Abstract
Rotating machines is an essential part of any manufacturing industry. The sudden breakdown of such machines due to improper maintenance can also lead to the industries' shutdown. The era of the 4th industrial revolution is taking its major shape concerning maintenance strategies, notable being in predictive maintenance. Fault prediction and diagnosis is the major concern in predictive maintenance as this is the major issue faced by all the maintenance engineers. Most of the bibliometric literature review studies that are accessible focus on fault diagnosis in rotating machines, mainly focusing on a single type of fault. However, there isn't a thorough analysis of the literature that focuses on the "multi-fault diagnosis using multi-sensor data" aspect of rotating machines. In this regard, this paper reviews the literature on the “multi-Fault diagnosis using multi-sensor data fusion” of Industrial Rotating Machines employing Machine learning/Deep learning techniques. A hybrid bibliometric approach was used to analyze articles from the “Web of Science” and “Scopus” Database for the last 10 years. The method for literature analysis used, is quantitative as well as qualitative, as not only the traditional approach (bibliometric and network analysis) but also a novel method named ProKnow-C is used, and it entails a number of phases, that includes intelligent and extensive filtering from the large set of results and finally selecting the articles that are more pertinent to the research theme. Based on available publications, an analysis is performed on year-by-year publication data, article types, linguistic distribution of articles, funding sponsors, affiliations, citation analysis and the relationship between keywords, authors, etc. to provide an in-depth vision of research trends in the related area. The paper also focuses on the maintenance strategies, predictive maintenance approaches, AI algorithms, Multi sensor data fusion, challenges, and future directions in “multi-fault diagnosis using multi-sensor data fusion” in rotating machines. The foundational work done in the field, the most prolific papers and the key research themes within the research area are all identified in this bibliometric survey.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akhil V, Raghav G, Arunachalam N, Srinivas DS (2020) Image data-based surface texture characterization and prediction using machine learning approaches for additive manufacturing. J Comput Inf Sci Eng 20(2):1–16. https://doi.org/10.1115/1.4045719
Ali MZ, Shabbir MNSK, Zaman SMK, Liang X (2020) Single- and multi-fault diagnosis using machine learning for variable frequency drive-fed induction motors. IEEE Trans Ind Appl 56(3):2324–2337. https://doi.org/10.1109/TIA.2020.2974151
Amoui M, Salehie M, Tahvildari L (2007) Temporal software change prediction using neural networks. Int J Softw Eng Knowl Eng—IJSEKE 19:380–385. https://doi.org/10.1142/S0218194009004489
Arora V, Wijnant YH, de Boer A (2014) Acoustic-based damage detection method. Appl Acoust 80:23–27. https://doi.org/10.1016/j.apacoust.2014.01.003
Babalola A, Musa S, Akinlolu MT, Haupt TC (2021) A bibliometric review of advances in building information modeling (BIM) research. J Eng, Design Technol. https://doi.org/10.1108/JEDT-01-2021-0013
Barbieri M, Nguyen KTP, Diversi R, Medjaher K, Tilli A (2020) RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques. J Intell Manuf. https://doi.org/10.1007/s10845-020-01696-6
Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2013) Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Eng Appl Artif Intell 26(7):1751–1760. https://doi.org/10.1016/j.engappai.2013.02
Bertoncel T, Meško M (2019) Early warning systems in industry 4.0: a bibliometric and topic analysis. Int J E-Serv Mobile Appl 11(2):56–70. https://doi.org/10.4018/IJESMA.2019040104
Betta G, Liguori C, Paolillo A, Pietrosanto A (2002) A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis. IEEE Trans Instrum Meas 51(6):1316–1321. https://doi.org/10.1109/TIM.2002.807987
Cao R, Yunusa-Kaltungo A (2021) An automated data fusion-based gear faults classification framework in rotating machines. Sensors. https://doi.org/10.3390/s21092957
Celaya J, Saxena A, Saha S, Goebel KF (2014) Prognostics of power mosfets under thermal stress accelerated aging using data-driven and model-based methodologies. In: Proceedings of the annual conference of the prognostics and health management society 2011, PHM 2011, January, 443–452.
