Abstract
Industrial systems operating under harsh and stochastic conditions are vulnerable to anomalies that degrade its performance and subsequently lead to unexpected breakdown. With the advent of Internet of Things (IoT), intelligent sensors have enabled maintenance managers to collect system data and analyze its behavior accurately in real-time. Based on this, many data-driven early fault detection approaches have been developed that try to detect anomalies associated with impending faults. Despite its advantages, only limited and scattered applications of anomaly detection approaches can be seen in system health monitoring of mechanical systems. One of the possible reasons could be scarcity of a comprehensive literature review that presents evolution of the field, highlighting key challenges and open questions to be addressed for future developments. This study narrows this gap by presenting a state-of-art review of data-driven approaches employed to early fault detection of various industrial systems. After critical analysis, challenges found in the previous studies and open questions for future research are also discussed. This study can be a reference point for researchers interested in addressing the critical challenges faced by maintenance practitioners in the industry.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abboud D, Antoni J (2017) Order-frequency analysis of machine signals. Mech Syst Signal Process 87:229–258. https://doi.org/10.1016/j.ymssp.2016.10.024
Abboud D, Antoni J, Sieg-Zieba S, Eltabach M (2017) Envelope analysis of rotating machine vibrations in variable speed conditions: a comprehensive treatment. Mech Syst Signal Process 84:200–226. https://doi.org/10.1016/j.ymssp.2016.06.033
Abboud D, Elbadaoui M, Smith WA, Randall RB (2019) Advanced bearing diagnostics: a comparative study of two powerful approaches. Mech Syst Signal Process 114:604–627. https://doi.org/10.1016/j.ymssp.2018.05.011
Abid A, Khan MT, Khan MS (2017) Multidomain features-based GA optimized artificial immune system for bearing fault detection. IEEE Trans Syst Man Cybern Syst 50(1):348-359
Afia A, Rahmoune C, Benazzouz D (2020) New intelligent gear fault diagnosis method based on autogram and radial basis function neural network. Adv Mech Eng 12:1–14. https://doi.org/10.1177/1687814020916593
Agyemang M, Barker K, Alhajj R (2006) A comprehensive survey of numeric and symbolic outlier mining techniques. Intell Data Anal 10:521–538. https://doi.org/10.3233/ida-2006-10604
Akçay H, Türkay S (2019) Power spectrum estimation in innovation models. Mech Syst Signal Process 121:227–245. https://doi.org/10.1016/j.ymssp.2018.11.026
Al Azzawi AD, Moncayo H, Perhinschi MG et al (2016) Comparison of immunity-based schemes for aircraft failure detection and identification. Eng Appl Artif Intell 52:181–193. https://doi.org/10.1016/j.engappai.2016.02.017
Allegorico C, Mantini V (2014) A data-driven approach for on-line gas turbine combustion monitoring using classification models. In: PHM society european conference (Vol 2, No 1)
Alvarez EJ, Ribaric AP (2017) An improved-accuracy method for fatigue load analysis of wind turbine gearbox based on SCADA. Renew Energy. https://doi.org/10.1016/j.renene.2017.08.040
Aydemir G, Acar B (2020) Anomaly monitoring improves remaining useful life estimation of industrial machinery. J Manuf Syst 56:463–469
Bai M, Liu J, Chai J et al (2019) Anomaly detection of gas turbines based on normal pattern extraction. Appl Therm Eng. https://doi.org/10.1016/j.applthermaleng.2019.11466
Barke D, Chiu KW (2005) Structural health monitoring in the railway industry: a review. Struct Heal Monit 4:81–94. https://doi.org/10.1177/1475921705049764
Bernal E, Spiryagin M, Cole C (2018) Onboard condition monitoring sensors, systems and techniques for freight railway vehicles : a review. IEEE Sens J. https://doi.org/10.1109/JSEN.2018.2875160
