Multi-Condition Fault Diagnosis of Dynamic Systems: A Survey, Insights, and Prospects

TL;DR

This survey reviews the challenges, methods, and future prospects of multi-condition fault diagnosis in dynamic systems, emphasizing the importance of addressing complex operational variations for reliable industrial system maintenance.

Contribution

It provides a comprehensive classification and analysis of existing multi-condition fault diagnosis methods, highlighting key challenges and future research directions.

Findings

Categorization of single-model and multi-model approaches

Summary of standard evaluation metrics and real-world applications

Discussion of key challenges and future prospects

Abstract

With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently arise during production. The multi-condition characteristics pose significant challenges to traditional fault diagnosis methods. In this context, multi-condition fault diagnosis has gradually become a key area of research, attracting extensive attention from both academia and industry. This paper aims to provide a systematic and comprehensive review of existing research in the field. Firstly, the mathematical definition of the problem is presented, followed by an overview of the current research status. Subsequently, the existing literature is reviewed and categorized from the perspectives of single-model and multi-model approaches. In addition, standard evaluation metrics and typical real-world application scenarios are summarized and analyzed. Finally, the key challenges and prospects in the field are thoroughly discussed.