Physics-Informed Auxiliary Losses for Learning Generalisable Diagnostic Models for Suspension Dampers

Reliability is an important objective in vehicle design and should be assured during vehicle use, as vehicle components degrade over their lifetime. Among other components, suspension dampers deteriorate, which can affect ride comfort and vehicle safety. Diagnosing the health state of suspension dampers in real-world scenarios is challenging because sensor signals are limited, and the measurable effects of degradation appear as complex signal patterns. To address these challenges, machine learning methods are increasingly used to develop diagnostic models. To reduce the need for high-quality and large-scale training data, this work proposes a physics-informed machine learning model for diagnosis. The model incorporates physics-based knowledge into a standard machine learning model through auxiliary losses. These losses are motivated by insights gained from a physics-based simulation model that describes the coupling between damper degradation and its effects on measurement signals. The diagnostic model and its performance have been successfully tested within a real vehicle, with and without degraded dampers. We show that the incorporation of auxiliary losses greatly improves the diagnostic performance and requires less training data for a comparable performance.