Independent component analysis as applied to vibration source separation and fault diagnosis

Mahvash, Ali; Lakis, A. A.

doi:10.1177/1077546314544349

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…For the diagnosis of a mechanical compound fault, it is very meaningful to effectively separate multiple unknown vibration source signals from a mixed signal, which is called blind source separation (BSS). To address this issue, blind source separation methods, e.g., independent component analysis (ICA), have been broadly applied to machinery fault diagnosis [19][20][21][22]. BSS usually needs to collect multi-channel signals.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm

Yuan

Chen

2021

Machines

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For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular value decomposition (shift invariant K-SVD) dictionary learning is suitable to extract the periodic and repeated fault features of a rotating machinery fault, hence in this article a single-channel compound fault analysis method is put forward which combines shift invariant K-SVD with improved fast independent component analysis (improved FastICA) algorithm. Firstly, based on single-channel compound fault signal, the shift invariant K-SVD algorithm can be used for learning multiple latent components that can be constructed as a virtual multi-channel signal. Then the improved FastICA algorithm is utilized to realize the separation of multiple fault source signals. With regard to the FastICA algorithm, the third-order convergence Newton iteration method is adopted to improve convergence speed. Moreover, in order to address the problem that FastICA is very sensitive to initialization, a steepest descent method can be applied. The experimental analysis of the compound fault of rolling bearing verifies that the presented method is effective to separate multiple fault source signals and the improved FastICA algorithm can increase convergence rate and overcome the problem of sensitivity to initialization.

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Section: Introductionmentioning

confidence: 99%

Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm

Yuan

Chen

2021

Machines

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“…Either its resulting ICs or mixing matrix can be directly used as damage features or further analyzed for extracting advanced damage-sensitive features. However, traditional ICA algorithms do not guarantee identical output ICs due to the issue of local minimum, 10,[21][22][23] which means that the ICs and mixing matrices are randomly permuted or shifted for each run. Therefore, it becomes challenging to extract damage-sensitive feature that captures the most valuable information using either the ICs or the mixing matrix.…”

Section: Introductionmentioning

confidence: 99%

A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

Zhang

Sun

2020

Struct Control Health Monit

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Summary This paper presents a solution to the multi‐site structural damage identification problem using a data‐driven method and constrained independent component analysis (cICA). While existing studies in this field presented encouraging results for single‐site damage identification, limited research effort has been devoted to identifying multi‐site damage due its complexity. Efficient features for single‐site damage identification may lose their effectiveness when multi‐site damage occurs. This paper extracts damage‐sensitive features from the ICA outcome of the structural responses under certain excitations. The information on structural damage contained in the response is compacted into the mixing matrix by enforcing identical independent components to that of intact structures. Hence, the cICA can significantly reduce the feature dimension and preserve all the valuable information of damage. A case study indicates that the mixing matrix elements, when used as damage features, can distinguish multi‐site damage cases from single‐site damage cases and locate the single‐site damage. Furthermore, the mixing matrix columns of multi‐site damage cases exhibit distinct correlation with that of the corresponding single‐site damage cases. As a result, the proposed method can progressively locate the structural damage. Moreover, the present study has the potential to identify multi‐site damage identification in data‐driven structural health monitoring without requiring multi‐site damage data as a reference. This relieves the burden from data incompleteness when using data‐based damage identification methods and pattern recognition.

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“…Diverse applications of BSS can be found in the literature, e.g. in biomedical signal processing (Vigario et al, 2000) and vibration-based applications (Haile and Dykas, 2016; Mahvash and Lakis, 2016).…”

Section: Introductionmentioning

confidence: 99%

A frequency domain blind identification method for operational modal analysis using a limited number of sensors

Antoni

Brennan

et al. 2020

Journal of Vibration and Control

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Operational modal analysis is an experimental modal analysis approach, which uses vibration data collected when the structure is under operating conditions. Amongst the methods for operational modal analysis, blind source separation–based methods have been shown to be efficient and powerful. The existing blind source separation modal identification methods, however, require the number of sensors to be at least equal to the number of modes in the frequency range of interest to avoid spatial aliasing. In this article, a frequency domain algorithm that overcomes this problem is proposed, which is based on the joint diagonalization of a set of weighted covariance matrices. In the proposed approach, the frequency range of interest is partitioned into several frequency ranges in which the number of active modes in each band is less than the number of sensors. Numerical simulations and an experimental example demonstrate the efficacy of the method.

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Independent component analysis as applied to vibration source separation and fault diagnosis

Cited by 9 publications

References 19 publications

Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm

Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm

A numerical study on multi‐site damage identification: A data‐driven method via constrained independent component analysis

A frequency domain blind identification method for operational modal analysis using a limited number of sensors

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