Abstract
For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is to review the current development of these qualitative IFD techniques and then to present some of the latest results. Another major focus of our research is to introduce the background of high-speed trains, like the composition of the core subsystems, system structure, etc., based on which it becomes convenient for researchers to extract the diagnostic knowledge of high-speed trains, where the purpose is to understand how to use these types of knowledge. By reasonable utilization of the knowledge, it is hopeful to address various challenges caused by the coupling among subsystems of high-speed trains. Furthermore, future research trends for qualitative IFD approaches are also presented.
Highlights
Since the Japanese Shinkansen was born in 1964, high-speed trains had made rapid progress all over the world [1,2,3]
This paper has discussed the development of qualitative intelligent FD (IFD) techniques for highspeed trains and reviewed the basic ideas and several important schemes of qualitative IFD techniques, as well as introduced some recent results in the IFD field of high-speed trains
One major focus has been on the background overview of high-speed trains, including the composition of the core subsystems, system structure, and so on, which are helpful to understand and extract fault knowledge, as well as construct a desired diagnostic knowledge base (KB)
Summary
Since the Japanese Shinkansen was born in 1964, high-speed trains had made rapid progress all over the world [1,2,3]. As one of the key steps to build a diagnostic KB, fault knowledge extraction should be carried out from two aspects, i.e., known quantitative and qualitative information in high-speed trains, as shown in Figure 5: (a) known qualitative information mainly refers to maintenance manuals, fault records, characteristic parameter manuals, electrical schematic diagrams, system structure diagrams, etc. They are derived from the historical maintenance records and mechanisms of high-speed trains. The typical mining methods used for extracting knowledge are mainly divided into the following categories: Known quantitative information
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