Appl 7(7):607–617

Gao XZ, Ovaska SJ, Wang X, Chow MY (2010) Multi-level optimization of negative selection algorithm detectors with application in motor fault detection. Intell Autom Soft Comput 16(3):353–375

Gelle G, Galy J, Delaunay G (2000) Blind source separation: a tool for system monitoring and fault detection?. In: IFAC Proceedings on fault detection, supervision and safety for tcchnicall’rocesses, vol 33. Elsevier, Budapest, Hungary , pp 705–710

Goebel K, Yan W (2008) Correcting sensor drift and intermittency faults with data fusion and automated learning. IEEE Syst J 2(2):189–197

Gottumukkala P, G SR (2016) Fault Detection in Mobile Communication Networks Using Data Mining techniques with big data analytics. Int J Cybern Inf 5(4):81–89

Grebenik J, Zhang Y, Bingham C, Srivastava S (2016) Roller element bearing acoustic fault detection using smartphone and consumer microphones comparing with vibration techniques. In: 17th international conference on mechatronics - mechatronika (ME), vol 1, pp 1–7

Guo H, Xu J, Chen YH (2015) Robust control of fault-tolerant permanent-magnet synchronous motor for aerospace application with guaranteed fault switch process. IEEE Trans Ind Electron 62(12):7309–7321

Haddad RZ, Strangas EG (2016) On the accuracy of fault detection and separation in permanent magnet synchronous machines using MCSA/MVSA and LDA. IEEE Trans Energy Convers 31(3):924–934

Hafaifa A, Guemana M, Daoudi A (2013) Fault detection and isolation in industrial systems based on spectral analysis diagnosis. Intell Control Autom 4(2):36–41

Haque MS, Choi S, Baek J (2018) Auxiliary particle filtering-based estimation of remaining useful life of IGBT. IEEE Trans Ind Electron 65(3):2693–2703

Hekmat S, Ravanmehr R (2016) Real time fault detection and isolation: a comparative study. Int J Comput Appl 134(6):8–15

Henr P, Alonso B, Ferrer MA, Travieso CM (2014) Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans Syst Man Cybern Syst 44(5):642–652

Hong W, Tian-You C, Jin-Liang D, Martin B (2009) Data driven fault diagnosis and fault tolerant control: some advances and possible new directions. Acta Autom Sin 35(6):739–747MathSciNet

Huang S, Tan KK, Lee TH (2012) Fault diagnosis and fault-tolerant control in linear drives using the Kalman filter. IEEE Trans Ind Electron 59(11):4285–4292

Huang H, Ouyang H, Gao H (2015) Blind source separation and dynamic fuzzy neural network for fault diagnosis in machines. In: Journal of physics: conference series 11th international conference on damage assessment of structures (DAMAS), vol 628

Isermann R (2005) Model-based fault-detection and diagnosis-status and applications. Ann Rev Control 29:71–85

James AT, Gandhi OP, Deshmukh SG (2018) Fault diagnosis of automobile systems using fault tree based on digraph modeling. Int J Syst Assur Eng Manag 9(2):494–508

Jiang X-p, Cao G-q (2015) Belt conveyor roller fault audio detection based on the wavelet neural network. In: 11th International conference on natural computation (lCNC), pp 954–958

Jiang W, Wei B, Xie C, Zhou D (2016) An evidential sensor fusion method in fault diagnosis. Adv Mech Eng 8(3):1–7

Jiang G, Xie P, He H, Yan J (2018a) Wind turbine fault detection using a denoising autoencoder with temporal information. IEEE/ASME Trans Mechatron 23(1):89–100

Jiang Y, Yin S, Kaynak O (2018b) Data-driven monitoring and safety control of industrial cyber-physical systems: basics and beyond. IEEE Access 6:47374–47384

Jin S, Kim JS, Lee SK (2015) Sensitive method for detecting tooth faults in gearboxes based on wavelet denoising and empirical mode decomposition. J Mech Sci Technol 29(8):3165–3173

Jung JH, Lee JJ, Kwon BH (2006) Online diagnosis of induction motors using MCSA. IEEE Trans Ind Electron 53(6):1842–1852

