Skip to main content
Springer Nature Link
Log in

Role of artificial intelligence in rotor fault diagnosis: a comprehensive review

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Artificial intelligence (AI)-based rotor fault diagnosis (RFD) poses a variety of challenges to the prognostics and health management (PHM) of the Industry 4.0 revolution. Rotor faults have drawn more attention from the AI research community in terms of utilizing fault-specific characteristics in its feature engineering, compared to any other rotating machinery faults. While the rotor faults, specifically structural rotor faults (SRF), have proven to be the root cause of most of the rotating machinery issues, the research in this field largely revolves around bearing and gear faults. Within this scenario, this paper is the first of its kind to attempt to review and define the role of AI in RFD and provides an all-encompassing review of rotor faults for the researchers and academics. In addition, this study is unique in three ways: (i) it emphasizes the use of fault-specific characteristic features with AI, (ii) it is grounded in fault-wise analysis rather than component-wise analysis with appropriate fault categorization, and (iii) it portrays the current research and analysis in accordance with different phases of an AI-based RFD framework. Finally, the section on future research directions is aimed at bridging the gap between a laboratory-based solution and a real-world industrial solution for RFD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Al-Bedoor B (2001) Modeling the coupled torsional and lateral vibrations of unbalanced rotors. Comput Methods Appl Mech Eng 190(45):5999–6008

    MATH  Google Scholar 

  • Alsalaet J (2012) Vibration analysis and diagnostic guide. http://www.jaafarali.info/files/vibration_analysis_guide.pdf. Accessed: 2019-09-30

  • Angelopoulos A, Michailidis ET, Nomikos N, Trakadas P, Hatziefremidis A, Voliotis S, Zahariadis T (2020) Tackling faults in the industry4.0 era—a survey of machine-learning solutions and key aspects. Sensors 20(1):109

    Google Scholar 

  • Armaki MG, Roshanfekr R (2010) A new approach for fault detection of broken rotor bars in induction motor based on support vector machine. In: Proceedings—2010 18th Iranian Conference on Electrical Engineering, ICEE 2010 pp 732–738, https://doi.org/10.1109/IRANIANCEE.2010.5506976

  • Aydin I, Karaköse M, Akin E (2007) Artificial immune based support vector machine algorithm for fault diagnosis of induction motors. In: International Aegean Conference on Electrical Machines and Power Electronics and Electromotion ACEMP’07 and Electromotion’07 Joint Conference pp 217–221, https://doi.org/10.1109/ACEMP.2007.4510505

  • Babu NT, Aravind A, Rakesh A, Jahzan M, Prabha RD, Ramalinga Viswanathan M (2018) Automatic fault classification for journal bearings using ANN and DNN. Arch Acoust 43(4):727–738. https://doi.org/10.24425/aoa.2018.125166

    Article  Google Scholar 

  • Baccarini LMR, Rocha E, Silva VV, De Menezes BR, Caminhas WM (2011) SVM practical industrial application for mechanical faults diagnostic. Expert Syst Appl 38(6):6980–6984. https://doi.org/10.1016/j.eswa.2010.12.017

    Article  Google Scholar 

  • Bachschmid N, Pennacchi P, Vania A (2002) Identification of multiple faults in rotor systems. J Sound Vib 254(2):327–366

    Google Scholar 

  • Bate GH (1987) Vibration diagnostics for industrial electric motor drives. In: Application Notes, Bruel & Kjaer, Nacrum

  • Biet M (2013) Rotor faults diagnosis using feature selection and nearest neighbors rule: application to a turbogenerator. IEEE Trans Ind Electron 60(9):4063–4073. https://doi.org/10.1109/TIE.2012.2218559

    Article  Google Scholar 

  • Biet M, Bijeire A (2011) Rotor faults diagnosis in synchronous generators using feature selection and nearest neighbors rule. In: SDEMPED 2011—8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives pp 300–306, https://doi.org/10.1109/DEMPED.2011.6063640

  • Bin GF, Gao JJ, Li XJ, Dhillon BS (2012) Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mech Syst Signal Process 27(1):696–711. https://doi.org/10.1016/j.ymssp.2011.08.002

    Article  Google Scholar 

  • Bo L, Jin Y, Chen Y, Fan X (2017) The study on rotating machinery fault diagnosis based on deep neural networks. In: Proceedings—2016 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2016 9:125–129, https://doi.org/10.1109/ICITBS.2016.67

  • Bognatz S (1995) Alignment of critical and non critical machines. Orbit 4:23–25

    Google Scholar 

  • Bonnett AH (2000) Root cause ac motor failure analysis with a focus on shaft failures. IEEE Trans Ind Appl 36(5):1435–1448

    Google Scholar 

  • Cabal-Yepez E, Fernandez-Jaramillo AA, Garcia-Perez A, Romero-Troncoso RJ, Lozano-Garcia JM (2015) Real-time condition monitoring on vsd-fed induction motors through statistical analysis and synchronous speed observation. Int Trans Electr Energy Syst 25(8):1657–1672

    Google Scholar 

  • Cacciola S, Agud IM, Bottasso CL (2016) Detection of rotor imbalance, including root cause, severity and location. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/753/7/072003

    Article  Google Scholar 

  • Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    MATH  Google Scholar 

  • Chen CS, Chen JS (2011) Rotor fault diagnosis system based on sGA-based individual neural networks. Expert Syst Appl 38(9):10822–10830. https://doi.org/10.1016/j.eswa.2011.02.074

    Article  Google Scholar 

  • Chen P (2009) Foundation and application of condition diagnosis technology for rotating machinery. San* eisha publication, Japan

  • Chen WY, Xu JX, Panda SK (2011) A study on automatic machine condition monitoring and fault diagnosis for bearing and unbalanced rotor faults. In: Proceedings—ISIE 2011: 2011 IEEE International Symposium on Industrial Electronics pp 2105–2110, https://doi.org/10.1109/ISIE.2011.5984486

