Work

Online diagnosis for bogie bearing unbalance impact faults of railway trains with wavelet phase space reconstruction mechanism

Year: 2026

Type: article

Abstract: The reliability of bearings directly affects the operational status of mechanical equipment, and there is a serious imbalance in its shock vibration characteristics. This paper constructed wavelet phase space in the offline diagnosis stage to locate the part of the fault signal that was concentrated in energy to reconstruct and generate pseudo-fault samples, and guides the category balancing weights in the two-phase feature learning network to reduce the loss contribution of the easy-to-classify samples, while the adaptive threshold is adjusted to balance the local noise with the global statistics, so that the model focuses on the difficult samples more. Finally, small batch incremental learning was applied to online diagnosis to improve the efficiency of industrial diagnosis. Thirty-six sets of experiments under multiple unbalance cases show that the reconstructed signals are of high quality, and the G-mean value can reach 95.04%, which provides a new idea for the online diagnosis.

Source: Ain Shams Engineering Journal

DOI: 10.1016/j.asej.2025.103970

Authors: Zheng Liu, Kun Xu, Xiaodong Miao, Qinjing He, Yubin Pan, Hu Yu

Institutions: Nanjing Tech University, Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,200