Bearing Fault Diagnosis Based on Improved Morlet Wavelet Transform and Shallow Residual Neural Network
Source abstract
Abstract
To solve the problem of the Morlet wavelet transform not satisfying wavelet admissibility conditions, which results in fuzzy and aliasing time–frequency image features, this paper proposes an improved Morlet wavelet. The scale parameter is the wavelet’s dominant frequency parameter. It largely satisfies the admissibility conditions and effectively improves the fuzzy aliasing effect of time–frequency image features. To further improve feature learning, this paper proposes a combined Morlet wavelet and residual network. The experimental results show that the accuracy of the proposed method is 10%, 4.5%, and 3% higher than that of LeNets, CNNs, and ResNets under varying load and noise disturbance levels, respectively.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Fault Detection and Control Systems · Mineral Processing and Grinding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Control Science and Engineering, Tiangong University, Tianjin 300387, China
Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tianjin 300387, China
Funding
National Natural Science Foundation of China
References (21)
Show source-provided references
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