Abdenour Soualhi
Papers
2
Total Citations
7
H-Index
1
About
Abdenour Soualhi is a leading researcher in prognostics and health management (PHM), specializing in the condition monitoring and fault diagnosis of rotating machinery and industrial systems. His work focuses on developing data-driven approaches to detect, classify, and predict failures in critical components—such as bearings, gears, and cutting tools—across diverse applications including railways, renewable energy, and smart manufacturing. Soualhi’s major contributions include the creation of open, heterogeneous datasets for multi-fault diagnosis under varying operating conditions, enabling more robust and generalizable machine learning models for predictive maintenance. His most cited paper (2023, 6 citations) introduces a benchmark dataset that addresses the challenge of detecting multiple faults in rotating machines, a key step toward real-world PHM deployment. More recently, his 2025 study on robotic cutting tools advances fault diagnosis in smart manufacturing environments. By bridging experimental data with intelligent algorithms, Soualhi’s work directly impacts industrial reliability, reducing downtime and maintenance costs. His research is essential reading for engineers and data scientists working on AI-driven maintenance solutions.
Research Focus
Key Achievements
Top Papers
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