Chatterton S, Pennacchi P, Vania A (2021) An unconventional method for the diagnosis and study of generator rotor thermal bows. J Eng Gas Turbines Power. https://doi.org/10.1115/1.4052079
Che C, Wang H, Ni X, Fu Q (2020) Domain adaptive deep belief network for rolling bearing fault diagnosis. Comput Ind Eng 143:106427. https://doi.org/10.1016/j.cie.2020.106427
Chen Z, Li W (2017) Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans Instrum Meas 66(7):1693–1702. https://doi.org/10.1109/TIM.2017.2669947
Chen J, Lin C, Peng D, Ge H (2020) Fault diagnosis of rotating machinery: a review and bibliometric analysis. IEEE Access 8:224985–225003. https://doi.org/10.1109/ACCESS.2020.3043743
Chinnam R, Baruah P (2004) A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems. Int J Mater Product Technol 20(1):166–179
Choudhary A, Shimi S, and Akula A (2018) Bearing fault diagnosis of induction motor using thermal imaging. 2018 International Conference on Computing, Power and Communication Technologies (GUCON). https://doi.org/10.1109/gucon.2018.8674889
Çinar ZM, Nuhu AA, Zeeshan Q, Korhan O, Asmael M, Safaei B (2020) Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability (Switzerland). https://doi.org/10.3390/su12198211
Cobo MJ, Jürgens B, Herrero-Solana V, Martínez MA, Herrera-Viedma E (2018) Industry 4.0: a perspective based on bibliometric analysis. Procedia Comput Sci 139:364–371. https://doi.org/10.1016/j.procs.2018.10.278
Dalzochio J, Kunst R, Pignaton E, Binotto A, Sanyal S, Favilla J, Barbosa J (2020) Machine learning and reasoning for predictive maintenance in Industry 4.0: current status and challenges. Comput Ind 123:103298. https://doi.org/10.1016/j.compind.2020.103298
Deng F, Guo S, Zhou R, Chen J (2017) Sensor multifault diagnosis with improved support vector machines. IEEE Trans Autom Sci Eng 14(2):1053–1063. https://doi.org/10.1109/TASE.2015.2487523
Diez-Olivan A, Pagan JA, Sanz R, Sierra B (2017) Data-driven prognostics using a combination of constrained k-means clustering, fuzzy modeling and lof-based score. Neurocomputing 241:97–107
Ding H, Gao RX, Isaksson AJ, Landers RG, Parisini T, Yuan Y (2020) State of AI-based monitoring in smart manufacturing and introduction to focused section. IEEE/ASME Trans Mechatron 25(5):2143–2154. https://doi.org/10.1109/TMECH.2020.3022983
Ding C, Zhao M, Lin J, Jiao J (2019) Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings. ISA Trans 88:199–215. https://doi.org/10.1016/j.isatra.2018.12.010
Dong S, Xu X, Liu J, Gao Z (2015) Rotating machine fault diagnosis based on locality preserving projection and back propagation neural network-support vector machine model. Meas Control 48(7):211–216. https://doi.org/10.1177/0020294015595995
Fabbiano L, Vacca G, Dinardo G (2018) A smart and intuitive machine condition monitoring in the Industry 4.0 scenario. Measurement. https://doi.org/10.1016/j.measurement.2018.05.041
Fahle S, Prinz C, Kuhlenkötter B (2020) Systematic review on machine learning (ML) methods for manufacturing processes—Identifying artificial intelligence (AI) methods for field application. Procedia CIRP 93:413–418. https://doi.org/10.1016/j.procir.2020.04.109
Fernandes EC, Fitzgerald B, Brown L, Borsato M (2019) Machine learning and process mining applied to process optimization: bibliometric and systemic analysis. Procedia Manuf 38(Faim 2019):84–91. https://doi.org/10.1016/j.promfg.2020.01.012
Ferrando Chacon JL, Kappatos V, Balachandran W, Gan TH (2015) A novel approach for incipient defect detection in rolling bearings using acoustic emission technique. Appl Acoust 89:88–100. https://doi.org/10.1016/j.apacoust.2014.09.002
Gangsar P, Tiwari R (2018) Multifault diagnosis of induction motor at intermediate operating conditions using wavelet packet transform and support vector machine. J Dyn Syst, Meas Control, Trans ASME. https://doi.org/10.1115/1.4039204
