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 classification models to identify the positive class when the negative class is either absent or poorly sampled. The efficiency of classifiers is constrained by defining the normal class boundary only with the knowledge of positive class as well as the problem of biased or incorrect feature extracted from the positive class. In proposed method, A deep semi-supervised learning method integrated with physical-based domain knowledge is applied for feature extraction. The suspension system for a full car model is modeled using a simulation tool, SIMPACK to generate the synthetic multi-sensor data. Our results show the effectiveness of the proposed in fault detection and diagnostics with one-class data.