  • Chen X, Wang S, Qiao B, Chen Q (2018) Basic research on machinery fault diagnostics: past, present, and future trends. Front Mech Eng 13(2):264–291

    Google Scholar 

  • Chen Z, Li Z (2017) Research on fault diagnosis method of rotating machinery based on deep learning. 2017 Prognostics and System Health Management Conference, PHM-Harbin 2017—Proceedings. https://doi.org/10.1109/PHM.2017.8079279

  • Chow MY, Mangum PM, Yee SO (1991) A neural network approach to real-time condition monitoring of induction motors. IEEE Trans Ind Electron 38(6):448–453

    Google Scholar 

  • Chu F, Lu W (2005) Experimental observation of nonlinear vibrations in a rub-impact rotor system. J Sound Vib 283(3–5):621–643

    Google Scholar 

  • Cui L, Huang J, Zhang F (2017) Quantitative and localization diagnosis of a defective ball bearing based on vertical-horizontal synchronization signal analysis. IEEE Trans Ind Electron 64(11):8695–8706

    Google Scholar 

  • Cunha Palácios RH, Da Silva IN, Goedtel A, Godoy WF (2015) A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electr Power Syst Res 127:249–258. https://doi.org/10.1016/j.epsr.2015.06.008

    Article  Google Scholar 

  • Darpe A, Gupta K, Chawla A (2006) Dynamics of a bowed rotor with a transverse surface crack. J Sound Vib 296(4–5):888–907

    Google Scholar 

  • Devendiran S, Manivannan K (2016) Vibration based condition monitoring and fault diagnosis technologies for bearing and gear components-a review. Int J Appl Eng Res 11(6):3966–3975

    Google Scholar 

  • Duan L, Xie M, Bai T, Wang J (2016a) A new support vector data description method for machinery fault diagnosis with unbalanced datasets. Expert Syst Appl 64:239–246. https://doi.org/10.1016/j.eswa.2016.07.039

    Article  Google Scholar 

  • Duan L, Yao M, Wang J, Bai T, Zhang L (2016b) Segmented infrared image analysis for rotating machinery fault diagnosis. Infrared Phys Technol 77:267–276. https://doi.org/10.1016/j.infrared.2016.06.011

    Article  Google Scholar 

  • El-Shafei A, Hassan TA, Soliman AK, Zeyada Y, Rieger N (2007) Neural network and fuzzy logic diagnostics of 1x faults in rotating machinery. J Eng Gas Turbines Power 129(3):703–710. https://doi.org/10.1115/1.2227417

    Article  Google Scholar 

  • Fahy F, Thompson D (2016) Fundamentals of sound and vibration. CRC Press, Boca Raton. https://doi.org/10.1201/b18348

    Book  Google Scholar 

  • Fang R, Ma H (2006) Application of mcsa and svm to induction machine rotor fault diagnosis. In: 2006 6th World Congress on Intelligent Control and Automation, IEEE, vol 2, pp 5543–5547

  • Fatima S, Guduri B, Mohanty AR, Naikan VNA (2014) Transducer invariant multi-class fault classification in a rotor-bearing system using support vector machines. Meas J Int Meas Conf 58:363–374

    Google Scholar 

  • Fei CW, Bai GC, Tang WZ, Ma S (2014) Quantitative diagnosis of rotor vibration fault using process power spectrum entropy and support vector machine method. Shock Vib 20:4. https://doi.org/10.1155/2014/957531

    Article  Google Scholar 

  • Fengqi W, Meng G (2006) Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM. Mech Syst Signal Process 20(8):2007–2021. https://doi.org/10.1016/j.ymssp.2005.10.004

    Article  Google Scholar 

  • Gang N, Son JD, Widodo A, Yang BS, Hwang DH, Kang DS (2007) A comparison of classifier performance for fault diagnosis of induction motor using multi-type signals. Struct Health Monitor 6(3):215–229. https://doi.org/10.1177/1475921707081110

    Article  Google Scholar 

  • Gangsar P, Tiwari R (2019) A support vector machine based fault diagnostics of induction motors for practical situation of multi-sensor limited data case. Measurement 135:694–711

    Google Scholar 

  • Glowacz A (2018) Acoustic based fault diagnosis of three-phase induction motor. Appl Acoust 137(February):82–89. https://doi.org/10.1016/j.apacoust.2018.03.010

    Article  Google Scholar 

  • Godoy WF, da Silva IN, Goedtel A, Palácios RHC, Lopes TD (2016) Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter. IET Electr Power Appl 10(5):430–439. https://doi.org/10.1049/iet-epa.2015.0469

    Article  Google Scholar 

  • Gohari M, Eydi AM (2020) Modelling of shaft unbalance: modelling a multi discs rotor using k-nearest neighbor and decision tree algorithms. Measurement 151:107253

    Google Scholar 

  • Guan Z, Liao Z, Li K, Chen P (2019) A precise diagnosis method of structural faults of rotating machinery based on combination of empirical mode decomposition, sample entropy, and deep belief network. Sens Switzerland. https://doi.org/10.3390/s19030591

    Article  Google Scholar 

  • Guo S, Yang T, Gao W, Zhang C (2018) A novel fault diagnosis method for rotating machinery based on a convolutional neural network. Sens Switzerland 18(5):1. https://doi.org/10.3390/s18051429

    Article  Google Scholar 

  • Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157–1182

    MATH  Google Scholar 

  • Han T, Liu C, Wu L, Sarkar S, Jiang D (2019a) An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems. Mech Syst Signal Process 117:170–187. https://doi.org/10.1016/j.ymssp.2018.07.048

    Article  Google Scholar 

  • Han T, Liu C, Yang W, Jiang D (2019b) A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2018.12.019

    Article  Google Scholar 

  • Hariharan V, Srinivasan P (2009) Vibration analysis of misaligned shaft vibration analysis of misaligned shaft-ball bearing system ball bearing system ball bearing system. Indian J Sci Technol 2(9)

