Abstract
In this paper, comparative combined fault diagnosis schemes are studied including vibration analysis, acoustic signal analysis and thermal image analysis based on the Convolutional Neural Network (CNN). The advantage of the CNN structure is that it does not need manual feature extraction or selection, which requires prior knowledge of specific machinery dynamics. The vibration and acoustic signals were transformed into spectrograms, which are effective for the diagnostic analysis by using CNN. Comparatively, the thermal images were directly analyzed using CNN. The effectiveness of the CNN-based diagnosis methods was investigated through the analysis of different experimental data, i.e., vibration, acoustic signals and thermal images, which were collected from a test rig where different types of faults are induced on the roller bearing and shaft. The results show that the thermal image analysis and acoustic signal analysis could achieve relatively higher accuracy rate compared to vibration analysis. Moreover, the advantage is easy-deployment because of the non-contact way during signal acquisition. With the CNN-based fault diagnosis method for the three different signals collected, the accuracy of different signal predictions for combined faults can be compared, and the effective method can be applied to fault diagnosis of other industrial rotating machinery.
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References
McKinsey and company: The Internet of Things mapping the value beyond the hype. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world,last. Accessed 1 Sept 2018
Janssens, O., et al.: Convolutional neural network based fault detection for rotating machinery. J. Sound Vibr. 377, 331–345 (2016)
Chahal, B., Ahmad, S., Rana, A.S., Verma, A., Goyat, N.S.: Fault diagnosis of bearing by the application of acoustic signal. Invertis J. Sci. Technol. 5, 40–44 (2012)
Huo, Z., Zhang, Y., Shu, L., Gallimore, M.: A new bearing fault diagnosis method based on fine-to-coarse multiscale permutation entropy, laplacian score and SVM. IEEE Access 7, 17050–17066 (2019)
Ashish, V.: Review on thermal image processing tecniques for machine condition monitoring. Int. J. Wireless Commun. Netw. Technol. 3, 49–53 (2014)
Xia, M., Li, T., Xu, L., Liu, L., de Silva, C.W.: Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans. Mechatron. 23(1), 101–110 (2018)
Touret, T., Changenet, C., Ville, F., Lalmi, M., Becquerelle, S.: On the use of temperature for online condition monitoring of geared systems–a review. Mech. Syst. Signal Process. 101, 197–210 (2018)
Adam, G.: Fault diagnosis of single-phase induction motor based on acoustic signals. Mech. Syst. Signal Process. 117, 65–80 (2019)
Janssens, O., Van de Walle, R., Loccufier, M., Van Hoecke, S.: Deep learning for infrared thermal image based machine health monitoring. IEEE/ASME Trans. Mechatron. 23(1), 151–159 (2018)
Dai, X., Gao, Z.: From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans. Ind. Inf. 23(1), 2226–2238 (2013)
Zhang, Y., Bingham, C., Yang, Z., Ling, B.W.K., Gallimore, M.: Machine fault detection by signal denoising—with application to industrial gas turbines. Measurement 58, 230–240 (2014)
Yuan, L., He, Y., Huang, J., Sun, Y.: A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Trans. Instrum. Meas. 59(3), 586–595 (2010)
Chen, J., et al.: Wavelet transform based on inner product in fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process. 70, 1–35 (2016)
Lei, Y., Lin, J., He, Z., Zuo, M.J.: A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 35(1), 108–126 (2013)
Hu, Q., Zhang, S., Yang, S.: Variable condition bearing fault diagnosis based on time-domain and artificial intelligence. In: Applied Mechanics and Materials, vol. 203, pp. 329–333 (2012)
Sreejith, B., Verma, A.K., Srividya, A.: Fault diagnosis of rolling element bearing using time-domain features and neural networks. In: IEEE Region 10 and the Third International Conference on Industrial and Information Systems, pp. 1–6 (2016)
Mao, K., Wu, Y.: Fault diagnosis of rolling element bearing based on vibration frequency analysis. In: 2011 Third International Conference on Measuring Technology and Mechatronics Automation, pp. 198–201 (2011)
Jiang, Z., Jiao, W., Meng, S.: Fault diagnosis method of time domain and time-frequency domain based on information fusion. In: Applied Mechanics and Materials, vol. 300, pp. 635–639 (2013)
Cao, M., Pan, H., Chang, X.: Research on automatic fault diagnosis based on time-frequency characteristics and PCASVM. In: International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 593–598 (2016)
Jardine, A.K., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 108, 1483–1510 (2006)
Younus, A.M., Yang, B.S.: Intelligent fault diagnosis of rotating machinery using infrared thermal image. Expert Syst. Appl. 39(2), 2082–2091 (2012)
Liu, R., Yang, B., Zio, E., Chen, X.: Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process. 108, 33–47 (2018)
Konar, P., Chattopadhyay, P.: Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Appl. Soft Comput. 11(6), 4203–4211 (2011)
Babu, T.R., Sekhar, A.S.: Shaft crack identification using artificial neural networks and wavelet transform data of a transient rotor. Adv. Vib. Eng 9, 207–214 (2010)
Xie, Y., Zhang, T.: Fault diagnosis for rotating machinery based on convolutional neural network and empirical mode decomposition. Shock Vibr. (2017)
Sharma DataCamp/aditya: Convolutional Neural Networks in Python with Keras. https://www.datacamp.com/community/tutorials/convolutional-neural-networks-python. Accessed 1 Sept 2018
PT 500 machinery diagnostic system. https://www.gunt.de/index.php?option=com_gunt&task=gunt.list.category&lang=en&category_id=77. Accessed 25 Mar 2015
Huo, Z., Zhang, Y., Francq, P., Shu, L., Huang, J.: Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access 5, 19442–19456 (2017)
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Li, M., Huo, Z., CAUS, F., Zhang, Y. (2019). Comparative Study of Combined Fault Diagnosis Schemes Based on Convolutional Neural Network. In: Cheng, X., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1058. Springer, Singapore. https://doi.org/10.1007/978-981-15-0118-0_52
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DOI: https://doi.org/10.1007/978-981-15-0118-0_52
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