Bearing fault diagnosis based on kernel independent component analysis and antlion optimization

Abstract

Fault diagnosis of gearboxes based on vibration signal processing is challenging, as vibration signals collected by acceleration sensors are typically a nonlinear mixture of unknown signals. Furthermore, the number of source signals is usually larger than that of sensors because of the practical limitation on sensor positions. Hence, the fault characterization is actually a nonlinear underdetermined blind source separation (NUBSS) problem. In this paper, a novel NUBSS algorithm based on kernel independent component analysis (KICA) and antlion optimization (ALO) is proposed to address the technical challenge. The mathematical model demonstrates the nonlinear mixing of source signals in the underdetermined cases. Ensemble empirical mode decomposition is used as a preprocessing tool to decompose the observed signals into a set of intrinsic mode functions that suffers from the problem of redundant components. The correlation coefficient is utilized to eliminate the redundant components. An adaptive threshold singular value decomposition method is proposed to estimate the number of source signals. Then a whitening process is carried out to transform the overdetermined blind source separation (BSS) into determined BSS, which can be solved by the KICA method. However, the reasonable selection of parameters in KICA limits its application to some extent. Therefore, ALO and Fisher’s linear discriminant analysis are adopted to further enhance the accuracy of the KICA method. The separation performance of the proposed method is assessed through simulation. The numerical results show that the proposed method can accurately estimate the number of source signals and attains a higher separation quality in tackling nonlinear mixed signals when compared with the existing methods. Finally, the inner ring fault experiment is conducted to preliminarily validate the practicability of the proposed method in bearing fault diagnosis.

Rolling Bearing Fault Diagnosis Based on Nonlinear Underdetermined Blind Source Separation

“…In order to obtain accurate source recovery, the source number needs to be determined before the mixing matrix estimation. In previous research, some common information−based methods, such as Akaike information criterion and Bayesian information criterion, have been typically utilized to estimate the number of sources [14]. Nevertheless, these methods are only effective for estimating the source number in the white noise environment, but are invalid in the color noise environment.…”

“…To overcome the mode mixing issue, an improved EMD named EEMD was proposed by Wu and Huang [19]. The basic idea is to add several instances of white noise to the raw signal, so that the components of different scales can be automatically projected to the proper scale related to the white noise [14]. Given the observed signal, e(t), the procedures of EEMD are listed as follows:…”

“…Another one takes advantage of the sparsity property of signals in the sparse domain, typically known as sparse component analysis (SCA), to resolve the UBSS in two stages: estimate the mixing matrix first and then recover the sources. In the first solution, the most commonly used decomposition methods are EMD, local mean decomposition (LMD), and variational mode decomposition (VMD), which have excellent performance when dealing with nonlinear signals [14]. The multi−channel components obtained by these decomposition methods are regarded as the virtual sensor outputs, hence the accuracy of the signal decomposition is crucial for source signal recovery.…”

A review on fault-tolerant control strategies for lithium-ion battery systems

“…The task of an FDD system is to supervise the system’s performance and provide achievable information about the abnormal operation of its components. Subsequently, the general role of fault diagnosis involves three subtasks, as shown in Figure 8 (Witczak, 2014; Zhong et al, 2021).…”

Dual-weight local linear embedding algorithm based on adaptive neighborhood

“…Usually, the vibration signal of the bearing contains a great deal of redundant information that may reduce the recognition accuracy and increase the computational complexity (Ji et al, 2018; Shi et al, 2021; Zhang et al, 2017; Zhong et al, 2021). Therefore, it is essential to reduce the dimensionality of the collected bearing data before data analysis.…”