Bonab H (2019) Less is more: a comprehensive framework for the number of components of ensemble classifiers. IEEE Trans Neural Networks Learn Syst. https://doi.org/10.1109/TNNLS.2018.2886341
Brotherton T, Jahns G, Jacobs J, Wroblewski D (2000) Prognosis of faults in gas turbine engines. IEEE Aerosp Conf Proc 6:163–172. https://doi.org/10.1109/AERO.2000.877892
Bulusu S, Member S, Member BK (2020) Anomalous example detection in deep learning: a survey. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3010274
Castellani F, Garibaldi L, Daga AP et al (2020) Diagnosis of faulty wind turbine bearings using tower vibration measurements. Energies. https://doi.org/10.3390/en13061474
Chalapathy R, Chawla S (2019) Deep learning for anomaly detection: a survey. arXiv preprint: arXiv:1901.03407
Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):1–58
Chen Y, Su S, Yang H (2020) Convolutional neural network. Analysis 30:1–13. https://doi.org/10.1142/S0218127420500029
Cheng Y, Zhu H, Wu J, Shao X (2018) Kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2018.2866549
Chinakay P, Wongsa S (2016) A PCA-based fault monitoring of splitter nozzles in gas turbine combustion chamber using exhaust gas temperature. In: International conference on instrumentation, control and automation (ICA), pp 120–125
Choi H, Kim C, Kwon D (2020) Data-driven fault diagnosis based on coal-fired power plant operating data. J Mech Sci Technol 34:1–6. https://doi.org/10.1007/s12206-020-2202-0
Cohen J, Li J (2021) A semi-supervised multiclass anomaly detection approach for partially labeled in-process measurement data. In: Proceedings of the ASME 2021 16th international manufacturing science and engineering conference. Volume 2: manufacturing processes; manufacturing systems; Nano/Micro/Meso Manufacturing; Quality and Reliability
Cui Y, Bangalore P, Tjernberg LB (2018) An anomaly detection approach based on machine learning and SCADA data for condition monitoring of wind turbines. In: 2018 IEEE international conference on probabilistic methods applied to power systems (PMAPS). IEEE, pp 1–6
Dao PB, Staszewski WJ, Barszcz T, Uhl T (2017) Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data. Renew Energy. https://doi.org/10.1016/j.renene.2017.06.089
Duan Z, Wu T, Guo S et al (2018) Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review. Int J Adv Manuf Technol 96:803–819
Eddine RC, Slimane B (2016) Detection of bearing defects using Hilbert envelope analysis and fast Kurtogram demodulation method. J Electr Syst 16:92–104
Erhan L, Ndubuaku M, Di MM et al (2020) Smart anomaly detection in sensor systems: a multi-perspective review. Inf Fusion. https://doi.org/10.1016/j.inffus.2020.10.001
Fang R, Wang Y, Shang R et al (2016) ScienceDirect The ultra-short term power prediction of wind farm considering operational condition of wind turbines. Int J Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2016.03.173
Gerber T, Martin N, Mailhes C (2015) Time-frequency tracking of spectral structures estimated by a data-driven method. IEEE Trans Ind Electron 62:6616–6626. https://doi.org/10.1109/TIE.2015.2458781
Ghadi MJ, Gilani SH, Afrakhte H, Baghramian A (2014) Electrical power and energy systems a novel heuristic method for wind farm power prediction : a case study. Int J Electr Power Energy Syst 63:962–970. https://doi.org/10.1016/j.ijepes.2014.07.008
Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61:3999–4010. https://doi.org/10.1109/TSP.2013.2265222
Gong M, Tang D, Yu J, Tian L (2021) A physics-informed transfer learning approach for anomaly detection of aerospace cmg with limited telemetry data. Glob Reliab Progn Heal Manag. https://doi.org/10.1109/PHM-Nanjing52125.2021.9612988
Gribbestad M, Hassan MU, Hameed IA, Sundli K (2021) Health monitoring of air compressors using reconstruction-based deep learning for anomaly detection with increased transparency. Entropy 23(1):83
