A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
作者： Omar AlShorman , Muhammad Irfan , Nordin Saad , D. Zhen , Noman Haider
DOI: 10.1155/2020/8843759
关键词: Induction motor 、 Automation 、 Field (computer science) 、 Fault (power engineering) 、 Bearing (mechanical) 、 Fault detection and isolation 、 Computer science 、 Artificial intelligence 、 Condition monitoring 、 Expert system
摘要: The fault detection and diagnosis (FDD) along with condition monitoring (CM) of rotating machinery (RM) have critical importance for early to prevent severe damage infrastructure in industrial environments. Importantly, valuable equipment needs continuous enhance the safety, reliability, availability decrease cost maintenance modern systems applications. However, induction motor (IM) has been extensively used several processes because it is cheap, reliable, robust. Rolling bearings are considered be main component IM. Undoubtedly, any failure this basic can lead a serious breakdown IM whole system. Thus, many current methods based on different techniques employed as prognosis rolling elements bearing Moreover, these include signal/image processing, intelligent diagnostics, data fusion, mining, expert time frequency well time-frequency domains. Artificial intelligence (AI) proven their significance every field digital technology. Industrial machines, automation, net frontiers AI adaptation. There quite developed literatures that approaching issues using signals processing techniques. key contribution work present an extensive review CM FDD IM, especially bearings, artificial methods. This study highlights advantages performance limitations each method. Finally, challenges future trends also highlighted.
hindawi.com参考文章(222)
A. Agoston, C. Schneidhofer, N. Dörr, B. Jakoby, A concept of an infrared sensor system for oil condition monitoring Elektrotechnik Und Informationstechnik. ,vol. 125, pp. 71- 75 ,(2008) , 10.1007/S00502-008-0506-3
Amin Taheri-Garavand, Hojjat Ahmadi, Mahmoud Omid, Seyed Saeid Mohtasebi, Kaveh Mollazade, Alan John Russell Smith, Giovanni Maria Carlomagno, An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique Applied Thermal Engineering. ,vol. 87, pp. 434- 443 ,(2015) , 10.1016/J.APPLTHERMALENG.2015.05.038
Zhiwei Gao, Carlo Cecati, Steven X. Ding, A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches IEEE Transactions on Industrial Electronics. ,vol. 62, pp. 3757- 3767 ,(2015) , 10.1109/TIE.2015.2417501
Shuang Pan, Tian Han, Andy C. C. Tan, Tian Ran Lin, Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm Shock and Vibration. ,vol. 2016, pp. 1- 12 ,(2016) , 10.1155/2016/5836717
Robert B. Randall, Jérôme Antoni, Rolling element bearing diagnostics—A tutorial Mechanical Systems and Signal Processing. ,vol. 25, pp. 485- 520 ,(2011) , 10.1016/J.YMSSP.2010.07.017
N.H Abu-Zahra, G Yu, Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves International Journal of Machine Tools and Manufacture. ,vol. 43, pp. 337- 343 ,(2003) , 10.1016/S0890-6955(02)00274-2
Xiaoyuan Zhang, Yitao Liang, Jianzhong Zhou, Yi zang, A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM Measurement. ,vol. 69, pp. 164- 179 ,(2015) , 10.1016/J.MEASUREMENT.2015.03.017
Achmad Widodo, Bo-Suk Yang, Dong-Sik Gu, Byeong-Keun Choi, Intelligent fault diagnosis system of induction motor based on transient current signal Mechatronics. ,vol. 19, pp. 680- 689 ,(2009) , 10.1016/J.MECHATRONICS.2009.02.002
Tianyang Wang, Ming Liang, Jianyong Li, Weidong Cheng, Chuan Li, Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification Mechanical Systems and Signal Processing. ,vol. 62, pp. 30- 53 ,(2015) , 10.1016/J.YMSSP.2015.03.005
Behrad Bagheri, Hojat Ahmadi, Reza Labbafi, Application of data mining and feature extraction on intelligent fault diagnosis by Artificial Neural Network and k-nearest neighbor international conference on electrical machines. pp. 1- 7 ,(2010) , 10.1109/ICELMACH.2010.5607984