| Autor: |
Nikolaos Kaliorakis, John S. Sakellariou, Spilios D. Fassois |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Machines, Vol 11, Iss 10, p 933 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2075-1702 |
| DOI: |
10.3390/machines11100933 |
| Popis: |
The problem of the prompt detection of early-stage hollow worn wheels in railway vehicles via on-board random vibration measurements under normal operation and varying speeds is investigated. This is achieved based on two unsupervised statistical time series (STS) methods which are founded on a multiple-model (MM) framework for the representation of healthy vehicle dynamics. The unsupervised MM power spectral density (U-MM-PSD) method employs Welch-based PSD estimates for wheel wear detection and the unsupervised MM autoregressive (U-MM-AR) method for the parameter vectors of multiple AR models. Both methods are assessed via two case studies using thousands of test cases. The first case study includes Monte Carlo simulations using a SIMPACK-based detailed railway vehicle model, while the second is based on field tests with an Athens Metro train. Wheel wear detection is pursued using lateral or vertical vibration signals from the bogie or the carbody of a trailed vehicle traveling with three different speeds (60, 70, 80 km/h) using wheels under healthy conditions or with early stage hollow wear. Both methods exhibit remarkable performance with the U-MM-AR method to achieve the best overall results, reaching correct detection rates of even 100% with false alarm rates below 5% based on a single accelerometer either on the carbody or bogie. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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