Kemalkar AK, Bairagi VK (2017) Engine fault diagnosis using sound analysis. Int Conf Autom Control Dyn Optim Tech ICACDOT 2016:943–946

Kim MH, Lee S, Lee KC (2011) A fuzzy predictive redundancy system for fault-tolerance of x-by-wire systems. Microprocess Microsyst 35(5):453–461

Khireddine MS, Chafaa K, Slimane N, Boutarfa A (2014) Fault diagnosis in robotic manipulators using artificial neural networks and fuzzy logic. In: 2014 World congress on computer applications and information systems (WCCAIS), Hammamet, pp 1–6. https://doi.org/10.1109/WCCAIS.2014.6916571

Kong W, Luo Y, Qin Z, Qi Y, Lian X (2019) Comprehensive fault diagnosis and fault-tolerant protection of in-vehicle intelligent electric power supply network. IEEE Trans Veh Technol 68(11):10453–10464

Kumar A, Kumar R (2017) Time-frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump. Meas J Int Meas Confed 108:119–133

Kwak J, Lee T, Kim CO (2015) An incremental clustering-based fault detection algorithm for class-imbalanced process data. IEEE Trans Semicond Manuf 28(3):318–328

Laurentys CA, Ronacher G, Palhares RM, Caminhas WM (2010) Design of an artificial immune system for fault detection: a negative selection approach. Expert Syst Appl 37(7):5507–5513

Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK (2020) Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process 138:1–39

Li X, Zhang W (2010) An adaptive fault-tolerant multisensor navigation strategy for automated vehicles. IEEE Trans Veh Technol 59(6):2815–2829MathSciNet

Li D, Zhou Y, Hu G, Spanos CJ (2016) Fault detection and diagnosis for building cooling system with a tree-structured learning method. Energy and Build 127:540–551

Li X, Ding Q, Sun JQ (2018) Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab Eng Syst Saf 172(2017):1–11

Lin W-c, Du X (2018) Prognosis of power connector disconnect and high resistance faults. In: 2018 IEEE international conference on prognostics and health management (ICPHM), vol 2, pp 1–8

Lin WC, Ghoneim YA (2016) Model-based fault diagnosis and prognosis for electric power steering systems. In: IEEE international conference on prognostics and health management, ICPHM, pp 1–8

Liu Z, Wang J, Duan L, Shi T, Fu Q (2017) infrared image combined with cnn based fault diagnosis for rotating machinery. In: 2017 International conference on sensing, diagnostics, prognostics, and control (SDPC), pp 137–142

Lizarraga-Morales RA, Rodriguez-Donate C, Cabal-Yepez E, Lopez-Ramirez M, Ledesma-Carrillo LM, Ferrucho-Alvarez ER (2017) Novel FPGA-based methodology for early broken rotor bar detection and classification through homogeneity estimation. IEEE Trans Instrum Meas 66(7):1760–1769

Loparo KA (2012) CWRU Case western reserve university bearing test data center

Low CB, Wang D, Member S, Arogeti S, Luo M (2010) Quantitative hybrid bond graph-based fault detection and isolation. IEEE Trans Autom Sci Eng 7(3):558–569

Mahgoun H, Bekka RE, Felkaoui A (2013) Gearbox fault detection using a new denoising method based on ensemble empirical mode decomposition and Fft. In: 4th International conference on integrity, reliability and failure (IRF2013), pp 1–11

Malhi A, Gao RX (2004) PCA-based feature selection scheme for machine defect classification. IEEE Trans Instrum Meas 53(6):1517–1525

McDonald TP, Fulton JP (2005) Automated time study of skidders using global positioning system data. Comput Electron Agric 48(1):19–37

Mitra P, Member S, Murthy CA, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24(3):301–312

Mostafa SA, Mustapha A, Hazeem AA, Khaleefah SH, Mohammed MA (2018) An agent-based inference engine for efficient and reliable automated car failure diagnosis assistance. IEEE Access 6:8322–8331

