Szczegóły publikacji
Opis bibliograficzny
Conceptual framework for predictive maintenance in urban mass transport systems based on signal processing and generative artificial intelligence: from fault prognostics to maintenance decision modelling / Wasihun KENO, Janusz SZPYTKO // W: ESREL 2026 [Dokument elektroniczny] : proceedings of the European Safety and Reliability Conference : 14–19 June 2026, Braga, Portugal / ed. by José C. Matos, [et al.]. — Wersja do Windows. — Dane tekstowe. — Singapore : Research Publishing, cop. 2026. — Dod. ISBN: 978-981-94-6718-1. — e-ISBN: 978-981-94-3281-3. — S. 624–630. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://cmsweb.com.sg/rps2prod/esrel2026/epro/pdf/esrel26-p27... [2026-07-17]. — Bibliogr. s. 630, Abstr.
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Dane bibliometryczne
| ID BaDAP | 169131 |
|---|---|
| Data dodania do BaDAP | 2026-09-03 |
| DOI | 10.3850/ESREL2026061419_esrel26-p27884-cd |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
Abstract
Urban mass transport systems require high availability, controlled life-cycle cost, and strict safety performance. This study develops and validates a predictive maintenance framework that links signal processing, deep generative modelling, and reliability-based decision analysis. Multi-sensor data from a tram traction system, including vibration sampled at 10 kHz and thermal and current signals sampled at 1 Hz , were processed using short-time Fourier transform and wavelet packet decomposition. Statistical and spectral features were extracted from segmented operating windows. A variational autoencoder and a generative adversarial network were trained on healthy-state data to model latent degradation behaviour. Reconstruction error and latent deviation were used to derive degradation indicators, which were embedded into a proportional hazards model for remaining useful life estimation. The approach was evaluated on five years of operational data from a fleet of 40 trams. Detection accuracy increased from 0.87 with a support vector machine baseline to 0.94 with the generative adversarial network. Mean absolute error of remaining useful life prediction decreased from 18 days to 9 days. Monte Carlo propagation of latent uncertainty produced 90 percent calibrated prediction intervals. Fleet-level simulation over one operational year showed reductions in corrective interventions and expected failure cost when compared with time-based maintenance. The results demonstrate that generative prognostic outputs can be formally linked to reliability functions and risk-based maintenance planning. The framework additionally integrates prognostic outputs into reliability-centered maintenance and risk-based scheduling models, clarifying the role of generative models in maintenance engineering and outlining future research directions for ESREL 2026.
