Document Type : Original Article
Authors
1 School of Industrial engineering, K.N.Toosi University of Technology,Tehran,Iran
2 Professor of industrial engineering, K.N. Toosi University of Technology, Tehran, Iran.
Abstract
In the Engineering discipline, predictive maintenance techniques play an essential role in improving system safety and reliability of industrial machines. Due to the adoption of crucial and emerging detection techniques and big data analytics tools, data fusion approaches are gaining popularity. This article thoroughly reviews the recent progress of data fusion techniques in predictive maintenance, focusing on their applications in machinery fault diagnosis. In this review, the primary objective is to classify existing literature and to report the latest research and directions to help researchers and professionals to acquire a clear understanding of the thematic area. This paper first summarizes fundamental data-fusion strategies for fault diagnosis. Then, a comprehensive investigation of the different levels of data fusion was conducted on fault diagnosis of industrial machines. In conclusion, a discussion of data fusion-based fault diagnosis challenges, opportunities, and future trends are presented.
Keywords
[1] Chen, H., B. Jiang, and N. Lu, Data-driven incipient sensor fault estimation with application in the inverter of high-speed railway. Mathematical Problems in Engineering, 2017. 2017.
[2] Huo, Z., et al., Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access, 2017. 5: p. 19442-19456.
[3] Salim, M. and M.J. Khosrowjerdi, Data-driven H_infinity Controller/Detector Design for a Quadruple Tank Process. Journal of Control Engineering and Applied Informatics, 2017. 19(1): p. 3-14.
[4] Munikoti, S., et al., Data-driven approaches for diagnosing incipient faults in dc motors. IEEE Transactions on Industrial Informatics, 2019. 15(9): p. 5299-5308.
[5] Tao, X., et al., Bearings fault detection using wavelet transform and generalized Gaussian density modeling. Measurement, 2020. 155: p. 107557.
[6] Cheng, C. et al., Data-driven incipient fault detection and diagnosis for the running gear in high-speed trains. IEEE Transactions on Vehicular Technology, 2020. 69(9): p. 9566-9576.
[7] Chen, H., et al., Data-driven and deep learning-based detection and diagnosis of incipient faults with application to electrical traction systems. Neurocomputing, 2020. 396: p. 429-437.
[8] Zonta, T., et al., Predictive maintenance in the industry 4.0: A systematic literature review. Computers & Industrial Engineering, 2020. 150: p. 106889.
[9] Carvalho, T.P., et al., A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 2019. 137: p. 106024.
[10] Boström, H., et al., On the definition of information fusion as a field of research. 2007, Institutionen för kommunikation och information.
[11] Abidi, M.A., and R.C. Gonzalez, Data fusion in robotics and machine intelligence. 1992: Academic Press Professional, Inc.
[12] Fu, C., et al. Research and implementation of fast identity registration system based on audio-visual fusion. 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). 2017. IEEE.
[13] Sala, D.A., et al. Positioning control on a collaborative robot by sensor fusion with liquid state machines. in 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). 2017. IEEE.
[14] Meng, T., et al., A survey on machine learning for data fusion. Information Fusion, 2020. 57: p. 115-129.
[15] Kumar, D.P., T. Amgoth, and C.S.R. Annavarapu, Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 2019. 49: p. 1-25.
[16] Lin, H. and S. Sun, An overview of multi-rate multisensor systems: Modelling and estimation. Information Fusion, 2019. 52: p. 335-343.
[17] Chen, Z. and W. Li, Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Transactions on Instrumentation and Measurement, 2017. 66(7): p. 1693-1702.
[18] Lei, Y., et al., Applying machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 2020. 138: p. 106587.
[19] Lei, Y., et al., A deep learning-based method for machinery health monitoring with big data. Journal of Mechanical Engineering, 2015. 51(21): p. 49-56.
[20] Azamfar, M., et al., Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis. Mechanical Systems and Signal Processing, 2020. 144: p. 106861.
[21] Jiang, Q., et al., new fault recognition method for rotary machinery based on information entropy and a probabilistic neural network. Sensors, 2018. 18(2): p. 337.
[22] Haidong, S., et al., Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge-Based Systems, 2018. 140: p. 1-14.
[23] Purushotham, V., S. Narayanan, and S.A. Prasad, Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model-based fault recognition. Pdt & E International, 2005. 38(8): p. 654-664.
[24] Lei, Y., et al., A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical systems and signal processing, 2013. 35(1-2): p. 108-126.