Gryllias K, Moschini S, Antoni J (2018) Application of cyclo-nonstationary indicators for bearing monitoring under varying operating conditions. J Eng Gas Turbines Power. https://doi.org/10.1115/1.4037638
Hasani RM, Wang G, Grosu R (2017) An automated auto-encoder correlation-based health-monitoring and prognostic method for machine bearings. arXiv preprint: arXiv:1703.06272
Hernandez-Vargas M, Cabal-Yepez E, Garcia-Perez A (2014) Real-time SVD-based detection of multiple combined faults in induction motors. Comput Electr Eng 40:2193–2203. https://doi.org/10.1016/j.compeleceng.2013.12.020
Hodge VJ, Austin JIM (2004) A survey of outlier detection methodologies. Artif Intell Rev 22:85–126
Huaitao S, Lei G, Tan Shuai BX (2019) Rolling bearing initial fault detection using long short-term memory recurrent network. IEEE Access 7:171559–171569. https://doi.org/10.1109/ACCESS.2019.2954091
Hundi P, Shahsavari R (2020) Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants. Appl Energy 265:114775
Jiang W, Cheng C, Zhou B, Yuan Y (2019) A novel GAN-based fault diagnosis approach for imbalanced industrial time series. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2944689
Jiang Qinyu CF (2020) A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection. J Mech Sci Technol. https://doi.org/10.1007/s12206-020-0808-x
Jiménez A, Marquez F, Moraleda V, Murioz C (2018) Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis. Renew Energy. https://doi.org/10.1016/j.renene.2018.08.050
Jin X, Sun Y, Que Z et al (2016) Anomaly detection and fault prognosis for bearings. IEEE Trans Instrum Meas 65:2046–2054
Liu J, Zhu L, Ma Y, Liu J, Zhou W, Yu D (2018) Anomaly detection of hot components in gas turbine based on frequent pattern extraction. Sci China Technol Sci 61(4):567–586
Kanarachos S, Christopoulos SG, Chroneos A, Fitzpatrick ME (2017) Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform. Expert Syst Appl 85:292–304. https://doi.org/10.1016/j.eswa.2017.04.028
Khan S, Yairi T (2018) A review on the application of deep learning in system health management. Mech Syst Signal Process 107:241–265. https://doi.org/10.1016/j.ymssp.2017.11.024
Larios DF, Personal E, Parejo A et al (2020) Operational simulation environment for SCADA integration of renewable resources. Energies 13(6):1333. https://doi.org/10.3390/en13061333
Leahy K, Hu RL, Konstantakopoulos IC, Spanos CJ, Agogino AM (2016) Diagnosing wind turbine faults using machine learning techniques applied to operational data. In: IEEE International conference on prognostics and health management (icphm), pp 1–8
Lee J, Wu F, Zhao W et al (2013) Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2013.06.004
Leite VCMN, Borges Da Silva JG, Veloso GFC et al (2015) Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current. IEEE Trans Ind Electron 62:1855–1865. https://doi.org/10.1109/TIE.2014.2345330
Li Z (2019) A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment. Int J Adv Manuf Technol 103:499–510
Li Z, Fang H, Huang M et al (2018) Data-driven bearing fault identification using improved hidden Markov model and self-organizing map. Comput Ind Eng 116:37–46. https://doi.org/10.1016/j.cie.2017.12.002
Li F, Zhou G, Li X, Zhu L, Wang H (2017) A symbolic reasoning based anomaly detection for gas turbine subsystems. In: Prognostics and system health management conference (PHM-Harbin), pp 1–8
Li D, Chen D, Goh J, Ng SK (2018) Anomaly detection with generative adversarial networks for multivariate time series. arXiv preprint: arXiv:1809.04758
Lin Z, Liu X, Collu M (2020) Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks. Int J Electr Power Energy Syst 118:105835
Liu C, Gryllias K (2020) A semi-supervised Support Vector Data Description-based fault detection method for rolling element bearings based on cyclic spectral analysis. Mech Syst Signal Process 140:106682
Liu L, Guo Q (2019) Data-driven remaining useful life prediction considering sensor anomaly detection and data recovery. IEEE Access 7:58336–58345. https://doi.org/10.1109/ACCESS.2019.2914236
