Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 18, Issue 4, Pages 2264-2273Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3099060
Keywords
Feature extraction; Convolution; Fault diagnosis; Logic gates; Vibrations; Deep learning; Wavelet packets; Fault diagnosis; gated dynamic sparsity; gearbox; multisensor fusion; wavelet packet transform (WPT)
Categories
Funding
- Chongqing Science and Technology Commission [cstc2019jcyj-zdxmX0026]
- National Key Research and Development Program of China [2020YFB1709800]
- National Science Foundation of China [51775065, TII-21-0139]
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This article proposes a new method for gearbox fault diagnosis using multisensor fusion. The residual gated dynamic sparse network is used to improve feature learning and fusion ability. Experimental results and engineering application show that this method is more effective than others under noise interference.
This article proposes a new multisensor fusion fault diagnosis method for gearbox, namely residual gated dynamic sparse network, to improve the multisensor feature learning and fusion ability. Considering that the fault sensitivity of the sensor varies with mounted location and complex transfer path modulation causes information from multisensor redundant, the lightweight channel attention unit is designed to strengthen the feature extraction ability of the network. The developed gated dynamic sparse unit is inserted into the deep architecture to eliminate ineffective components caused by high noise interference. Besides, the loss function is improved with multiple activation criteria to enhance convergence ability. The results of experiments and the engineering application show that the proposed method is more effective than other methods under varying degrees of noise interference.
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