{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T05:08:46Z","timestamp":1723871326539},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,3]],"date-time":"2018-10-03T00:00:00Z","timestamp":1538524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery.<\/jats:p>","DOI":"10.3390\/s18103323","type":"journal-article","created":{"date-parts":[[2018,10,4]],"date-time":"2018-10-04T06:19:49Z","timestamp":1538633989000},"page":"3323","source":"Crossref","is-referenced-by-count":46,"title":["A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology"],"prefix":"10.3390","volume":"18","author":[{"given":"Huimin","family":"Zhao","sequence":"first","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Yantai 264005, China"},{"name":"Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China"},{"name":"Liaoning Key Laboratory of Welding and Reliability of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, China"}]},{"given":"Shaoyan","family":"Zuo","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian 116028, China"}]},{"given":"Ming","family":"Hou","sequence":"additional","affiliation":[{"name":"Chuzhou Technical Supervision and Testing Center, Chuzhou 239000, China"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"China Household Electric Appliance Research Institute, Beijing 100037, China"}]},{"given":"Ling","family":"Yu","sequence":"additional","affiliation":[{"name":"China Household Electric Appliance Research Institute, Beijing 100037, China"}]},{"given":"Xinhua","family":"Yang","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Liaoning Key Laboratory of Welding and Reliability of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, China"}]},{"given":"Wu","family":"Deng","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Yantai 264005, China"},{"name":"Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China"},{"name":"Liaoning Key Laboratory of Welding and Reliability of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21918","DOI":"10.1109\/ACCESS.2017.2763172","article-title":"Time-frequency analysis of torsional vibration signals in resonance region for planetary gearbox fault diagnosis under variable speed conditions","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.ymssp.2015.03.014","article-title":"Time-frequency demodulation analysis based on iterative generalized demodulation for fault diagnosis of planetary gearbox under nonstationary conditions","volume":"62","author":"Feng","year":"2015","journal-title":"Mech. 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