Intelligent Fault Diagnosis for Industrial Systems: Theoretical Research and Engineering Practices

2025-04-22

We are thrilled to announce our forthcoming Thematic Collection, in collaboration with renowned scholars:

Assoc. Prof. Xiaoxuan Jiao
Assoc. Prof. Yuchen Song
Assoc. Prof. Andrea Cioncolini
Assoc. Prof. Damiano Padovani

This collection focuses on the intriguing area of intelligent fault diagnosis for complex industrial systems — integrating both theoretical frameworks and engineering applications.

Fault diagnosis and prognosis are crucial for ensuring the safety and efficiency of industrial systems. However, the complex interdependencies among monitoring data and dynamic operating conditions present ongoing challenges in early fault detection. The introduction of artificial intelligence (AI) into this domain has emerged as a promising solution. AI enhances practical applications by improving accuracy, responsiveness, and adaptability.

This thematic issue aims to gather the latest insights, with emphasis on the following key research areas:

Fault Pattern Analysis – explores anomaly and fault propagation mechanisms, including their modeling in multi-modal monitoring data.
Monitoring Data Modeling – covers dynamic correlation models for high-dimensional data and self-adaptive temporal models for evolving single-dimensional data streams.
Fault Diagnosis and Prognosis – utilizes deep learning and machine learning approaches, especially physics-informed neural networks, generative models, and other advanced methods.
Model Validation and Verification – highlights benchmarks and platforms such as digital twin and multi-physical models for verifying diagnosis accuracy.
System Modeling and Estimation – focuses on early failure estimation, observer-based state estimation, and measurement-oriented mathematical modeling.

We aim to:

Highlight cutting-edge developments
Stimulate discussion around future directions
Promote global collaboration

We invite researchers to submit:

Original research articles
Review papers
Perspectives and viewpoints

We look forward to your invaluable contributions to this Thematic Collection!

Keywords:

Fault Diagnosis · State Monitoring · System Modeling · Digital Twin · Deep Learning
Prognostics and Health Management · Complex Systems · State Estimation · Experimental Validation

Assoc. Prof. Xiaoxuan Jiao
Air Force Engineering University
Assoc. Prof. Yuchen Song
Harbin Institute of Technology
Assoc. Prof. Andrea Cioncolini
Guangdong Technion-Israel Institute of Technology
Assoc. Prof. Damiano Padovani
Guangdong Technion-Israel Institute of Technology