M-CLUSTER: multistage clustering for unsupervised train wheel condition monitoring

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Ghiasi, R (Author) Other; Gordan, M (Author) Other; Mosleh, A (Author) FEUP; Ribeiro, D (Author) Other; Malekjafarian, A (Author) Other

Journal

Title: Vehicle System Dynamics

Pages: 1-26

ISSN: 0042-3114

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-017-FYJ

DOI: 10.1080/00423114.2024.2425415

Abstract (EN): Wheel out-of-roundness (OOR) is a common wheel defect that raises maintenance costs and increases the risk of failure or damage to track components. This paper proposes a novel multistage clustering framework (M-CLUSTER) for unsupervised condition monitoring of train wheels. The framework initially extracts time-domain features from raw acceleration responses collected by rail-mounted sensors. Sensitive features are then selected through an unsupervised feature selection algorithm called local learning-based clustering (LLC). Next, a detector model is trained using density-based spatial clustering of applications with noise (DBSCAN), a data clustering method effective for clusters with similar density. Since this algorithm does not originally involve separate training and testing phases, a new two-step mechanism is introduced: (1) training on a healthy dataset and (2) testing on an unlabelled dataset. Finally, the severity of train wheel defects is classified by K-means, with cluster validity indices (CVI) automatically determining the number of severity clusters (classes). The framework's efficiency is demonstrated through the detection of defective wheels using the Alfa Pendular passenger model. Results indicate that M-CLUSTER accurately identifies train wheel flats and polygonal wear without labelled data, achieving 98% accuracy by selecting 10 features from the set.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 26