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

arXiv: https://arxiv.org/abs/2412.19497 · HTML: https://mlsanity.com/paper/2412.19497/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis](https://mlsanity.com/paper/2506.17740/) (2025)
- [Awesome-OL: An Extensible Toolkit for Online Learning](https://mlsanity.com/paper/2507.20144/) (2025)
- [Asymmetric Adaptation-based Real-time Fault Diagnosis Under Transitional Operating Conditions](https://mlsanity.com/paper/2605.24457/) (2026)

## Abstract (arXiv)

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.