Garcia-Perez A, Romero-Troncoso RDJ, Cabal-Yepez E, Osornio-Rios RA (2011) The application of high-resolution spectral analysis for identifying multiple combined faults in induction motors. IEEE Trans Industr Electron 58(5):2002–2010. https://doi.org/10.1109/TIE.2010.2051398
Garga AK, McClintic KT, Campbell RL, Yang CC, Lebold MS, Hay TA, Byington CS (2001) Hybrid reasoning for prognostic learning in CBM systems. IEEE Aerosp Conf Proc 6:62957–62969. https://doi.org/10.1109/AERO.2001.931316
Gawde SS, and Borkar S (2018) Condition monitoring using image processing. Proceedings of the International Conference on Computing Methodologies and Communication, ICCMC 2017, 2018-Janua, 1083–1086. https://doi.org/10.1109/ICCMC.2017.8282638
Glowacz A (2019) Fault diagnosis of single-phase induction motor based on acoustic signals. Mech Syst Signal Process 117:65–80. https://doi.org/10.1016/j.ymssp.2018.07.044
Gong W, Chen H, Zhang Z, Zhang M, Wang R, Guan C, Wang Q (2019) A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors (Switzerland). https://doi.org/10.3390/s19071693
Hao S, Ge FX, Li Y, Jiang J (2020) Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks. Meas: J Int Meas Confed 159:107802. https://doi.org/10.1016/j.measurement.2020.107802
He D, Wang X, Li S, Lin J, Zhao M (2016) Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis. Mech Syst Signal Process 81:235–249. https://doi.org/10.1016/j.ymssp.2016.03.016
He Z, Wang S, Wang K, and Li K (2012) “Prognostic analysis based on hybrid prediction method for axial piston pump.” In: Proc. 10th IEEE Int. Conf. Ind. Inf. (INDIN 2012), pp. 688–692.
He J, Li X, Chen Y, Chen D, Guo J, Zhou Y (2021) Deep transfer learning method based on 1D-CNN for bearing fault diagnosis. Shock Vib 2021(1). https://doi.org/10.1155/2021/6687331
Hong S, Zhou Z, Zio E, Hong K (2014) Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digit Signal Process 27(1):159–166. https://doi.org/10.1016/j.dsp.2013.12.010
Huang M, Liu Z, Tao Y (2020) Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simul Model Pract Theory 102(September):101981. https://doi.org/10.1016/j.simpat.2019.101981
Huang M, Liu Z (2020) Research on mechanical fault prediction method based on multifeature fusion of vibration sensing data. Sensors (Switzerland). https://doi.org/10.3390/s20010006
Islam MR, Kim YH, Kim JY, Kim JM (2019) Detecting and learning unknown fault states by automatically finding the optimal number of clusters for online bearing fault diagnosis. Appl Sci (Switzerland). https://doi.org/10.3390/app9112326
Jablon LS, Avila SL, Borba B, Mourão GL, Freitas FL, Penz CA (2020) Diagnosis of rotating machine unbalance using machine learning algorithms on vibration orbital features. J Vib Control. https://doi.org/10.1177/1077546320929830
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
Jia F, Lei Y, Lin J, Zhou X, Lu N (2016) Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 72–73:303–315. https://doi.org/10.1016/j.ymssp.2015.10.025
Jiang H, Li C, Li H (2013) An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis. Mech Syst Signal Process 36(2):225–239. https://doi.org/10.1016/j.ymssp.2012.12.010
Jiang LL, Yin HK, Li XJ, Tang SW (2014) Fault diagnosis of rotating machinery based on multisensor information fusion using SVM and time-domain features. Shock Vib. https://doi.org/10.1155/2014/418178
Jiang W, Hu W, **e C (2017) A new engine fault diagnosis method based on multi-sensor data fusion. Appl Sci (Switzerland). https://doi.org/10.3390/APP7030280
**g L, Wang T, Zhao M, Wang P (2017) An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox. Sensors (Switzerland). https://doi.org/10.3390/s17020414
John Britto JJ, Vasanthanathan A, Nagaraj P (2018) Finite element modeling and simulation of condition monitoring on composite materials using piezoelectric transducers - ANSYS®. Mater Today 5(2, Part 2):6684–6691. https://doi.org/10.1016/j.matpr.2017.11.325
Jung JH, Lee JJ, Kwon BH (2006) Online diagnosis of induction motors using MCSA. IEEE Trans Industr Electron 53(6):1842–1852. https://doi.org/10.1109/TIE.2006.885131
