{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:12:06Z","timestamp":1760242326797,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,4,25]],"date-time":"2017-04-25T00:00:00Z","timestamp":1493078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Roller bearings are the most widely used and easily damaged mechanical parts in rotating machinery. Their running state directly affects rotating machinery performance. Empirical mode decomposition (EMD) easily occurs illusive component and mode mixing problem. From the view of feature extraction, a new feature extraction method based on integrating ensemble empirical mode decomposition (EEMD), the correlation coefficient method, and Hilbert transform is proposed to extract fault features and identify fault states for motor bearings in this paper. In the proposed feature extraction method, the EEMD is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with different frequency components. Then the correlation coefficient method is used to select the IMF components with the largest correlation coefficient, which are carried out with the Hilbert transform. The obtained corresponding envelope spectra are analyzed to extract the fault feature frequency and identify the fault state by comparing with the theoretical value. Finally, the fault signal transmission performance of vibration signals of the bearing inner ring and outer ring at the drive end and fan end are deeply studied. The experimental results show that the proposed feature extraction method can effectively eliminate the influence of the mode mixing and extract the fault feature frequency, and the energy of the vibration signal in the bearing outer ring at the fan end is lost during the transmission of the vibration signal. It is an effective method to extract the fault feature of the bearing from the noise with interference.<\/jats:p>","DOI":"10.3390\/sym9050060","type":"journal-article","created":{"date-parts":[[2017,4,25]],"date-time":"2017-04-25T13:21:12Z","timestamp":1493126472000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Fault Feature Extraction Method for Motor Bearing and Transmission Analysis"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6524-6760","authenticated-orcid":false,"given":"Wu","family":"Deng","sequence":"first","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Sichuan Provincial Key Laboratory of Process Equipment and Control, Sichuan University of Science and Engineering, Zigong 64300, China"},{"name":"The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China"},{"name":"Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China"},{"name":"Dalian Key Laboratory of Welded Structures and Its Intelligent Manufacturing Technology (IMT) of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, China"}]},{"given":"Huimin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Sichuan Provincial Key Laboratory of Process Equipment and Control, Sichuan University of Science and Engineering, Zigong 64300, China"},{"name":"The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China"},{"name":"Traction Power State Key Laboratory of Southwest Jiaotong University, Chengdu 610031, China"},{"name":"Dalian Key Laboratory of Welded Structures and Its Intelligent Manufacturing Technology (IMT) of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, China"}]},{"given":"Xinhua","family":"Yang","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian 116028, China"},{"name":"Dalian Key Laboratory of Welded Structures and Its Intelligent Manufacturing Technology (IMT) of Rail Transportation Equipment, Dalian Jiaotong University, Dalian 116028, China"}]},{"given":"Chang","family":"Dong","sequence":"additional","affiliation":[{"name":"Software Institute, Dalian Jiaotong University, Dalian 116028, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1109\/TEC.2005.847955","article-title":"Condition monitoring and fault diagnosis of electrical motors\u2014A review","volume":"20","author":"Nandi","year":"2005","journal-title":"IEEE Trans. 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