Abstract
The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed, and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.
Highlights
As a key component of rotating machinery[1], rolling bearings are directly related to the operation of mechanical equipment and parts and instruments[2]
The traditional method is to denoise the original signal by selecting an appropriate threshold K, and retaining the SCs corresponding to the top K largest singular values, namely: k x = ∑ xi xis the reconstructed denoising signal, and the determination of effective singular components (SCs) has always been a hot issue and difficult problem in the application of singular value decomposition
This paper proposes a new weak feature enhancement method based on singular value decomposition
Summary
As a key component of rotating machinery[1], rolling bearings are directly related to the operation of mechanical equipment and parts and instruments[2]. The existing SVD-based noise reduction methods are usually based on finding a suitable threshold to reconstruct the low-rank matrix for the subsequent processing[4]. In the case of weak bearing faults, the traditional differential singular value decomposition noise reduction method (DS-SVD) can’t find the correct threshold well. The arrangement of this paper is as follows: section 2 introduces the basic principles of the SVD noise reduction and the construction method of Hankel matrix; section 3 introduces the new indicator FIC, and elaborates specific steps of the proposed method FIC-SVD; section 4 builds a bearing fault simulation signal and compares the proposed method with the traditional method; section 5 applies this method to actual bearing test data; section 6 draws a conclusion
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