A Semi-Quantitative Information Based Fault Diagnosis Method for the Running Gears System of High-Speed Trains

Abstract

The proper operation of running gears of a high-speed train is one of the key factors to ensure its safety and reliability. The diagnosis of the state of running gears of a high-speed train is one of the effective ways to improve its reliability. It is difficult to diagnose the running gears of a high-speed train accurately because of the characteristics of its complex-analytic structure, multiple types of monitoring feature data, and lack of effective failure mode data. Therefore, this paper proposes a fault diagnosis method for the running gears of a high-speed train based on a semi-quantitative information model. The relation between the effective data and expert knowledge is studied, and the state of the running gears of a high-speed train is rigorously analyzed. To reduce the data dimension and the diagnostic calculation time of the running gears of a high-speed train, the principal component analysis (PCA) is used to screen its key monitoring features. Then, based on the change of the feature quantity in the working process of the running gears of a highspeed train, the semi-quantitative information model of belief-rule-base (BRB) fault diagnosis is established. In the diagnosis process, the initial model parameters of BRB are determined by expert knowledge and they have certain subjectivity. To improve the accuracy of the model, the constrained covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm is used to optimize the parameters of the initial BRB model to improve the validity and accuracy of the diagnosis. Finally, to verify the effectiveness of the proposed semiquantitative information model, a set of real data of the running gears of a high-speed train is used as case studies.INDEX TERMS Semi-quantitative information, fault diagnosis, principle component analysis, belief-rulebase, constraint covariance matrix adaptive evolution strategy.

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

“…Currently, BRB methods have many studies on FD tasks of high-speed trains. To reduce the complexity of BRB, Reference [ 24 ] develops a BRB approach, by combining with principal component analysis technology (PCA), to detect faults in running gear systems of high-speed trains. On this basis, Reference [ 21 ] designs a BRB with mixed reliability (BRB-mr) scheme, by quantifying two types of uncertainty factors in the process of sensor measurements, to diagnose faults in running gear systems.…”

“…The occurrence of faults in high-speed trains is often random. Moreover, there are still several uncertainties under the following situations, such as monitoring process of measurement data, transformation in external operation environment, and so on [ 24 ].…”

A Novel Fault Detection Method for Running Gear Systems Based on Dynamic Inner Slow Feature Analysis

“…There are many types for faults in the running gear, which are mainly divided into bearing faults and gear faults [2]- [4]. Metal particles and sand particles are squeezed with the gear, the dynamic interaction between the wheelset and the axle box bearing, the oil film ruptures when the meshing tooth surface is relatively sliding, the unit area load on the tooth surface is too large, and the data collection is performed under the condition of interference, these will increase the probability of fault [5].…”

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

“…However, when the VMD method decomposes the signal, the decomposition effect is seriously affected by the number of decomposition components [56][57][58][59][60]. Some other methods have been proposed to realize signal analysis and fault diagnosis in recent years [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80].…”