  • Hassan T, El-Shafei A, Zeyada Y, Rieger N (2003) Comparison of neural network architectures for machinery fault diagnosis. In: ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference, American Society of Mechanical Engineers Digital Collection, pp 415–424

  • He H, Bai Y, Garcia EA, Li S (2008) Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), IEEE, pp 1322–1328

  • Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M (2016) Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans Ind Electron 63(11):7067–7075. https://doi.org/10.1109/TIE.2016.2582729

    Article  Google Scholar 

  • Ishibashi T, Han B, Kawai T (2017) Rotating machinery library for diagnosis. In: Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15–17, 2017, Linköping University Electronic Press, 132, pp 381–387

  • Janssens O, Schulz R, Slavkovikj V, Stockman K, Loccufier M, Van De Walle R, Van Hoecke S (2015) Thermal image based fault diagnosis for rotating machinery. Infrared Phys Technol 73:78–87. https://doi.org/10.1016/j.infrared.2015.09.004

    Article  Google Scholar 

  • Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, Van de Walle R, Van Hoecke S (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345. https://doi.org/10.1016/j.jsv.2016.05.027

    Article  Google Scholar 

  • Jeong H, Park S, Woo S, Lee S (2016) Rotating machinery diagnostics using deep learning on orbit plot images. Proc Manuf 5:1107–1118. https://doi.org/10.1016/j.promfg.2016.08.083

    Article  Google Scholar 

  • Jiang Q, Shen Y, Li H, Xu F (2018) New fault recognition method for rotary machinery based on information entropy and a probabilistic neural network. Sens Switzerland. https://doi.org/10.3390/s18020337

    Article  Google Scholar 

  • Kang M, Kim JM (2013) Singular value decomposition based feature extraction approaches for classifying faults of induction motors. Mech Syst Signal Process 41(1–2):348–356. https://doi.org/10.1016/j.ymssp.2013.08.002

    Article  Google Scholar 

  • Karvelis P, Georgoulas G, Tsoumas IP, Antonino-Daviu JA, Climente-Alarcón V, Stylios CD (2015) A symbolic representation approach for the diagnosis of broken rotor bars in induction motors. IEEE Trans Ind Informatics 11(5):1028–1037. https://doi.org/10.1109/TII.2015.2463680

    Article  Google Scholar 

  • Keskes H, Braham A, Lachiri Z (2013) Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM. Electr Power Syst Res 97:151–157. https://doi.org/10.1016/j.epsr.2012.12.013

    Article  Google Scholar 

  • Konar P, Sil J, Chattopadhyay P (2015) Knowledge extraction using data mining for multi-class fault diagnosis of induction motor. Neurocomputing 166:14–25. https://doi.org/10.1016/j.neucom.2015.04.040

    Article  Google Scholar 

  • Kong X, Peng G, Li X, Wang Z, Na X (2019) Aeroengine Fault Diagnosis Method Based on Stack Denoising Auto-Encoders Network. In: Proceedings—2018 11th International Congress on Image and Signal Processing. BioMedical Engineering and Informatics, CISP-BMEI 2018:1–9. https://doi.org/10.1109/CISP-BMEI.2018.8633128

  • Kurek J, Osowski S (2010) Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor. Neural Comput Appl 19(4):557–564. https://doi.org/10.1007/s00521-009-0316-5

    Article  Google Scholar 

  • Langarica S, Ruffelmacher C, Nunez F (2019a) An Industrial Internet Application for Real-Time Fault Diagnosis in Industrial Motors. IEEE Transactions on Automation Science and Engineering pp 1–12, https://doi.org/10.1109/TASE.2019.2913628

  • Langarica S, Rüffelmacher C, Núñez F (2019b) An industrial internet application for real-time fault diagnosis in industrial motors. IEEE Transactions on Automation Science and Engineering

  • Lee GY, Kim M, Quan YJ, Kim MS, Kim TJY, Yoon HS, Min S, Kim DH, Mun JW, Oh JW et al (2018) Machine health management in smart factory: a review. J Mech Sci Technol 32(3):987–1009

    Google Scholar 

  • Lee YO, Jo J, Hwang J (2017) Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection. In: Proceedings—2017 IEEE International Conference on Big Data, Big Data 2017 2018-January:3248–3253, https://doi.org/10.1109/BigData.2017.8258307

  • Lees A, Sinha J, Friswell M (2009) Model-based identification of rotating machines. Mech Syst Signal Process 23(6):1884–1893

    Google Scholar 

  • Lei J, Liu C, Jiang D (2019) Fault diagnosis of wind turbine based on long short-term memory networks. Renew Energy 133:422–432. https://doi.org/10.1016/j.renene.2018.10.031

    Article  Google Scholar 

  • Lei Y, He Z, Zi Y, Hu Q (2007) Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mech Syst Signal Process 21(5):2280–2294. https://doi.org/10.1016/j.ymssp.2006.11.003

    Article  Google Scholar 

  • Lei Y, He Z, Zi Y (2008) A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst Appl 35(4):1593–1600. https://doi.org/10.1016/j.eswa.2007.08.072

    Article  Google Scholar 

  • Lei Y, Jia F, Lin J, Xing S, Ding SX (2016a) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 63(5):3137–3147. https://doi.org/10.1109/TIE.2016.2519325

    Article  Google Scholar 

  • Lei Y, Jia F, Lin J, Xing S, Ding SX (2016b) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 63(5):3137–3147. https://doi.org/10.1109/TIE.2016.2519325

    Article  Google Scholar 

  • Li W, Tsai Y, Chiu C (2004a) The experimental study of the expert system for diagnosing unbalances by ann and acoustic signals. J Sound Vib 272(1–2):69–83

    Google Scholar 

  • Li W, Tsai YP, Chiu CL (2004b) The experimental study of the expert system for diagnosing unbalances by ANN and acoustic signals. J Sound Vib 272(1–2):69–83. https://doi.org/10.1016/S0022-460X(03)00317-1