Mouba J, Marchand S (2006) A source localization / separation / respatialization system based on unsupervised classification of interaural cues. In: Proceedings of the 9th international conference on digital audio effects, pp 233–238, Canada

Naqvi SM, Khan MS, Liu Q, Wang W, Chambers JA (2011) Multimodal blind source separation with a circular microphone array and robust beamforming. In: European signal processing conference. Barcelona, Spain, pp 1050–1054

Ploeg J, Semsar-Kazerooni E, Lijster G, Van De Wouw N, Nijmeijer H (2015) Graceful degradation of cooperative adaptive cruise control. IEEE Trans Intell Transp Syst 16(1):488–497

Purarjomandlangrudi A, Ghapanchi AH, Esmalifalak M (2014) A data mining approach for fault diagnosis: an application of anomaly detection algorithm. Measurement 55:343–352

Qiu M, Li W, Jiang F, Zhu Z (2018) Remaining useful life estimation for rolling bearing with SIOS-based indicator and particle filtering. IEEE Access 6:24521–24532

Ranjan PV (2017) Machine condition monitoring using audio signature analysis. In: 4th International conference on signal processing. communications and networking (ICSCN -2017). Chennai, India, pp 1–6

Rodrigues LR (2018) Remaining useful life prediction for multiple-component systems based on a system-level performance indicator. IEEE/ASME Trans Mechatron 23(1):141–150MathSciNet

Romero-troncoso RJ, Saucedo-gallaga R, Cabal-yepez E, Garcia-perez A, Osornio-rios RA, Alvarez-salas R, Miranda-vidales H, Huber N (2011) FPGA-based online detection of multiple combined faults in induction motors through information entropy and fuzzy inference. IEEE Trans Ind Electron 58(11):5263–5270

Sadeghkhani I, Golshan MEH, Mehrizi-Sani A, Guerrero JM, Ketabi A (2018) Transient monitoring function-based fault detection for inverter-interfaced microgrids. IEEE Trans Smart Grid 9(3):2097–2107

Salehifar M, Arashloo RS, Moreno-equilaz JM, Sala V, Romeral L (2014) Fault Detection and Fault Tolerant Operation of a Five Phase PM Motor drive using adaptive model identification approach. IEEE J Emerg Sel Top Power Electron 2(2):212–223

Salmasi FR, Najafabadi TA, Maralani PJ (2010) An Adaptive Flux Observer With Online Estimation of DC-Link Voltage and rotor resistance for VSI-based induction motors. IEEE Trans Power Electron 25(5):1310–1319

Samantaray K, Medjaher K, Ould Bouamama B, Staroswiecki M, Dauphin-Tanguy G (2006) Diagnostic bond graphs for online fault detection and isolation. Simul Modell Pract Theory 14(3):237–262

Samantaray S, Panigrahi B, Dash P (2008) High impedance fault detection in power distribution networks using time-frequency transform and probabilistic neural network. IET Gener Trans Distrib 28(2):261–270

Senanayaka JSL, Khang HV, Robbersmyr KG (2019) Multiple classifier and data fusion for robust fault diagnosis of gearbox mixed faults. IEEE Trans Ind Inf 15(8):4569–4579

Shah DS, Patel VN (2014) A Review of Dynamic Modeling and Fault Identifications Methods for Rolling element bearing. Procedia Technol 14:447–456

Shao H, Jiang H, Zhao H, Wang F (2017a) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process 95:187–204

Shao H, Jiang H, Zhao H, Wang F (2017b) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Knowl Based Syst 119:200–220

Shao H, Jiang H, Zhang H, Duan W, Liang T, Wu S (2018) Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech Syst Signal Process 100:743–765

Shen Q, Jiang B, Member S, Cocquempot V (2013) Fuzzy Logic System-Based Adaptive Fault-Tolerant Control for Near-Space vehicle attitude dynamics with actuator faults. IEEE Trans Fuzzy Syst 21(2):289–300

Shen C, Wang D, Liu Y, Kong F, Tse PW (2014) Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines. Smart Struct Syst 13(3):453–471

Shu Y, Liu H, Wu Z, Yang X (2009) Modeling of software fault detection and correction processes based on the correction lag. Inform Technol J 8(5):735–742

