Model-Based Fault Diagnosis and Fault-Tolerant Control: An Approach for Automotive Electric Machines
| AUTHOR | Salah, Alia |
| PUBLISHER | Springer Vieweg (11/09/2025) |
| PRODUCT TYPE | Paperback (Paperback) |
Alia Salah introduces a multi-functional, model-based method for fault detection and identification in automotive electric machines. This approach integrates current vehicle diagnostics to detect faults early, before component failure. It utilizes digital twins and parameter estimation, alongside machine learning classification, to identify fault type and location. Moreover, it incorporates model reference adaptive control for fault-tolerant control, helping to maintain performance and ensure a safe driving experience.
Alia Salah introduces a multi-functional, model-based method for fault detection and identification in automotive electric machines. This approach integrates current vehicle diagnostics to detect faults early, before component failure. It utilizes digital twins and parameter estimation, alongside machine learning classification, to identify fault type and location. Moreover, it incorporates model reference adaptive control for fault-tolerant control, helping to maintain performance and ensure a safe driving experience.
Content
- Study on the effects of common faults on the performance of automotive electric machines
- A Concept for fault detection and identification for automotive electric machines, for active fault-tolerant control based on model reference adaptive control and of simulation models to prove the effectiveness of the proposed concepts
Target Groups
- Researchers and students in the field of electro-mobility and electric drives
- Development engineers and specialists who focus on electro-mobility, vehicle diagnostics and control systems
About the Author
Alia Salah holds a doctoral degree in Automotive Mechatronics Engineering from University of Stuttgart, Germany. She is active in the electro-mobility field, automotive diagnostics and the development of control concepts for automotive electric machines. Her expertise extends to the development of anomaly detection concepts, predictive maintenance, and data science within the automotive domain. She has an extensive history of research publications in these fields.
Alia Salah introduces a multi-functional, model-based method for fault detection and identification in automotive electric machines. This approach integrates current vehicle diagnostics to detect faults early, before component failure. It utilizes digital twins and parameter estimation, alongside machine learning classification, to identify fault type and location. Moreover, it incorporates model reference adaptive control for fault-tolerant control, helping to maintain performance and ensure a safe driving experience.