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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Ghiasi, R (Autor) Outra; Gordan, M (Autor) Outra; Mosleh, A (Autor) FEUP; Ribeiro, D (Autor) Outra; Malekjafarian, A (Autor) Outra

Revista

Título: Vehicle System Dynamics

Páginas: 1-26

ISSN: 0042-3114

Editora: Taylor & Francis

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 26