Soualhi A, Clerc G, Razik H (2013) Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique. IEEE Trans Ind Electron 60(9):4053–4062

Strangas EG, Aviyente S, Zaidi SSH (2008) Time-frequency analysis for efficient fault diagnosis and failure prognosis for interior permanent-magnet AC motors. IEEE Trans Ind Electron 55(12):4191–4199

Su J, Chen W-h (2019) Model-based fault diagnosis system verification using reachability analysis. IEEE Trans Syst Man Cybern Syst 49(4):742–751

Tabbache B, El M, Benbouzid H, Kheloui A, Bourgeot J-M (2013) Virtual-sensor-based maximum-likelihood voting approach for fault-tolerant control of electric vehicle powertrains. IEEE Trans Veh Technol 62(3):1075–1083

Tadina M, Bolte M (2011) Improved model of a ball bearing for the simulation of vibration signals due to faults during run-up. J Sound Vib 300(17):4287–4301

Thumati B, Sarangapani J (2018) A Novel Fault Diagnostics and Prediction Scheme Using a Nonlinear Observer with artificial immune system as an online approximator. IEEE Trans Control Syst Technol 26(1):377–378

Tong Z, Li W, Jiang F, Zhu Z, Zhou G (2018) Bearing fault diagnosis based on spectrum image sparse representation of vibration signal. Adv Mech Eng 10(9):1687814018797788

Venkatasubramanian V, Rengaswamy R, Ka SN (2003) A review of process fault detection and diagnosis part III: process history based methods. Comput Chem Eng 27:327–346

Wang Y, Cheng Y (2016) An approach to fault diagnosis for gearbox based on image processing. Shock Vib 1–10:2016

Wang W, Lee H (2013) An energy kurtosis demodulation technique for signal denoising and bearing fault detection. Meas Sci Technol 24(2):025601

Wang G, Li T, Zhang G, Gui X, Xu D (2014) Recursive-Least-Square Adaptive Filter for Model-Based Sensorless Interior permanent-magnet synchronous motor drives. IEEE Trans Ind Electron 61(9):5115–5125

Wang J, Zhang J, Qu B, Wu H, Zhou J (2017) Unified architecture of active fault detection and partial active fault-tolerant control for incipient faults. IEEE Trans Syst Man Cybern Syst 47(7):1688–1700

Wang B, Wang J, Griffo A, Sen B (2018) Stator turn fault detection by second harmonic in instantaneous power for a triple-redundant fault-tolerant PM drive. IEEE Trans Ind Electron 65(9):7279–7289

Wang Z-Q, Hu C-H, Fan H-D (2018) Real-remaining useful life prediction for a nonlinear degrading system in service: application to bearing data. IEEE/ASME Trans Mechatron 23(1):211–222

Wang Y, Ren X, Nan G, Yang Y, Deng W (2012) Rotating machine fault diagnosis based on denoising source separation. In: 2012 IEEE 5th international conference on advanced computational intelligence. ICACI 2012. Nanjing, Jiangsu, China, pp 1124–1127

Wei Y, Xu M, Wang X, Huang W, Li Y (2019) A hybrid approach for weak fault feature extraction of gearbox. IEEE Access 7:16616–16625

Weipeng Z (2013) International journal of mining science and technology image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering. Int J Min Sci Technol 23(2):221–225

Wen L, Gao L, Li X (2019) A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans Syst Man Cybern Syst 49(1):136–144

Wu H, Zhao J (2018) Deep convolutional neural network model based process fault diagnosis. Comput Chem Eng 115:185–197

Wu H, Zhao J (2020) Fault detection and diagnosis based on transfer learning for multimode chemical processes. Comput Chem Eng 135:1–27

Xia M, Li T, Xu L, Liu L, De Silva CW (2018) Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans Mechatron 23(1):101–110

Xiao B, Yin S, Gao H (2018) Reconfigurable tolerant control of uncertain mechanical systems with actuator faults: a sliding mode observer-based approach. IEEE Trans Control Syst Technol 26(4):1249–1258