[25] Abdelkader, R., A. Kaddour, and Z. Derouiche, Enhancement of rolling bearing fault diagnosis based on the improvement of empirical mode decomposition denoising method. The International Journal of Advanced Manufacturing Technology, 2018. 97(5): p. 3099-3117.
[26] Wang, X.-B., Z.-X. Yang, and X.-A. Yan, Novel particle swarm optimization-based variational mode decomposition method for the fault diagnosis of complex rotating machinery. IEEE/ASME Transactions on Mechatronics, 2017. 23(1): p. 68-79.
[27] Zhang, S. et al., Bearing fault diagnosis based on variational mode decomposition and total variation denoising. Measurement Science and Technology, 2016. 27(7): p. 075101.
[28] Wang, L., et al., Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 2018. 103: p. 60-75.
[29] Shi, P., X. Ma, and D. Han, A weak fault diagnosis method for rotating machinery based on compressed sensing and stochastic resonance. Journal of Vibroengineering, 2019. 21(3): p. 654-664.
[30] Li, Z. et al., A multi-parameter constrained potential underdamped stochastic resonance method and its application for weak fault diagnosis. Journal of Sound and Vibration, 2019. 459: p. 114862.
[31] Wang, H. and P. Chen, Fault diagnosis for a rolling bearing used in a reciprocating machine by adaptive filtering technique and fuzzy neural network. WSEAS Transactions on Systems, 2008. 7(1): p. 1-6.
[32] Liao, Z., et al., An automatic filtering method based on an improved genetic algorithm—with application to rolling bearing fault signal extraction. IEEE Sensors Journal, 2017. 17(19): p. 6340-6349.
[33] Long, J., et al., Applications of fractional lower order S transform time-frequency filtering algorithm to machine fault diagnosis. PloS one, 2017. 12(4): p. e0175202.
[34] Liu, Z.C., L.W. Tang, and L.J. Cao. A feature extraction method for rolling bearing’s week fault based on Kalman filter and FSK. in Applied Mechanics and Materials. 2014. Trans Tech Publ.
[35] Zhang, K., et al., Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks. Neurocomputing, 2011. 74(17): p. 2941-2952.
[36] Kohavi, R. and G.H. John, Wrappers for feature subset selection. Artificial intelligence, 1997. 97(1-2): p. 273-324.
[37] Osman, H., M. Ghafari, and O. Nierstrasz. Automatic feature selection by regularization to improve bug prediction accuracy. In 2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE). 2017. IEEE.
[38] Žvokelj, M., S. Zupan, and I. Prebil, Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis. Mechanical Systems and Signal Processing, 2010. 24(4): p. 1049-1067.
[39] Wang, H. et al., Research on rolling bearing state health monitoring and life prediction based on PCA and Internet of things with multi-sensor. Measurement, 2020. 157: p. 107657.
[40] Li, J., et al., multi-source feature extraction of rolling bearing compression measurement signal based on independent component analysis. Measurement, 2021. 172: p. 108908.
[41] Shakya, P., M.S. Kulkarni, and A.K. Darpe, A novel methodology for online detection of bearing health status for naturally progressing defect. Journal of Sound and Vibration, 2014. 333(21): p. 5614-5629.
[42] Dong, Z., et al. Transformer fault diagnosis based on factor analysis and gene expression programming. In 2011 International Conference on Advanced Power System Automation and Protection. 2011. IEEE.
[43] Li, X., et al., Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor. Control Engineering Practice, 2018. 72: p. 177-191.
[44] Zhou, Y., et al., Rolling bearing fault diagnosis using transient-extracting transform and linear discriminant analysis. Measurement, 2021. 178: p. 109298.
[45] Hoang, D.-T. and H.-J. Kang, A survey on deep learning based bearing fault diagnosis. Neurocomputing, 2019. 335: p. 327-335.
[46] Xu, Z., C. Li, and Y. Yang, Fault diagnosis of rolling bearing of wind turbines based on the variational mode decomposition and deep convolutional neural networks. Applied Soft Computing, 2020. 95: p. 106515.
[47] Che, C., et al., Domain adaptive deep belief network for rolling bearing fault diagnosis. Computers & Industrial Engineering, 2020. 143: p. 106427.
[48] Li, C., et al., Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition. Information Processing & Management, 2020. 57(3): p. 102185.
[49] LeCun, Y. and Y. Bengio, Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 1995. 3361(10): p. 1995.
