Data-driven multimodal operation monitoring and fault diagnosis of high-speed train bearings

Abstract

The multiple bearings of high-speed trains operate in a similar condition that correlates the bearings temperatures dynamically. This allows for detecting bearing abnormalities and diagnosing the faults according to unexpected changes in the dynamic relations. Traditional single-modal modeling methods do not consider the different dynamic characteristics while the train is in different operating zones. This leads to difficulty with modeling the relationship using a single model, resulting in false alarms. To solve the above problems, the bearing temperature data collected by the bearing temperature monitoring system, the location information of the historical bearing fault alarms collected by the train control system, and the maintenance data are incorporated to identify the operation modes. In this paper, a novel data-driven multimodal bearing operation monitoring and fault diagnosis method is proposed for high-speed trains. First, to manage the outliers and missing values caused by train network communication disconnection and sensor faults, a preprocessing method, which combines linear-filling and a dynamic principal component pursuit, is proposed to reconstruct the contaminated data of the bearing temperatures. Second, the train location information-based operation mode identification method is proposed according to the fact that the clustering results of multiple bearing temperature correlations match well with the operating zone, extracted from the location information of historical faults. Third, a multimodal dynamic inner canonical correlation analysis (M-DiCCA) modeling method and the corresponding operation monitoring method for the train bearings are proposed. Subsequently, a dynamic time warping (DTW)-based fault diagnosis method is proposed. Finally, the application results by using the bearing temperature data collected from practical train operations demonstrate the effectiveness of the proposed methods.