Kamat P, Kamat P, Sugandhi R (2020) Bibliometric analysis of bearing fault detection using artifificial intelligence. Libr Philos Pract 2020(October):1–22
Karamti H, Lashin MMA, Alrowais F, Mahmoud AM (2021) A new deep stacked architecture for multi-fault machinery identification with imbalanced samples. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3071796
Kim Y-H, Tan ACC, Mathew J, Yang B-S (2006) Condition monitoring of low speed bearings: a comparative study of the ultrasound technique versus vibration measurements. Engineering asset management. Springer, London. https://doi.org/10.1007/978-1-84628-814-2_21
Krüger S, Borsato M (2019) Develo** knowledge on digital manufacturing to digital twin: a bibliometric and systemic analysis. Procedia Manuf 38(2019):1174–1180. https://doi.org/10.1016/j.promfg.2020.01.207
Kumar S, Bongale A, Patil S, Bongale AM, Kamat P, Kotecha K (2020) Demystifying artificial intelligence based digital twins in manufacturing—a bibliometric analysis of trends and techniques. Libr Philos Pract 2020(November):1–21
Lee, S. Bin, Hyun, D., Kang, T. J., Yang, C., Shin, S., Kim, H., Park, S., Kong, T. S., & Kim, H. D. (2015). Identification of false rotor fault indications produced by on-line MCSA for medium voltage induction machines. IEEE Conference Record of Annual Pulp and Paper Industry Technical Conference, 2015-July(1), 729–739. https://doi.org/10.1109/PPIC.2015.7165709
Li Q, Liang SY (2018) Multiple faults detection for rotating machinery based on bicomponent sparse low-rank matrix separation approach. IEEE Access 6(September):20242–20254. https://doi.org/10.1109/ACCESS.2018.2823719
Li C, Sánchez R, Zurita G, Cerrada M, Cabrera D (2016a) Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors 16:895. https://doi.org/10.3390/s16060895
Li Y, Xu M, Wang R, Huang W (2016b) A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy. J Sound Vib 360:277–299. https://doi.org/10.1016/j.jsv.2015.09.016
Li X, Zhang W, Xu N-X, Ding Q (2019) Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places. IEEE Trans Industr Electron. https://doi.org/10.1109/tie.2019.2935987
Liao L, Köttig F (2014) Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Trans Reliab 63(1):191–207. https://doi.org/10.1109/TR.2014.2299152
Liu Z, Cao H, Chen X, He Z, Shen Z (2013) Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing 99:399–410. https://doi.org/10.1016/j.neucom.2012.07.019
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. https://doi.org/10.1016/j.ymssp.2018.02.016
Liu Z, Li Q, and Mu C (2012) “A hybrid LSSVR-HMM based prognostics approach,” In: Proc. 4th Int. Conf. Intell. Human-Machine Syst. Cy- bernet.,vol. 2, pp. 275–278
Lopez-Perez D, Antonino-Daviu J (2017) Application of infrared thermography to failure detection in industrial induction motors: case stories. IEEE Trans Ind Appl 53(3):1901–1908. https://doi.org/10.1109/TIA.2017.2655008
Luo W, Hu T, Ye Y, Zhang C, Wei Y (2020) A hybrid predictive maintenance approach for CNC machine tool driven by digital twin. Robot Comput-Integr Manuf 65(2019):101974. https://doi.org/10.1016/j.rcim.2020.101974
Luwei KC, Yunusa-Kaltungo A, Sha’aban YA (2018) Integrated fault detection framework for classifying rotating machine faults using frequency domain data fusion and artificial neural networks. Machines. https://doi.org/10.3390/MACHINES6040059
Malla C, Panigrahi I (2019) Review of condition monitoring of rolling element bearing using vibration analysis and other techniques. J Vib Eng Technol. https://doi.org/10.1007/s42417-019-00119-y
Mao W, Feng W, Liu Y, Zhang D, Liang X (2021) A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mech Syst Signal Process 150:107233. https://doi.org/10.1016/j.ymssp.2020.107233
Mey O, Neudeck W, Schneider A, Enge-Rosenblatt O (2020) Machine learning-based unbalance detection of a rotating shaft using vibration data. https://doi.org/10.1109/etfa46521.2020.9212000
Mohanty, AR (2014). Machinery condition monitoring: principles and practices. In Machinery condition monitoring: principles and practices.