Liu H, Wang Y, Chen W (2019) Anomaly detection for condition monitoring data using auxiliary feature vector and density-based clustering. IET Gener Transm Distrib. https://doi.org/10.1049/iet-gtd.2019.0682
Liu X, Lu S, Ren Y, Wu Z (2020) Wind turbine anomaly detection based on SCADA. Electronics. https://doi.org/10.3390/electronics9050751
Lu F, Li Z, Huang J (2020a) Hybrid state estimation for aircraft engine anomaly detection and fault accommodation. AIAA J 2514(1):J059044
Lu W, Li Y, Cheng Y, Meng D, Liang B, Zhou P (2018a) Early fault detection approach with deep architectures. IEEE Trans Instrum Meas 67(7):1679–1689
Lu Y, Xie R, Liang SY (2018b) Detection of weak fault using sparse empirical wavelet transform for cyclic fault. Int J Adv Manuf Technol 99(5):1195-1201
Lu Y, Xie R, Liang SY (2020) CEEMD-assisted kernel support vector machines for bearing diagnosis. Int J Adv Manuf Technol 106(7):3063–3070
Luo S, Cheng J, Fu J (2015) Application of self-adaptive wavelet ridge demodulation method based on LCD to incipient fault diagnosis. Shock Vib. https://doi.org/10.1155/2015/735853
Luo H, Zhong S (2017) Gas turbine engine gas path anomaly detection using deep learning with Gaussian distribution. In: Prognostics and system health management conference (PHM-Harbin). IEEE, pp 1–6
Manobel B, Sehnke F, Lazzús JA et al (2018) Wind turbine power curve modeling based on Gaussian processes and artificial neural networks. Renew Energy. https://doi.org/10.1016/j.renene.2018.02.081
Mao W, Tian S, Fan J et al (2020) Online detection of bearing incipient fault with semi-supervised architecture and deep feature representation. J Manuf Syst 55(179):198
Marugán AP, Chacón AMP, Márquez FPG (2019) Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines. Reliab Eng Syst Saf 191:106574
Montechiesi L, Cocconcelli M, Rubini R (2015) Artificial immune system via Euclidean distance minimization for anomaly detection in bearings. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2015.04.017
Morshedizadeh M, Kordestani M, Carriveau R et al (2017) Improved power curve monitoring of wind turbines. Wind Eng. https://doi.org/10.1177/0309524X17709730
Moustafa N, Hu J, Slay J (2019) AC SC. J Netw Comput Appl. https://doi.org/10.1016/j.jnca.2018.12.006
Nordenstrom K (2005) Forced outages and MRO costs reduced with PdM, https://www.plantservices.com/predictive-maintenance/shaft-alignment/article/11343798/forced-outages-and-mro-costs-reduced-with-pdm
Park J, Kim S, Choi JH, Lee SH (2021) Frequency energy shift method for bearing fault prognosis using microphone sensor. Mech Syst Signal Process 147:107068
Patil A, Soni G, Prakash A, Karwasra K (2021a) Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches. Int J Qual Reliab Manag. https://doi.org/10.1108/IJQRM-04-2021-0105
Patil A, Soni G, Prakash A, Ram M (2021b) Intelligent valve fault diagnosis approach for reciprocating compressor based on acoustic signals. Reliab Theory Appl 16:35–47. https://doi.org/10.24412/1932-2321-2021-264-35-47
Patton R, Frank P, Clark R (2013) Issues of fault diagnosis for dynamic systems. Springer, London
Pariaman H, Luciana GM, Wisyaldin MK, Hisjam M (2021) Anomaly detection using LSTM-Autoencoder to predict coal pulverizer condition on Coal-fired power plant
Pimentel MAF, Clifton DA, Clifton L, Tarassenko L (2014) A review of novelty detection. Signal Process. https://doi.org/10.1016/j.sigpro.2013.12.026
Qian P, Zhang D, Tian X et al (2019) A novel wind turbine condition monitoring method based on cloud computing. Renew Energy 135:390–398. https://doi.org/10.1016/j.renene.2018.12.045
Qu F, Liu J, Zhu H, Zang D (2019) Assembled multidimensional membership. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2019.2957409
Ramachandran S, Belardi C (2021) Case-Based Reasoning for System Anomaly Detection and Management. In: IEEE Aerospace Conference (50100). IEEE, pp 1–9