[50] Krizhevsky, A., I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012. 25.
[51] Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
[52] Simonyan, K. and A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[53] He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[54] Angelou, M., et al., Graph-based multimodal fusion with metric learning for multimodal classification. Pattern Recognition, 2019. 95: p. 296-307.
[55] Li, C., et al., A Bayesian approach to consequent parameter estimation in probabilistic fuzzy systems and its application to bearing fault classification. Knowledge-based systems, 2017. 129: p. 39-60.
[56] Yu, J., Health condition monitoring of machines based on hidden Markov model and contribution analysis. IEEE Transactions on Instrumentation and Measurement, 2012. 61(8): p. 2200-2211.
[57] Shatnawi, Y. and M. Al-Khassaweneh, Fault diagnosis in internal combustion engines using extension neural network. IEEE Transactions on Industrial Electronics, 2013. 61(3): p. 1434-1443.
[58] Unal, M., et al., Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement, 2014. 58: p. 187-196.
[59] Li, C., et al., Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing, 2015. 168: p. 119-127.
[60] Zheng, J., H. Pan, and J. Cheng, rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mechanical Systems and Signal Processing, 2017. 85: p. 746-759.
[61] Pan, Z., et al., A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings. Mechanical Systems and Signal Processing, 2020. 144: p. 106899.
[62] Li, L., et al., Exploration of classification confidence in ensemble learning. Pattern recognition, 2014. 47(9): p. 3120-3131.
[63] Bonab, H. and F. Can, Less is more: A comprehensive framework for the number of components of ensemble classifiers. IEEE Transactions on neural networks and learning systems, 2019. 30(9): p. 2735-2745.
[64] Xu, G., et al., Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning. Sensors, 2019. 19(5): p. 1088.
[65] Wang, Z.-Y., C. Lu, and B. Zhou, Fault diagnosis for rotary machinery with selective ensemble neural networks. Mechanical Systems and Signal Processing, 2018. 113: p. 112-130.
[66] Yu, G., et al., Semi-supervised classification based on random subspace dimensionality reduction. Pattern Recognition, 2012. 45(3): p. 1119-1135.
[67] Sinha, J.K. and K. Elbhbah, A future possibility of vibration-based condition monitoring of rotating machines. Mechanical Systems and Signal Processing, 2013. 34(1-2): p. 231-240.
[68] Zhao, X., M. Jia, and M. Lin, Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery. Measurement, 2020. 152: p. 107320.
[69] Meng, Z., et al., An enhancement denoising autoencoder for rolling bearing fault diagnosis. Measurement, 2018. 130: p. 448-454.
[70] Kong, X., et al., A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings. Measurement, 2020. 151: p. 107132.
[71] Liang, P., et al., Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. Computers in Industry, 2019. 113: p. 103132.
[72] Zhang, Y., et al., An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. Measurement, 2020. 157: p. 107667.
[73] Chen, Z., K. Gryllias, and W. Li, Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mechanical systems and signal processing, 2019. 133: p. 106272.
[74] Shao, H., et al., Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mechanical Systems and Signal Processing, 2018. 100: p. 743-765.
[75] Zhang, W., X. Li, and Q. Ding, Deep residual learning-based fault diagnosis method for rotating machinery. ISA transactions, 2019. 95: p. 295-305.
[76] Du, Y., et al., Fault diagnosis under variable working conditions based on STFT and transfer deep residual network. Shock and Vibration, 2020. 2020.
[77] Sun, R.-B., et al., Planetary gearbox spectral modeling based on the hybrid method of dynamics and LSTM. Mechanical Systems and Signal Processing, 2020. 138: p. 106611.
[78] Wang, B., et al., Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery. Neurocomputing, 2020. 379: p. 117-129.
[79] Yu, K., et al., A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning. Mechanical Systems and Signal Processing, 2021. 146: p. 107043.
[80] García-Holgado, A. and F.J. García-Peñalvo, Validation of the learning ecosystem metamodel using transformation rules. Future Generation Computer Systems, 2019. 91: p. 300-310.
[81] Vázquez-Ingelmo, A., et al., A meta-model integration for supporting knowledge discovery in specific domains: a case study in healthcare. Sensors, 2020. 20(15): p. 4072.
[82] Huang, M., Z. Liu, and Y. Tao, Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simulation Modelling Practice and Theory, 2020. 102: p. 101981.
[83] Jing, L., et al., An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox. Sensors, 2017. 17(2): p. 414.
)