2018
DOI: 10.36001/phmconf.2018.v10i1.546
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Rail Suspension System Fault Detection using Deep Semi-Supervised Feature Extraction with One-class data

Abstract: In this paper we propose a novel semi-supervised fault detection methodology for a vehicle suspension system with one-class multi-sensor data. Supervised data-driven methods have been applied in fault detection successfully in recent studies. However, it is difficult and expensive to collect data under faulty condition for supervised learning while data collection under normal condition is much easier and cheaper. Fault detection under such situation is a one-class classification problem that requires classifi… Show more

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Cited by 4 publications
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“…-Peng et al [41] proposed a deep semi-supervised feature extraction method for fault detection in rail suspension systems. -This approach leveraged data from multiple sensors and was particularly effective when only one data class was available.…”
mentioning
confidence: 99%
“…-Peng et al [41] proposed a deep semi-supervised feature extraction method for fault detection in rail suspension systems. -This approach leveraged data from multiple sensors and was particularly effective when only one data class was available.…”
mentioning
confidence: 99%