    Article  Google Scholar 

  • Li W, Zhu Z, Jiang F, Zhou G, Chen G (2015a) Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method. Mech Syst Signal Process 50:414–426

    Google Scholar 

  • Li W, Zhu Z, Jiang F, Zhou G, Chen G (2015b) Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method. Mech Syst Signal Process 50–51:414–426. https://doi.org/10.1016/j.ymssp.2014.05.034

    Article  Google Scholar 

  • Li X, Wang K, Jiang L (2011) The application of AE signal in early cracked rotor fault diagnosis with PWVD and SVM. J Softw 6(10):1969–1976. https://doi.org/10.4304/jsw.6.10.1969-1976

    Article  Google Scholar 

  • Li Z, Pedersen VGB, Wang K, He Y (2018) Fault classification and degradation assessment based on wavelet packet decomposition for rotary machinery. Lecture Notes Electr Eng 451:509–516. https://doi.org/10.1007/978-981-10-5768-7_54

    Article  Google Scholar 

  • Li Z, Wang Y, Wang K (2019) A deep learning driven method for fault classification and degradation assessment in mechanical equipment. Comput Ind 104:1–10. https://doi.org/10.1016/j.compind.2018.07.002

    Article  Google Scholar 

  • Liang P, Deng C, Wu J, Yang Z, Zhu J, Zhang Z (2019) Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. Comput Ind 113:103132. https://doi.org/10.1016/j.compind.2019.103132

    Article  Google Scholar 

  • Liao GP, Gao W, Yang GJ, Guo MF (2019) Hydroelectric generating unit fault diagnosis using 1-D convolutional neural network and gated recurrent unit in small hydro. IEEE Sens J 19(20):9352–9363. https://doi.org/10.1109/JSEN.2019.2926095

    Article  Google Scholar 

  • Liu D, Lai X, Xiao Z, Hu X, Zhang P (2020) Fault diagnosis of rotating machinery based on convolutional neural network and singular value decomposition. Shock and Vibration 2020

  • Liu R, Meng G, Yang B, Sun C, Chen X (2017a) Dislocated time series convolutional neural architecture: an intelligent fault diagnosis approach for electric machine. IEEE Trans Ind Inform 13(3):1310–1320. https://doi.org/10.1109/TII.2016.2645238

    Article  Google Scholar 

  • Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Process 108:33–47

    Google Scholar 

  • Liu Z, Wang J, Duan L, Shi T, Fu Q (2017b) Infrared image combined with cnn based fault diagnosis for rotating machinery. In: Proceedings—2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017 2017-December:137–142, https://doi.org/10.1109/SDPC.2017.35

  • Lobato TH, da Silva RR, da Costa ES, Mesquita AL (2019) An integrated approach to rotating machinery fault diagnosis using, eemd, svm, and augmented data. J Vib Eng Technol pp 1–6

  • Lu J, Ming T, Zhang C (2019a) Simulation research of rotor misalignment fault based on adams. In: IOP Conference Series: Earth and Environmental Science, IOP Publishing, vol 252, p 022148

  • Lu S, Qian G, He Q, Liu F, Liu Y, Wang Q (2019b) Insitu motor fault diagnosis using enhanced convolutional neural network in an embedded system. IEEE Sens J PP(c):1–1. https://doi.org/10.1109/jsen.2019.2911299

    Article  Google Scholar 

  • Ma H, Zhao X, Teng Y, Wen B (2011) Analysis of dynamic characteristics for a rotor system with pedestal looseness. Shock Vib 18(1–2):13–27

    Google Scholar 

  • Cruz MA, Marques Cardoso SAJ (2000) Rotor cage fault diagnosis in three-phase induction motors by extended park’s vector approach. Electr Mach Power Syst 28(4):289–299

    Google Scholar 

  • MacCamhaoil M (2016) Static and dynamic balancing of rigid rotors. Bruel & Kjaer application notes, BO pp 0276–12

  • Mais J (2002) Spectrum analysis the key features of analyzing spectra. https://www.skf.com/binary/tcm:12-113997/CM5118%20EN%20Spectrum%20Analysis.pdf

  • Malik H, Mishra S (2016) Proximal support vector machine (PSVM) based imbalance fault diagnosis of wind turbine using generator current signals. Energy Proc 90(December 2014):593–603. https://doi.org/10.1016/j.egypro.2016.11.228

    Article  Google Scholar 

  • Malik H, Mishra S (2017) Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and Simulink. IET Renew Power Gener 11(6):889–902. https://doi.org/10.1049/iet-rpg.2015.0382

    Article  Google Scholar 

  • Mangasarian OL, Wild EW (2001) Proximal support vector machine classifiers. In: Proceedings KDD-2001: Knowledge discovery and data mining, Citeseer

  • Martin-Diaz I, Morinigo-Sotelo D, Duque-Perez O, De Romero-Troncoso RJ (2017a) Early fault detection in induction motors using adaboost with imbalanced small data and optimized sampling. IEEE Trans Ind Appl 53(3):3066–3075. https://doi.org/10.1109/TIA.2016.2618756

    Article  Google Scholar 

  • Martin-Diaz I, Romero-Troncoso RJ, Morinigo-Sotelo D, Duque-Perez O (2017b) Evaluation of intelligent approaches for motor diagnosis under changing operational conditions. 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines. Power Electronics and Drives (SDEMPED), IEEE, pp 65–71

  • Martin-Diaz I, Morinigo-Sotelo D, Duque-Perez O, Romero-Troncoso RJ (2018) An experimental comparative evaluation of machine learning techniques for motor fault diagnosis under various operating conditions. IEEE Trans Ind Appl 54(3):2215–2224. https://doi.org/10.1109/TIA.2018.2801863

    Article  Google Scholar 

  • Martínez-Morales JD, Palacios-Hernández ER, Campos-Delgado DU (2018) Multiple-fault diagnosis in induction motors through support vector machine classification at variable operating conditions. Electr Eng 100(1):59–73. https://doi.org/10.1007/s00202-016-0487-x

