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Fault-related monitoring variables selection is a process of obtaining a subset of variables from the original set, which is of great significance for reducing information redundancy and improving the performance of the fault diagnosis models. This paper aims to propose a novel variables selection approach based on complex networks.

Firstly, a dual-layer correlation networks (DlCN) which consists of mechanism-oriented correlation sub-network (MoCSN) and data-oriented correlation sub-network (DoCSN) is constructed. Secondly, an algorithm for identifying critical fault-related monitoring variables based on dual correlations is introduced. In the algorithm, the topological attributes of the MoCSN and correlation threshold of the DoCSN are used successively.

In the experiments of vertical elevator fault diagnosis, the critical fault-related monitoring variables selected by the DlCN-based approach is more effective than the traditional approaches. It indicates that fusion mechanism-oriented correlation can enhance the comprehensiveness of variable correlation analysis. Moreover, the approach has been proved to be adaptable to different fault diagnosis models.

In the DlCN-based variables selection approach, the mechanism-oriented correlation and data-oriented correlation are comprehensively considered. It improves the precision of variables selection. Meanwhile, it is an unsupervised and model-agnostic approach which addresses the shortcomings of some conventional approaches that require data labels and have insufficient adaptability for fault diagnosis models.

The purpose of this study is to systematically investigate the novel phenomenon of rail corrugation on small radius curves with rail joints in mountainous city metros, characterized by the coexistence of short and long wavelengths (30–40 mm and 150–200 mm) on the low rail.

The finite element model of the wheel-rail system in the section with rail joint is constructed based on field surveys. The friction-coupled vibration characteristics of the wheel-rail system are studied from the perspective of friction self-excited vibration of the wheel-rail system and feedback vibration of the rail irregularity.

The rail corrugation with short wavelength is primarily induced by the friction self-excited vibration of wheel-rail system. In contrast, the rail corrugation with long wavelength is predominantly caused by the feedback vibration of rail joint irregularity. Additionally, the feedback vibration of corrugated irregularity accelerates the progression of corrugation depth without triggering the emergence of rail corrugation with new wavelength.

The research advances the understanding of the vibration inducement behind rail corrugation in mountainous city metros.