Ritter G, Gallegos MT (1997) Outliers in statistical pattern recognition and an application to automatic chromosome classification. Pattern Recognit Lett 18:525–539. https://doi.org/10.1016/S0167-8655(97)00049-4
Riveiro M, Pallotta G, Vespe M (2018) Maritime anomaly detection: a review. Wires Data Mining Knowl Discov. https://doi.org/10.1002/widm.1266
Rodríguez-gonzálvez P, Rodríguez-martín M (2019) Weld bead detection based on 3D geometric features and machine learning approaches. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2891367
Salgado-plasencia E, Carrillo-serrano RV, Rivas-araiza EA, Toledano-ayala M (2019) SCADA-based heliostat control system with a fuzzy logic controller for the heliostat orientation. Appl Sci. https://doi.org/10.3390/app915296
Saucedo-dorantes JJ, Delgado-prieto M, Osornio-rios RA et al (2020) industrial data-driven monitoring based on incremental learning applied to the detection of novel faults. IEEE Trans Ind Inform 16:5985–5995. https://doi.org/10.1109/TII.2020.2973731
Schmidt S, Heyns PS, Gryllias KC (2019) A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions. Mech Syst Signal Process 116:40–61. https://doi.org/10.1016/j.ymssp.2018.06.026
Sequeira C, Pacheco A, Pedro MG et al (2018) Analysis of the efficiency of wind turbine gearboxes using the temperature variable. Renew Energy 135:465–472. https://doi.org/10.1016/j.renene.2018.12.040
Simani S, Alvisi S (2019) Data-driven control techniques for renewable energy conversion systems: wind turbine and hydroelectric plants. Electronics 8(2):237. https://doi.org/10.3390/electronics8020237
Skurichina M, Duin RPW (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5:121–135
Sochen R, Habetler T, Kamran F, Bartheld R (1995) Motor bearing damage detection using stator current monitoring. IEEE Trans Ind Appl 55:110–116
Sun P, Li J, Wang C, Lei X (2016) A generalized model for wind turbine anomaly identification based on SCADA data. Appl Energy 168:550–567. https://doi.org/10.1016/j.apenergy.2016.01.133
Tahan M, Tsoutsanis E, Muhammad M, Abdul Karim ZA (2017) Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: a review. Appl Energy 198:122–144. https://doi.org/10.1016/j.apenergy.2017.04.048
Tautz-weinert J, Watson SJ (2016) Using SCADA data for wind turbine condition monitoring – a review. IET Renew Power Gener 11:382–394
Tchakoua P, Wamkeue R, Ouhrouche M et al (2014) Wind turbine condition monitoring: state-of-the-art review, new trends, and future challenges. Energies. https://doi.org/10.3390/en7042595
Trofimov AG, Kuznetsova KE, Korshikova AA (2019) Abnormal operation detection in heat power plant using ensemble of binary classifiers. In: Advances in neural computation, machine learning, and cognitive research II. Springer, Cham
Volponi AJ (2014) Gas turbine engine health management: Past, present, and future trends. J Eng Gas Turbines Power 136:1–20. https://doi.org/10.1115/1.4026126
Wang Y, Infield DG, Stephen B, Galloway SJ (2014) Copula-based model for wind turbine power curve outlier rejection. Engineering. https://doi.org/10.1002/we.661
Wang L, Member S, Zhang Z et al (2016) Wind turbine gearbox failure identification with deep neural networks. IEEE Trans. Ind. Inf. 3203:1–9. https://doi.org/10.1109/TII.2016.2607179
Wang T, Lu G, Yan P (2019a) Multi-sensors based condition monitoring of rotary machines: an approach of multidimensional time-series analysis. Measurement 134:326–335. https://doi.org/10.1016/j.measurement.2018.10.089
Wang Z, Gu Y, Han X, Zhu J (2019b) Anomaly detection for heavy power generation gas turbine considering the effect of output power variation. Proc Inst Mech Eng Part A J Power Energy. https://doi.org/10.1177/0957650919879610
Wang M, Zhou D, Chen M, Wang Y (2020) Control engineering practice anomaly detection in the fan system of a thermal power plant monitored by continuous and two-valued variables. Control Eng Pract 102:104522. https://doi.org/10.1016/j.conengprac.2020.104522