    Article  Google Scholar 

  • Meng Z, Guo X, Pan Z, Sun D, Liu S (2019) Data segmentation and augmentation methods based on raw data using deep neural networks approach for rotating machinery fault diagnosis. IEEE Access 7:79510–79522. https://doi.org/10.1109/ACCESS.2019.2923417

    Article  Google Scholar 

  • Moosavian A, Ahmadi H, Sakhaei B, Labbafi R (2014) Support vector machine and K-nearest neighbour for unbalanced fault detection. J Qual Maint Eng 20(1):65–75. https://doi.org/10.1108/JQME-04-2012-0016

    Article  Google Scholar 

  • Mosterman PJ, Zander J (2016) Industry 4.0 as a cyber-physical system study. Softw Syst Model 15(1):17–29

    Google Scholar 

  • Nakhaeinejad M, Ganeriwala S (2009) Observations on dynamic responses of misalignments. Technologial Notes SpectraQuest Inc

  • Narendiranath Babu T, Aravind A, Rakesh A, Jahzan M, Rama Prabha D (2018) Application of EMD ANN and DNN for self-aligning bearing fault diagnosis. Arch Acoust 43(2):163–175. https://doi.org/10.24425/122364

    Article  Google Scholar 

  • Nguyen NT, Lee HH (2008) Improvement of induction motor fault diagnosis performance by using genetic algorithm-based feature selection. Proc Inst Mech Eng Part C J Mech Eng Sci 222(8):1613–1619. https://doi.org/10.1243/09544062JMES913

    Article  Google Scholar 

  • Nguyen NT, Lee HH (2010) Induction motor fault diagnosis based on the k-nn and optimal feature selection. Int J Electron 97(9):1071–1081

    Google Scholar 

  • Nguyen NT, Lee HH, Kwon JM (2008) Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor. J Mech Sci Technol 22(3):490–496. https://doi.org/10.1007/s12206-007-1036-3

    Article  Google Scholar 

  • Niu G, Han T, Yang BS, Tan ACC (2007) Multi-agent decision fusion for motor fault diagnosis. Mech Syst Signal Process 21(3):1285–1299. https://doi.org/10.1016/j.ymssp.2006.03.003

    Article  Google Scholar 

  • Niu G, Widodo A, Son JD, Yang BS, Hwang DH, Kang DS (2008) Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal. Expert Syst Appl 35(3):918–928. https://doi.org/10.1016/j.eswa.2007.08.024

    Article  Google Scholar 

  • Oh H, Jeon BC, Jung JH, Youn BD (2016) Smart diagnosis of journal bearing rotor systems: Unsupervised feature extraction scheme by deep learning. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM 2016-October: 109–116

  • Oztemel E, Gursev S (2020) Literature review of industry 4.0 and related technologies. J Intell Manuf 31(1):127–182

    Google Scholar 

  • Pang B, Tang G, Zhou C, Tian T (2018) Rotor fault diagnosis based on characteristic frequency band energy entropy and support vector machine. Entropy. https://doi.org/10.3390/e20120932

    Article  Google Scholar 

  • Pang X, Shao J, Xue X, Jiang W (2020) Application of axis orbit image optimization in fault diagnosis for rotor system. International Journal of Rotating Machinery 2020

  • Patel T, Darpe A (2009a) Use of full spectrum cascade for rotor rub identification. Adv Vib Eng 8(2):139–151

    Google Scholar 

  • Patel TH, Darpe AK (2009b) Coupled bending-torsional vibration analysis of rotor with rub and crack. J Sound Vib 326(3–5):740–752

    Google Scholar 

  • Patel TH, Darpe AK (2009c) Vibration response of misaligned rotors. J Sound Vib 325(3):609–628

    Google Scholar 

  • Pennacchi P, Bachschmid N, Vania A, Zanetta GA, Gregori L (2006) Use of modal representation for the supporting structure in model-based fault identification of large rotating machinery: part 1-theoretical remarks. Mech Syst Signal Process 20(3):662–681

    Google Scholar 

  • Qin J, Liu Y, Grosvenor R (2016) A categorical framework of manufacturing for industry 4.0 and beyond. Procedia Cirp 52:173–178

    Google Scholar 

  • Qiu Y, Rao SS (2005) A fuzzy approach for the analysis of unbalanced nonlinear rotor systems. J Sound Vib 284(1–2):299–323. https://doi.org/10.1016/j.jsv.2004.06.029

    Article  Google Scholar 

  • Quiroz JC, Mariun N, Mehrjou MR, Izadi M, Misron N, Mohd Radzi MA (2018) Fault detection of broken rotor bar in LS-PMSM using random forests. Meas J Int Meas Conf 116(November 2017):273–280. https://doi.org/10.1016/j.measurement.2017.11.004

    Article  Google Scholar 

  • Rahman AGA, Ismail Z, Noroozi S, Chao OZ (2013) Study of open crack in rotor shaft using changes in frequency response function phase. Int J Damage Mech 22(6):791–807

    Google Scholar 

  • Rai V, Mohanty A (2007) Bearing fault diagnosis using fft of intrinsic mode functions in hilbert-huang transform. Mech Syst Signal Process 21(6):2607–2615

    Google Scholar 

  • Reddy MCS, Sekhar AS (2013) Application of artificial neural networks for identification of unbalance and looseness in rotor bearing system. Int J Appl Sci Eng 11(1):69–84

    Google Scholar 

  • Ricci R, Pennacchi P (2011) Diagnostics of gear faults based on emd and automatic selection of intrinsic mode functions. Mech Syst Signal Process 25(3):821–838

    Google Scholar 

  • Roemer MJ, Hong CA, Hesler SH (1995) Machine health monitoring and life management using finite element based neural networks. In: Proceedings of the ASME Turbo Expo 5(October), https://doi.org/10.1115/95-GT-243

