LPF/OGS: A Low-Pass Filtering and Overlapping Group Shrinkage Denoising Method for Diesel Engine Fault Diagnosis

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 low-pass filtering (LPF)/overlapping group shrinkage (OGS) (the simultaneously LPF and OGS denoising method) is proposed. For filtering low-frequency components, a zero-phase noncausal recursive filter that takes the form of a banded matrix is employed. Meanwhile, the nonconvex 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.