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 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.