Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 69, Issue 3, Pages 770-781Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2019.2905043
Keywords
Compound fault; fault diagnosis; feature extraction; gearbox; sparse decomposition
Funding
- China Scholarship Council State Scholarship Fund [201706920010]
- National Science Foundation for Young Scientists of China [51405320]
- National Natural Science Foundation of China [51405320, 51875376]
- Postgraduate Research and Practice Innovation Program of Jiangsu Province [SJKY19_2276]
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The vibration monitoring of gearboxes is an effective means of ensuring the long-term safe operation of rotating machinery. A gearbox may have more than one fault in actual applications. Therefore, gearbox compound fault diagnosis should be investigated. In this paper, a novel multiple enhanced sparse decomposition (MESD) method is proposed to address multiple feature extraction for gearbox compound fault vibration signals. Through this method, a novel MESD algorithm is utilized to simultaneously separate and extract the harmonic components and transient features of the gear and bearing from the compound fault signal. Three subdictionaries are specially constructed according to the gearbox failure mechanism to accurately extract each feature component. Meanwhile, the generalized minimax concave (GMC) penalty is used as sparse regularization to further ensure the accuracy of sparse decomposition. The simulation and engineering signals of the gearbox validate the performance of the proposed MESD method.
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