An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria

Abstract

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method.

Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis

“…In view of this issue, reference [29] introduces a new tensor decomposition model named tensor singular value decomposition (TSVD) and [30] defines a new tensor tubal rank. Similar to matrix SVD [31], the singular value tubes (SVTs) decomposed by TSVD can represent singular subspace of different feature component or noise components in the raw signals. Hence, by selecting an appropriate reconstructed order of SVTs, the relatively clean fault feature signals can be extracted.…”

“…It is generally accepted that the first few orders of SVTs dominate the fault feature information [32]. Currently, the selection criterion mainly depends on experience [31] or finding the turning point of SVTs, such as difference spectra [35]. However, the interference features can affect the determination of the turning point.…”

An Effective Bearing Fault Diagnosis Technique via Local Robust Principal Component Analysis and Multi-Scale Permutation Entropy

“…Currently, the selection of singular values representing fault feature components still depends on experience, which may lead to considerable error. Especially for the early weak faults of the bearings, the singular values representing different feature components are almost impossible to be identified [ 25 ]. The classical manifold learning theory holds that the feature component of the signal matrix has a lower intrinsic dimension, which is distributed in a low-dimensional submanifold of a high dimensional phase space [ 26 , 27 ].…”

Rolling Bearing Incipient Fault Detection Based on a Multi-Resolution Singular Value Decomposition

“…Therefore, SVD-based signal processing methods have been proposed, either for noise reduction or classification recognition. The SVD based methods have been also widely used for bearings and rotor-related fault detection to certain success [41][42][43][44][45]. The background noise, the harmonic interference, and the periodic shock vibration have different singular value characteristics after the singular value decomposition (SVD).…”