Yaman O, Karaköse M, Ak E, Ayd (2015) Image processing based fault detection approach for rail surface

Yan K, Shen W, Mulumba T, Afshari A (2014) ARX model based fault detection and diagnosis for chillers using support vector machines. Energy and Build 81:287–295

Yang G-H, Li X-J (2013) Fault diagnosis for non-linear single output systems based on adaptive high-gain observer. IET Control Theory Appl 7(16):1969–1977MathSciNet

Yang S, Member S, Tang Y (2018) Seamless fault-tolerant operation of a modular multilevel converter with switch open-circuit fault diagnosis in a distributed control architecture. IEEE Trans Ind Electron 33(8):7058–7070

Yan K, Ji Z, Lu H, Huang J, Shen W, Xue Y (2019a) Fast and accurate classification of time series data using extended elm : application in fault diagnosis of air handling units. IEEE Trans Syst Man Cybern Syst 49(7):1349–1356

Yan X, Liu Y, Jia M, Zhu Y (2019b) A multi-stage hybrid fault diagnosis approach for rolling element bearing under various working conditions. IEEE Access 7:138426–138441

Yuan J, Liu G, Member S, Wu B (2011) Power Efficiency Estimation-Based Health Monitoring and Fault Detection of modular and reconfigurable robot. IEEE Trans Ind Electron 58(10):4880–4887

Zhang Y, Jiang J (2003) Fault tolerant control system design with explicit consideration of performance degradation. IEEE Trans Aerosp Electron Syst 39(3):838–848

Zhang L, Zhai J (2018) Fault diagnosis for oil-filled transformers using voting based extreme learning machine. Cluster Comput 1:1–8

Zhang Z, Zhao J (2017) A deep belief network based fault diagnosis model for complex chemical processes. Comput Chem Eng 107:395–407

Zhang Y, Fan Y, Du W (2016) Nonlinear process monitoring using regression and reconstruction method. IEEE Trans Autom Sci Eng 13(3):1343–1354

Zhang G, Zhang H, Huang X, Wang J, Yu H, Graaf R (2016a) Active fault-tolerant control for electric vehicles with independently driven rear in-wheel motors against certain actuator faults. IEEE Trans Control Syst Technol 24(5):1557–1572

Zhang H, Bauer L, Kochte MA, Schneider E, Wunderlich H-J, Henkel J (2016b) Aging resilience and fault tolerance in runtime reconfigurable architectures. IEEE Trans Comput 66(6):1MathSciNetMATH

Zhang D, Qian L, Mao B, Huang C, Huang B, Si Y (2018) A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE Access 6:21020–2103

Zhang J, Wang P, Gao RX, Yan R (2018a) An image processing approach to machine fault diagnosis based on visual words representation. Procedia Manuf 19(2017):42–49

Zhang W, Li C, Peng G, Chen Y, Zhang Z (2018b) A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process 100:439–453

Zhao Y, Lam J, Gao H (2009) Fault detection for fuzzy systems with intermittent measurements. IEEE Trans Fuzzy Syst 17(2):398–410

Zheng S, Zhao J (2020) A new supervised data mining method based on the stacked autoencoder for chemical process fault diagnosis. Comput Chem Eng 135:1–31

Zhong ZM, Chen J, Zhong P, Wu JB (2006) Application of the blind source separation method to feature extraction of machine sound signals. Int J Adv Manuf Technol 28(9):855–862

Zhong K, Han M, Han B (2020) Data-driven based fault prognosis for industrial systems: a concise overview. IEEE/CAA J Autom Sin 7(2):330–345MathSciNet

Zhou S, Qian S, Chang W, Xiao Y, Cheng Y (2018) A novel bearing multi-fault diagnosis approach based on weighted permutation entropy and an improved SVM ensemble classifier. Sensors (Switzerland) 18(6):1–23

Title: A review on fault detection and diagnosis techniques: basics and beyond
Authors: Anam Abid
Muhammad Tahir Khan
Javaid Iqbal
Publication date: 10-11-2020
Publisher: Springer Netherlands
Published in: Artificial Intelligence Review / Issue 5/2021
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-020-09934-2

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