  • Ruiming F, Hongzhong M (2006) Application of MCSA and SVM to induction machine rotor fault diagnosis. Proc World Congress Intell Control Autom (WCICA) 2(2):5543–5547. https://doi.org/10.1109/WCICA.2006.1714134

    Article  Google Scholar 

  • Sadeghian A, Ye Z, Wu B (2009) Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Trans Instrum Meas 58(7):2253–2263. https://doi.org/10.1109/TIM.2009.2013743

    Article  Google Scholar 

  • Sadoughi M, Hu C (2019) Physics-based convolutional neural network for fault diagnosis of rolling element bearings. IEEE Sens J 19(11):4181–4192. https://doi.org/10.1109/JSEN.2019.2898634

    Article  Google Scholar 

  • Saimurugan M, Ramachandran K (2014) A comparative study of sound and vibration signals in detection of rotating machine faults using support vector machine and independent component analysis. Int J Data Anal Tech Strat 6(2):188–204

    Google Scholar 

  • Santos P, Villa LF, Reñones A, Bustillo A, Maudes J (2012) Wind turbines fault diagnosis using ensemble classifiers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7377 LNAI:67–76, https://doi.org/10.1007/978-3-642-31488-9_6

  • Shao H, Jiang H, Wang F, Zhao H (2017) An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowl-Based Syst 119:200–220. https://doi.org/10.1016/j.knosys.2016.12.012

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.ymssp.2017.08.002

    Article  Google Scholar 

  • Shao S, Sun W, Wang P, Gao RX, Yan R (2016) Learning features from vibration signals for induction motor fault diagnosis. Int Symp Flex Autom ISFA 2016:71–76. https://doi.org/10.1109/ISFA.2016.7790138

    Article  Google Scholar 

  • Sharma S, Malik H, Khatri A (2015) External fault classification experienced by three-phase induction motor based on multi-class ELM. Proc Comput Sci 70:814–820. https://doi.org/10.1016/j.procs.2015.10.122

    Article  Google Scholar 

  • Singh S, Kumar N (2015) Rotor faults diagnosis using artificial neural networks and support vector machines. Int J Acoust Vib 20(4):153–159. https://doi.org/10.20855/ijav.2015.20.3379

    Article  Google Scholar 

  • Son JD, Niu G, Yang BS, Hwang DH, Kang DS (2009) Development of smart sensors system for machine fault diagnosis. Expert Syst Appl 36(9):11981–11991. https://doi.org/10.1016/j.eswa.2009.03.069

    Article  Google Scholar 

  • Sonkul N, Singh J, Vyas NS (2019) Deep learning protocol for condition monitoring and fault identification in a rotor-bearing system from raw time-domain data

  • Srinivas H, Holla SS (2014) Applications of artificial neural network and wavelet transform for condition monitoring of the combined faults of unbalance and bearing clearance

  • Srinivas H, Srinivasan K, Umesh K (2010a) Application of artificial neural network and wavelet transform for vibration analysis of combined faults of unbalances and shaft bow. Adv Theor Appl Mech 3(4):159–176

    Google Scholar 

  • Srinivas HK, Srinivasan KS, Umesh KN (2010b) Role of an artificial neural network and a wavelet transform for condition monitoring of the combined faults of unbalance and cracked rotors. Int J Acoust Vib 15(3):121–127. https://doi.org/10.20855/ijav.2010.15.3264

    Article  Google Scholar 

  • Sun W, Shao S, Zhao R, Yan R, Zhang X, Chen X (2016) A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Meas J Int Meas Conf 89:171–178. https://doi.org/10.1016/j.measurement.2016.04.007

    Article  Google Scholar 

  • Sun W, Zhao R, Yan R, Shao S, Chen X (2017) Convolutional discriminative feature learning for induction motor fault diagnosis. IEEE Trans Ind Inform 13(3):1350–1359

    Google Scholar 

  • Tajik M, Movasagh S, Shoorehdeli MA, Yousefi I (2015a) Gas turbine shaft unbalance fault detection by using vibration data and neural networks. Int Conf Robot Mechatron ICROM 2015:308–313. https://doi.org/10.1109/ICRoM.2015.7367802

    Article  Google Scholar 

  • Tajik M, Movasagh S, Shoorehdeli MA, Yousefi I (2015b) Gas turbine shaft unbalance fault detection by using vibration data and neural networks. In: 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM), IEEE, pp 308–313

  • Tang H, Liao Z, Ozaki Y, Chen P (2020) Stepwise intelligent diagnosis method for rotor system with sliding bearing based on statistical filter and stacked auto-encoder. Appl Sci 10(7):2477

    Google Scholar 

  • Tang X, Zhuang L, Cai J, Li C (2010) Multi-fault classification based on support vector machine trained by chaos particle swarm optimization. Knowl-Based Syst 23(5):486–490. https://doi.org/10.1016/j.knosys.2010.01.004

    Article  Google Scholar 

  • Tao H, Mo L, Shen F, Du Z, Yan R (2016) Multi-classifiers ensemble with confidence diversity for fault diagnosis in induction motors. Proc Int Conf Sens Technol ICST 2015:1–5. https://doi.org/10.1109/ICSensT.2016.7796328

    Article  Google Scholar 

  • Timusk M, Lipsett M, Mechefske CK (2008) Fault detection using transient machine signals. Mech Syst Signal Process 22(7):1724–1749

    Google Scholar 

  • Uddin J, Kang M, Nguyen DV, Kim JM (2014) Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine. Mathematical Problems in Engineering 2014

  • Udmale SS, Patil SS, Phalle VM, Singh SK (2019a) A bearing vibration data analysis based on spectral kurtosis and convnet. Soft Comput 23(19):9341–9359

    Google Scholar 

  • Udmale SS, Singh SK, Singh R, Sangaiah AK (2019b) Multi-fault bearing classification using sensors and convnet-based transfer learning approach. IEEE Sens J

  • Vyas NS, Satishkumar D (2001) Artificial neural network design for fault identification in a rotor-bearing system. Mech Mach Theory 36(2):157–175. https://doi.org/10.1016/S0094-114X(00)00034-3

