From physical models to data intelligence: evolution of rail vehicle suspension fault diagnosis

Abstract

Timely and accurate fault diagnosis of suspension systems is paramount for ensuring the operational safety of rail vehicles. Recent years have witnessed extensive research in this field, primarily categorized into model-based and data-driven methods based on their underlying knowledge sources. Model-based methods rely on precise mathematical models to achieve fault detection and isolation via state or parameter estimation. Data-driven methods leverage historical and real-time data to extract fault features using statistical analysis, traditional machine learning, or deep learning techniques. This article presents a systematic review of these methods, offering a detailed comparison of their advantages and disadvantages, while summarizing current development trends and existing challenges. Finally, several future research directions are proposed, including model-data fusion diagnostic strategies, enhancement of real-time performance and robustness, enhancement of reliability prediction and uncertainty quantification, translation into engineering applications, and intelligent diagnostic techniques under small-sample conditions, aiming to provide references for the further development of suspension system fault diagnosis technology.