Xin G, Hamzaoui N, Antoni J (2018) Semi-automated diagnosis of bearing faults based on a hidden Markov model of the vibration signals. Meas J Int Meas Confed 127:141–166. https://doi.org/10.1016/j.measurement.2018.05.040
Xiuyao S, Mingxi W, Jermaine C, Ranka S (2007) Conditional anomaly detection. IEEE Trans Knowl Data Eng 19:631–644. https://doi.org/10.1109/TKDE.2007.1009
Yan H, Sun J, Zuo H (2020) Anomaly detection based on multivariate data for the aircraft hydraulic system. Proc Inst Mech Eng Part I J Syst Control Eng. https://doi.org/10.1177/0959651820954577
Yan W (2016) One-class extreme learning machines for gas turbine combustor anomaly detection. In: International joint conference on neural networks (ijcnn), pp 2909–2914
Yang Y, Dong XJ, Peng ZK et al (2015) Vibration signal analysis using parameterized time-frequency method for features extraction of varying-speed rotary machinery. J Sound Vib 335:350–366. https://doi.org/10.1016/j.jsv.2014.09.025
Yang B, Liu R, Chen X (2017) Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD. IEEE Trans Ind Inform 13:1321–1331. https://doi.org/10.1109/TII.2017.2662215
Yoo Y (2020) Computers in Industry Data-driven fault detection process using correlation based clustering. Comput Ind 122:103279. https://doi.org/10.1016/j.compind.2020.103279
Yu W, Zhao C (2019) Online fault diagnosis for industrial processes with Bayesian network-based probabilistic. IEEE Trans Autom Sci Eng 16:1922–1932. https://doi.org/10.1109/TASE.2019.2915286
Yuan M, Wu Y, Lin L (2016) Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. In: IEEE international conference on aircraft utility systems. pp 135–140
Zeng XJ, Yang M, Bo YF (2020) Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation. Int J Electr Power Energ Syst 123:106233
Zhang Y, Bingham C, Garlick M, Gallimore M (2016) Applied fault detection and diagnosis for industrial gas turbing systems. Int J Autom Comput. https://doi.org/10.1007/s11633-016-0967-5
Zhang Y, Lu W, Chu F (2017) Planet gear fault localization for wind turbine gearbox using acoustic emission signals. Renew Energy 109:449–460. https://doi.org/10.1016/j.renene.2017.03.035
Zhang D, Entezami M, Stewart E et al (2018a) Adaptive fault feature extraction from wayside acoustic signals from train bearings. J Sound Vib 425:221–238. https://doi.org/10.1016/j.jsv.2018.04.004
Zhang S, He Q, Ouyang K, Xiong W (2018b) Multi-bearing weak defect detection for wayside acoustic diagnosis based on a time-varying spatial filtering rearrangement. Mech Syst Signal Process 100:224–241. https://doi.org/10.1016/j.ymssp.2017.06.035
Zhang X, Liu Z, Miao Q, Wang L (2018c) Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time–frequency atom dictionary. Mech Syst Signal Process 107:29–42. https://doi.org/10.1016/j.ymssp.2018.01.027
Zhang Y, Dong ZY, Kong W (2019a) A composite anomaly detection system for data-driven power plant condition monitoring. IEEE Trans Ind Inform. https://doi.org/10.1109/TII.2019a.2945366
Zhang Y, Li M, Dong ZY, Meng K (2019b) Probabilistic anomaly detection approach for data-driven wind turbine condition monitoring. J Mater 5:149–158. https://doi.org/10.17775/CSEEJPES.2019.00010
Zhao N, Wen X (2017) A review on gas turbine anomaly detection for implementing health management. In: ASME Turbo Expo 2016: turbomachinery technical conference and exposition. pp 1–14
Zhu J, Yoon J, He D, Bechhoefer E (2015) Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines. Wind Energy. https://doi.org/10.1002/we.1746
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
Informed consent
We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Patil, A., Soni, G. & Prakash, A. Data-driven approaches for impending fault detection of industrial systems: a review. Int J Syst Assur Eng Manag 15, 1326–1344 (2024). https://doi.org/10.1007/s13198-022-01841-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-022-01841-9