    Article  MATH  Google Scholar 

  • Walker RB, Vayanat R, Perinpanayagam S, Jennions IK (2014) Unbalance localization through machine nonlinearities using an artificial neural network approach. Mech Mach Theory 75:54–66. https://doi.org/10.1016/j.mechmachtheory.2014.01.006

    Article  Google Scholar 

  • Wang D, Tsui KL, Miao Q (2017) Prognostics and health management: a review of vibration based bearing and gear health indicators. IEEE Access 6:665–676

    Google Scholar 

  • Wang J, Liu S, Gao RX, Yan R (2012) Current envelope analysis for defect identification and diagnosis in induction motors. J Manuf Syst 31(4):380–387. https://doi.org/10.1016/j.jmsy.2012.06.005

    Article  Google Scholar 

  • Wang J, Fu P, Li W, Zhang L (2018) Multimodal deep learning for multiple motor and sensor faults diagnosis. In: Proceedings of the International Symposium on Flexible Automation, The Institute of Systems, Control and Information Engineers, pp 366–372

  • Wang N, Jiang D, Han T (2016) Dynamic characteristics of rotor system and rub-impact fault feature research based on casing acceleration. J Vibroeng 18(3):1525–1539

    Google Scholar 

  • Wang XB, Zhang X, Li Z, Wu J (2020) Ensemble extreme learning machines for compound-fault diagnosis of rotating machinery. Knowl-Based Syst 188:105012

    Google Scholar 

  • Wei Y, Li Y, Xu M, Huang W (2019) A review of early fault diagnosis approaches and their applications in rotating machinery. Entropy 21(4):409

    Google Scholar 

  • Widodo A, Yang BS (2007) Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Syst Appl 33(1):241–250. https://doi.org/10.1016/j.eswa.2006.04.020

    Article  Google Scholar 

  • Widodo A, Yang BS (2008) Wavelet support vector machine for induction machine fault diagnosis based on transient current signal. Expert Syst Appl 35(1–2):307–316. https://doi.org/10.1016/j.eswa.2007.06.018

    Article  Google Scholar 

  • Widodo A, Yang BS, Han T (2007) Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Syst Appl 32(2):299–312. https://doi.org/10.1016/j.eswa.2005.11.031

    Article  Google Scholar 

  • Wu T, Chung Y, Liu C (2010) Looseness diagnosis of rotating machinery via vibration analysis through hilbert-huang transform approach. J Vib Acoust 132(3):031005

    Google Scholar 

  • Xia Z, Xia S, Wan L, Cai S (2012) Spectral regression based fault feature extraction for bearing accelerometer sensor signals. Sensors 12(10):13694–13719

    Google Scholar 

  • Xiao D, Huang Y, Qin C, Shi H, Li Y (2019a) Fault Diagnosis of Induction Motors Using Recurrence Quantification Analysis and LSTM with Weighted BN. Shock and Vibration 2019: https://doi.org/10.1155/2019/8325218

  • Xiao D, Huang Y, Zhao L, Qin C, Shi H, Liu C (2019b) Domain adaptive motor fault diagnosis using deep transfer learning. IEEE Access 7:80937–80949. https://doi.org/10.1109/ACCESS.2019.2921480

    Article  Google Scholar 

  • Xiaoxun Z, Jianhong Z, Dongnan H, Zhonghe H (2019) Research on mechanical rotor condition monitoring based on VCNN. Energy Procedia 158:6393–6398. https://doi.org/10.1016/j.egypro.2019.01.208

    Article  Google Scholar 

  • Xu BG (2012) Intelligent fault inference for rotating flexible rotors using Bayesian belief network. Expert Syst Appl 39(1):816–822. https://doi.org/10.1016/j.eswa.2011.07.079

    Article  Google Scholar 

  • Xu X, Zheng H, Guo Z, Wu X, Zheng Z (2019) SDD-CNN: small data-driven convolution neural networks for subtle roller defect inspection. Appl Sci (Switzerland). https://doi.org/10.3390/app9071364

    Article  Google Scholar 

  • Xu X, Cao D, Zhou Y, Gao J (2020) Application of neural network algorithm in fault diagnosis of mechanical intelligence. Mechanical Systems and Signal Processing p 106625

  • Xue H, Wang H, Song L, Chen P (2011) Structural fault diagnosis of rotating machinery based on distinctive frequency components and support vector machines. In: International Conference on Intelligent Computing, Springer, pp 341–348

  • Xue H, Wang H, Chen P, Li K, Song L (2013) Automatic diagnosis method for structural fault of rotating machinery based on distinctive frequency components and support vector machines under varied operating conditions. Neurocomputing 116:326–335

    Google Scholar 

  • Yan J, Hu Y, Guo C (2019a) Rotor unbalance fault diagnosis using DBN based on multi-source heterogeneous information fusion. Procedia Manuf 35:1184–1189. https://doi.org/10.1016/j.promfg.2019.06.075

    Article  Google Scholar 

  • Yan X, Sun Z, Zhao J, Shi Z, Zhang CA (2019b) Fault diagnosis of active magnetic bearing-rotor system via vibration images. Sensors 19(2):244

    Google Scholar 

  • Yang X, Yan R, Gao RX (2015) Induction motor fault diagnosis using multiple class feature selection. In: 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, IEEE, pp 256–260

  • Yang ZX, Wang XB, Zhong JH (2016) Representational learning for fault diagnosis of wind turbine equipment: a multi-layered extreme learning machines approach. Energies 9(6):1–17. https://doi.org/10.3390/en9060379

    Article  Google Scholar 

  • Yao Y, Li Y, Zhang P, Xie B, Xia L (2018) Data fusion methods for convolutional neural network based on self-sensing motor drive system. In: Proceedings: IECON 2018—44th Annual Conference of the IEEE Industrial Electronics Society 1:5371–5376. https://doi.org/10.1109/IECON.2018.8591160

