Application of Independent Component Analysis in Temperature Data Analysis for Gearbox Fault Detection

Wodecki, Jacek; Stefaniak, Paweł; Sawicki, M.

doi:10.1007/978-3-319-51445-1_11

Cited by 3 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%