Source Contribution Evaluation of Mechanical Vibration Signals via Enhanced Independent Component Analysis

Abstract

Extraction of effective information from measured vibration signals is a fundamental task for the machinery condition monitoring and fault diagnosis. As a typicai blind source separation (BSS) method, independent component analysis (ICA) is known to be abie to effectively extract the latent information in complex signals even when the mixing mode and sources are unknown. In this paper, we propose a novel approach to overcome two major drawbacks of the traditional ICA algorithm: lack of robustness and source contribution evaluation. The enhanced ICA algorithm is established to escalate the separation performance and robustness of ICA algorithm. This algorithm repeatedly separates the mixed signals multiple times with different initial parameters and evaluates the optimal separated components by the clustering evaluation method. Furthermore, the source contributions to the mixed signals can also be evaluated. The effectiveness of the proposed method is validated through the numerical simulation and experiment studies.

A source contribution quantitative calculation method for mechanical systems based on the simplified independent component analysis with reference

“…The ICs are shown in Figure 16, which are not in consistent order with the source signals. The source contribution is calculated by the elements of mixing matrix, 45 and the contribution is given in Table 2.

Figure 16.The waveforms and Fourier spectrum of the ICs separated by the fixed-point algorithm.

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A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

“…Independently acquiring information from each source of the mechanical system can help to quickly judge its running state. However, in practice, the information measured by sensors is the superposition of some sources, because different components of the mechanical system will interfere with each other, which makes it difficult to directly measure the source information [3]. Therefore, some supplementary signal processing methods are needed to further process the collected information to obtain the expected source signals [1].…”

Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems

“…The common signal decomposition methods (Zhang et al., 2016), such as wavelet transform, empirical mode decomposition, and variational mode decomposition, may fail when different sources contain similar or cross-frequencies. Blind source separation (BSS) provides a new way of source recovery, which can recover sources from their mixtures with little prior knowledge about sources and the transmission channel characteristics, and has been widely used in many practical applications (Cheng et al., 2012, 2014a, 2014b; Becker et al., 2017; Sadhu et al., 2017). However, most of the current methods are only effective for a linear system.…”