Nath AG, Udmale SS, Singh SK (2021) Role of artificial intelligence in rotor fault diagnosis: a comprehensive review. Artif Intell Rev 54:2609–2668. https://doi.org/10.1007/s10462-020-09910-w
Nguyen G, Dlugolinsky S, Bobák M, Tran V, LópezGarcía Á, Heredia I et al (2019) Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev. https://doi.org/10.1007/s10462-018-09679-z
Noman MA, Nasr ESA, Al-Shayea A, Kaid H (2019) Overview of predictive condition based maintenance research using bibliometric indicators. J King Saud Univ—Eng Sci 31(4):355–367. https://doi.org/10.1016/j.jksues.2018.02.003
Okoh C, Roy R, Mehnen J (2017) Predictive maintenance modelling for through-life engineering services. Procedia CIRP 59(TESConf 2016):196–201. https://doi.org/10.1016/j.procir.2016.09.033
Oztemel E, Gursev S (2020) Literature review of Industry 4.0 and related technologies. J Intell Manuf 31(1):127–182. https://doi.org/10.1007/s10845-018-1433-8
Peel L (2008) “Data driven prognostics using a Kalman filter ensemble of neural network models.” In: Proc Int. Conf. Prognost. Health Manage- ment, pp. 1–6.
Peng, Y., Cheng, F., Qiao, W., & Qu, L. (2017). Fault prognosis of drivetrain gearbox based on a recurrent neural network. In: 2017 IEEE international conference on electro information technology 593–599. https://doi.org/10.1109/EIT.2017.8053433
Rahmoune C, Djamel B (2013) Monitoring gear fault by using motor current signature analysis and fast kurtogram method. Int Rev Electr Eng 8:616–625
Safizadeh MS, Latifi SK (2014) Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Inf Fusion 18(1):1–8. https://doi.org/10.1016/j.inffus.2013.10.002
Samanta B, Al-Balushi KR, Al-Araimi SA (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16(7–8):657–665. https://doi.org/10.1016/j.engappai.2003.09
Sánchez RV, Lucero P, Vásquez RE, Cerrada M, Macancela JC, Cabrera D (2018) Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN. J Intell Fuzzy Syst 34(6):3463–3473. https://doi.org/10.3233/JIFS-169526
Satish B and Sarma N (2005) “A fuzzy BP approach for diagnosis and prognosis of bearing faults in induction motors.” In: Proceedings IEEE Power Engineering Society General Meeting. pp. 2291–2294.
