Article
Fault Diagnosis of Brake Train Based on Multi-Sensor
Data Fusion
Yongze Jin, Guo Xie *, Yankai Li, Xiaohui Zhang, Ning Han, Anqi Shangguan and Wenbin Chen
Citation: Jin, Y.; Xie, G.; Li, Y.;
Zhang, X.; Han, N.; Shangguan, A.;
Chen, W. Fault Diagnosis of Brake
Train Based on Multi-Sensor Data
Fusion. Sensors 2021, 21, 4370.
https://doi.org/10.3390/s21134370
Academic Editors: Hamed Badihi,
Ningyun Lu and Tao Chen
Received: 12 May 2021
Accepted: 23 June 2021
Published: 25 June 2021
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4.0/).
Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing,
Xi’an University of Technology, Xi’an 710048, China; 1190313032@stu.xaut.edu.cn (Y.J.);
liyankai@xaut.edu.cn (Y.L.); xhzhang@xaut.edu.cn (X.Z.); 2200320062@stu.xaut.edu.cn (N.H.);
1200313023@stu.xaut.edu.cn (A.S.); 2190321310@stu.xaut.edu.cn (W.C.)
* Correspondence: guoxie@xaut.edu.cn
Abstract:
In this paper, a fault diagnosis method is proposed based on multi-sensor fusion informa-
tion for a single fault and composite fault of train braking systems. Firstly, the single mass model
of the train brake is established based on operating environment. Then, the pre-allocation and
linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the
improved expectation maximization, the braking modes and braking parameters are identified, and
the braking faults are diagnosed in real time. The simulation results show that the braking parameters
of systems can be effectively identified, and the braking faults can be diagnosed accurately based on
the identification results. Even if the monitoring data are missing or abnormal, compared with the
maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the
needs of the actual systems, and the effectiveness and robustness of the method can be verified.
Keywords:
high-speed train; information fusion; fault diagnosis; parameter identification; unscented
Kalman filter (UKF); expectation maximization (EM)
1. Introduction
With the increase of speed, the reliability and safety of the train system are put forward
with higher requirements. However, influenced by the potential technique abnormalities
and component failures, the train system still fails frequently [
1
–
3
]. While these failures
may not be serious in the early stages, the performance of the system has indeed been de-
graded [
4
,
5
]. Therefore, the early detections and identifications of any potential anomalies
and failures are essential, as they avoid dangers for high-speed train operation [6,7].
The high-speed train is composed of multiple subsystems interworking with each
other. A stable and reliable braking system is indispensable, to ensure a safe and com-
fortable operation environment. It can slow down or stop smoothly and timely when
needed. Over past few decades, a large number of monitoring, diagnosis, and prediction
techniques have been applied to train systems [
8
]. To be more specific, the fault diagnosis
methods based on feature extraction, feature selection, and feature fusion have been studied
in [
9
–
13
], and the accuracies of fault diagnosis are improved greatly by these methods. The
intermittent fault detection, isolation, and diagnosis of train multi-axis speed sensors are
addressed in [
14
–
16
], and the composite fault diagnosis of rolling equipment such as train
bearings has been proposed in [
17
–
19
]. These above technologies have greatly improved
the level of intelligence in ensuring the safe and reliable operation of trains.
In recent years, with the development of sensors, train monitoring data have become
diverse. Therefore, fully mining the value of multi-source data and realizing the fault
diagnosis are of great significance based on multi-sensor information fusion [
20
,
21
]. In
multi-sensor fusion, centralized and distributed fusion methods are mainly used to process
the monitoring data. In centralized fusion, all measured data from multiple sensors are
stacked into a sensor measurement (with higher dimensions), and the specific fusion
Sensors 2021, 21, 4370. https://doi.org/10.3390/s21134370 https://www.mdpi.com/journal/sensors
