LPF/OGS: A low-pass filtering and overlapping group shrinkage denoising method for diesel engine fault diagnosis

Wangpeng He, Xiaoya Guo, Mingxuan Li,Mingquan Zhang,Binqiang Chen

IEEE Sensors Journal(2024)

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摘要
The signal generated by a diesel engine is considered as relatively intricate mechanical signal due to its susceptibility to interference from both low-pass components and significant background noise. In order to improve the feature extraction performance, a novel method termed LPF/OGS (the simultaneously low-pass filtering and OGS denoising method) is proposed. For filtering low-frequency components, a zero-phase non-causal recursive filter that takes the form of a banded matrix is employed. Meanwhile, the non-convex regularization technique, which incorporates the group sparse structure is adopted to capture the tendency of clustering. Moreover, the whole formulated objective function is constrained as a convex optimization problem. As a result, the sparsity of useful features is maximally promoted while the noise between adjacent pulses is eliminated. The proposed method is applied to a simulated signal and a real signal collected from the diesel engine. The processing results provide compelling evidence that the proposed method outperforms traditional methods in accurately extracting transient impulses for machinery fault diagnosis.
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关键词
fault diagnosis,low-pass filter,sparse optimization,feature extraction,convex optimization
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