Sayyad S, Kumar S, Bongale A, Kamat P, Patil S, Kotecha K (2021a) Data-driven remaining useful life estimation for milling process: sensors, algorithms, datasets, and future directions. IEEE Access 9:110255–110286. https://doi.org/10.1109/ACCESS.2021.3101284
Sayyad S, Kumar S, Bongale A, Bongale AM, Patil S (2021b) Estimating remaining useful life in machines using artificial intelligence: a sco** review. Libr Philos Pract 2021(January):1–26
Seshadrinath J, Singh B, Panigrahi BK (2014) Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Trans Power Electron 29(2):936–945. https://doi.org/10.1109/TPEL.2013.2257869
Shao SY, Sun WJ, Yan RQ, Wang P, Gao RX (2017) A deep learning approach for fault diagnosis of induction motors in manufacturing. Chin J Mech Eng (english Edition) 30(6):1347–1356. https://doi.org/10.1007/s10033-017-0189-y
Shi P, Guo X, Han D, Fu R (2020) A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis. J Mech Sci Technol 34(4):1445–1458. https://doi.org/10.1007/s12206-020-0306-1
Short M, Twiddle J (2019) An industrial digitalization platform for condition monitoring and predictive maintenance of pum** equipment. Sensors (Switzerland). https://doi.org/10.3390/s19173781
Sobral, J and Roque A (2018). Predictive Maintenance using Ultrasound Technology as Condition Monitoring. MPMM 2018—Maintenance Performance Measurement and Management Conference 2018
Su Z, Tang B, Liu Z, Qin Y (2015) Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing 157:208–222. https://doi.org/10.1016/j.neucom.2015.01.016
Sun J, Wang L, Li J, Li F, Li J, Lu H (2021) Online oil debris monitoring of rotating machinery: a detailed review of more than three decades. Mech Syst Signal Process 149:107341. https://doi.org/10.1016/j.ymssp.2020.10734
Swanson DC (2001) A general prognostic tracking algorithm for predictive maintenance. Proc IEEE Aerosp Conf 6:2971–2977
Tang T, Hu T, Chen M, Lin R, Chen G (2020) A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions. Proc Inst Mech Eng C J Mech Eng Sci. https://doi.org/10.1177/0954406220902181
Tao J, Liu Y, Yang D (2016) Bearing fault diagnosis based on deep belief network and multisensor information fusion. Shock Vib. https://doi.org/10.1155/2016/9306205
Tiago Z, André da Costa C, Righi R, Lima M, Li G (2020) Predictive maintenance in the industry 4.0: a systematic literature review. Comput Ind Eng 150:17. https://doi.org/10.1016/j.cie.2020.106889
Toutountzakis T, Tan CK, Mba D (2005) Application of acoustic emission to seeded gear fault detection. NDT and E Int 38(1):27–36. https://doi.org/10.1016/j.ndteint.2004.06.008
Touzout W, Benazzouz D, Gougam F, Afia A, Rahmoune C (2020) Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis. Adv Mech Eng 12(12):1–13. https://doi.org/10.1177/1687814020980569
Traini E, Bruno G, Lombardi F (2021) Design of a physics-based and data-driven hybrid model for predictive maintenance. In: Dolgui A, Bernard A, Lemoine D, von Cieminski G, Romero D (eds) Advances in production management systems. Artificial intelligence for sustainable and resilient production systems. Springer International Publishing, Cham, pp 536–543
Udmale SS, Singh SK (2019) Application of spectral kurtosis and improved extreme learning machine for bearing fault classification. IEEE Trans Instrum Meas 68(11):4222–4233. https://doi.org/10.1109/TIM.2018.2890329
Vieira EL, da Costa SEG, de Lima EP, Ferreira CC (2019) Application of the Proknow-C methodology in the search of literature on performance indicators for energy management in manufacturing and industry 4.0. Procedia Manuf 39(41):1259–1269. https://doi.org/10.1016/j.promfg.2020.01.343
Wang P, Tamilselvan P, Hu C (2014) Health diagnostics using multi-attribute classification fusion. Eng Appl Artif Intell 32:192–202. https://doi.org/10.1016/j.engappai.2014.03.006
Wang S, **ang J, Zhong Y, Tang H (2018) A data indicator-based deep belief networks to detect multiple faults in axial piston pumps. Mech Syst Signal Process 112:154–170. https://doi.org/10.1016/j.ymssp.2018.04.038
Wang H, Li S, Song L, Cui L (2019) A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals. Comput Ind 105:182–190. https://doi.org/10.1016/j.compind.2018.12.013
Wang H, Ni G, Chen J, Qu J (2020) Research on rolling bearing state health monitoring and life prediction based on PCA and Internet of things with multi-sensor. Meas: J Int Meas Confed 157:107657. https://doi.org/10.1016/j.measurement.2020.107657