  • Yongbo L, Xiaoqiang D, Fangyi W, Xianzhi W, Huangchao Y (2020) Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chin J Aeronaut 33(2):427–438

    Google Scholar 

  • Younus AM, Yang BS (2012) Intelligent fault diagnosis of rotating machinery using infrared thermal image. Expert Syst Appl 39(2):2082–2091. https://doi.org/10.1016/j.eswa.2011.08.004

    Article  Google Scholar 

  • Yu T, Han Q (2010) Crack fault identification in rotor shaft with artificial neural network. In: Proceedings—2010 6th International Conference on Natural Computation, ICNC 2010 3(Icnc):1629–1634, https://doi.org/10.1109/ICNC.2010.5583771

  • Yu W, Huang S, Xiao W (2018) Fault diagnosis based on an approach combining a spectrogram and a convolutional neural network with application to awind turbine system. Energies. https://doi.org/10.3390/en11102561

    Article  Google Scholar 

  • Yuan SF, Chu FL (2006) Support vector machines-based fault diagnosis for turbo-pump rotor. Mech Syst Signal Process 20(4):939–952. https://doi.org/10.1016/j.ymssp.2005.09.006

    Article  Google Scholar 

  • Yuan Z, Zhang L, Duan L (2018) A novel fusion diagnosis method for rotor system fault based on deep learning and multi-sourced heterogeneous monitoring data. Meas Sci Technol. https://doi.org/10.1088/1361-6501/aadfb3

    Article  Google Scholar 

  • Yuan Z, Zhang L, Duan L (2019) Multi-sourced monitoring fusion diagnosis for rotating machinery faults. In: Proceedings—Annual Reliability and Maintainability Symposium 2019-January(1):1–7. https://doi.org/10.1109/RAMS.2019.8769018

  • Yusuf S, Brown DJ, MacKinnon A, Papanicolaou R (2013) Fault classification improvement in industrial condition monitoring via hidden markov models and naïve bayesian modeling. ISIEA 2013—2013 IEEE Symposium on Industrial Electronics and Applications pp 75–80, https://doi.org/10.1109/ISIEA.2013.6738971

  • Zhang J, Ma W, Lin J, Ma L, Jia X (2015) Fault diagnosis approach for rotating machinery based on dynamic model and computational intelligence. Meas J Int Meas Conf 59:73–87. https://doi.org/10.1016/j.measurement.2014.09.045

    Article  Google Scholar 

  • Zhang J, Zhang D, Yang M, Xu X, Liu W, Wen C (2018) Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples: A Deep CNN Method Based on Knowledge-Transferring from Shallow Models. ICCAIS 2018—7th International Conference on Control, Automation and Information Sciences (Iccais):482–487, https://doi.org/10.1109/ICCAIS.2018.8570515

  • Zhang J, Tian J, Wen T, Yang X, Rao Y, Xu X (2019a) Deep Fault Diagnosis for Rotating Machinery with Scarce Labeled Samples. arXiv preprint arXiv:190709411 1(61806064), https://doi.org/10.1049/cje.2019.0X.0XX

  • Zhang S, Zhang S, Wang B, Habetler TG (2019b) Machine learning and deep learning algorithms for bearing fault diagnostics-a comprehensive review. arXiv preprint arXiv:190108247

  • Zhang W, Peng G, Li C, Chen Y, Zhang Z (2017) A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sens (Switzerland). https://doi.org/10.3390/s17020425

    Article  Google Scholar 

  • Zhang ZY, Wang KS (2014) Wind turbine fault detection based on scada data analysis using ann. Adv Manuf 2(1):70–78

    Google Scholar 

  • Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237

    Google Scholar 

  • Zhao W, Hua C, Wang D, Dong D (2020a) Fault diagnosis of shaft misalignment and crack in rotor system based on mi-cnn. In: Proceedings of the 13th International Conference on Damage Assessment of Structures, Springer, pp 529–540

  • Zhao X, Wu J, Zhang Y, Shi Y, Wang L (2018) Fault diagnosis of motor in frequency domain signal by stacked de-noising auto-encoder. Comput Mater Continua 57(2):223–242. https://doi.org/10.32604/cmc.2018.02490

    Article  Google Scholar 

  • Zhao X, Jia M, Ding P, Yang C, She D, Liu Z (2020b) Intelligent fault diagnosis of multi-channel motor-rotor system based on multi-manifold deep extreme learning machine. IEEE/ASME Transactions on Mechatronics

  • Zhu X, Hou D, Zhou P, Han Z, Yuan Y, Zhou W, Yin Q (2019a) Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images. Meas J Int Meas Conf 138:526–535. https://doi.org/10.1016/j.measurement.2019.02.022

    Article  Google Scholar 

  • Zhu X, Zhao J, Hou D, Han Z (2019b) An SDP characteristic information fusion-based CNN vibration fault diagnosis method. Shock and Vibration 2019: https://doi.org/10.1155/2019/3926963

Download references

Acknowledgements

The authors would like to acknowledge IMAGENOUS Engineering Pvt. Ltd. Vadodara-390 016, Gujarat, India and Meggitt India Pvt. Ltd., North Bangalore-560 022, India for sharing valuable information about vibration data and demonstration on the experimental environment.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, 221005, India

    Aneesh G. Nath & Sanjay Kumar Singh

  2. Department of Computer Engineering and IT, Veermata Jijabai Technological Institute (VJTI), Mumbai, Maharashtra, 400019, India

    Sandeep S. Udmale

Authors
  1. Aneesh G. Nath
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sandeep S. Udmale
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sanjay Kumar Singh
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Aneesh G. Nath.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 179 KB)

Supplementary material 2 (pdf 123 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nath, A.G., Udmale, S.S. & Singh, S.K. Role of artificial intelligence in rotor fault diagnosis: a comprehensive review. Artif Intell Rev 54, 2609–2668 (2021). https://doi.org/10.1007/s10462-020-09910-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-020-09910-w

Keywords