Abstract
Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.
Highlights
Bearings are widely applied to rotating machinery equipment, such as motors and pumps
A wide range of fault feature extraction methods have been explored for bearing fault diagnosis. ey include filtering methods [5], signal decomposition methods [6], statistical analysis methods [7], and stochastic resonance [8]
Beetle antennae search (BAS) is a type of metaheuristic intelligent optimization method that is based on group optimization [20, 21]. e BAS algorithm imitates the function of antennae and the random walking mechanism of beetles in nature
Summary
Bearings are widely applied to rotating machinery equipment, such as motors and pumps. Is method decomposes a signal into several different mode components and reconstructs the signal by extracting the effective modal component that contains sufficient fault information to enhance the fault feature. SVD is effective in fault feature extraction and has excellent stability and invariability In this method, a singular value (SV) can present a signal’s intrinsic characteristics and promote the signal-tonoise ratio (SNR). SVD can extract fault features effectively against strong background noise. E extracted fault feature signal matrix is reconstructed by minimizing the asymptotic loss and performs better than that resulting from the ordinary reduction of SVs by thresholding methods [17]. E contributions of this work are as follows: (1) the designed probability-frequency density information criterion (PFDIC) can effectively extract fault features; (2) the proposed algorithm can efficiently select the best ED and SV. By selecting and directly adding SVs representing fault information can fault feature extraction be realized
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