Wu Q, Yang X, Deng R (2018) Predictive maintenance strategy of running fault based on ELM algorithm for power transformer. Int J Internet Manuf Serv 5(2–3):297–309. https://doi.org/10.1504/IJIMS.2018.091999
**a 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. https://doi.org/10.1109/TMECH.2017.2728371
**ang L, Gao X, Hu A (2016) Nonlinear dynamics of an asymmetric rotor-bearing system with coupling faults of crack and rub-impact under oil-film forces. Nonlinear Dyn 86(2):1057–1067. https://doi.org/10.1007/s11071-016-2946-3
Xu Y, Sun Y, Liu X, Zheng Y (2019) A digital-twin-assisted fault diagnosis using deep transfer learning. IEEE Access 7(c):19990–19999. https://doi.org/10.1109/ACCESS.2018.2890566
Xu X, Tao Z, Ming W, An Q, Chen M (2020) Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion. Meas: J Int Meas Confed 165:108086. https://doi.org/10.1016/j.measurement.2020.108086
Xue Y, Dou D, Yang J (2020) Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine. Meas: J Int Meas Confed 156:107571. https://doi.org/10.1016/j.measurement.2020.107571
Yan X, Jia M (2018) A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing 313:47–64. https://doi.org/10.1016/j.neucom.2018.05.002
Yang C, Jia M (2021) Health condition identification for rolling bearing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree. Struct Health Monit 20(1):151–172. https://doi.org/10.1177/1475921720923973
Yuan R, Lv Y, Li H, Song G (2019) Robust fault diagnosis of rolling bearings using multivariate intrinsic multiscale entropy analysis and neural network under varying operating conditions. IEEE Access 7(September):130804–130819. https://doi.org/10.1109/ACCESS.2019.2939546
Yunusa-Kaltungo A, Cao R (2020) Towards develo** an automated faults characterisation framework for rotating machines. Part 1: rotor-related faults. Energies. https://doi.org/10.3390/en13061394
Zair M, Rahmoune C, Benazzouz D (2019) Multi-fault diagnosis of rolling bearing using fuzzy entropy of empirical mode decomposition, principal component analysis, and SOM neural network. Proc Inst Mech Eng C J Mech Eng Sci 233(9):3317–3328. https://doi.org/10.1177/0954406218805510
Zhang X, Zhou J (2013) Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mech Syst Signal Process 41(1–2):127–140. https://doi.org/10.1016/j.ymssp.2013.07.006
Zhang G, Lee S, Propes N, Zhao Y, Vachtsevanos G, Thakker A, and Galie T (2002) “A novel architecture for an integrated fault diagnostic/ prognostic system,” In: Proceeding AAAI Spring Symposium, Stanford, California.
Zhang X, Wang B, Chen X (2015) Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowl-Based Syst 89:56–85. https://doi.org/10.1016/j.knosys.2015.06.017
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(January):213–237. https://doi.org/10.1016/j.ymssp.2018.05.050
Zhao X, Lv K, Zhang Z, Zhang Y, Wang Y (2020a) A multi-fault diagnosis method of gear-box running on edge equipment. J Cloud Comput. https://doi.org/10.1186/s13677-020-00205-7
Zhao Z, Wang S, Sun C, Yan R, Chen X (2020b) Sparse multiperiod group lasso for bearing multifault diagnosis. IEEE Trans Instrum Meas 69(2):419–431. https://doi.org/10.1109/TIM.2019.2902807
Zhao D, Cheng W, Gao RX, Yan R, Wang P (2020c) Generalized Vold-Kalman filtering for nonstationary compound faults feature extraction of bearing and gear. IEEE Trans Instrum Meas 69(2):401–410. https://doi.org/10.1109/TIM.2019.2903700
Zhao S, Song Q, Mingsheng W (2021) An effective residual life prediction method of rolling element bearings based on degradation trajectory analysis. J Mech Sci Technol. https://doi.org/10.1007/s12206-021-1103-1
Zhu J, Yoon J, He D, Qu A, Bechhoefer E (2013) Lubrication oil condition monitoring and remaining useful life prediction with particle filtering. Int J Progn Health Manag 4:1–15. https://doi.org/10.36001/ijphm.2013.v4i3.2151
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Contributions
NA.
Corresponding author
Ethics declarations
Conflict of interest
The authors have not disclosed any competing interests.
Ethical approval
NA.
Consent to participate
NA.
Consent for publication
NA.
Data availability
NA.
Code availability
NA.
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 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
Gawde, S., Patil, S., Kumar, S. et al. A sco** review on multi-fault diagnosis of industrial rotating machines using multi-sensor data fusion. Artif Intell Rev 56, 4711–4764 (2023). https://doi.org/10.1007/s10462-022-10243-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-022-10243-z