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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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.
fault diagnosis,low-pass filter,sparse optimization,feature extraction,convex optimization
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