Fault diagnosis of locomotive electro-pneumatic brake through uncertain bond graph modeling and robust online monitoring

• Integration of data-driven online monitoring and model-based fault diagnosis.
• Linear fractional transformations bond graph modeling for parameter uncertainties.
• Auto-associative kernel regression residual estimation for measurement uncertainties.
• A simulation experiment validation on a pneumatic equalizer control device.
• The developed scheme of online anomaly detection and diagnosis is robust.

To improve reliability, safety and efficiency, advanced methods of fault detection and diagnosis become increasingly important for many technical fields, especially for safety related complex systems like aircraft, trains, automobiles, power plants and chemical plants. This paper presents a robust fault detection and diagnostic scheme for a multi-energy domain system that integrates a model-based strategy for system fault modeling and a data-driven approach for online anomaly monitoring. The developed scheme uses LFT (linear fractional transformations)-based bond graph for physical parameter uncertainty modeling and fault simulation, and employs AAKR (auto-associative kernel regression)-based empirical estimation followed by SPRT (sequential probability ratio test)-based threshold monitoring to improve the accuracy of fault detection. Moreover, pre- and post-denoising processes are applied to eliminate the cumulative influence of parameter uncertainty and measurement uncertainty. The scheme is demonstrated on the main unit of a locomotive electro-pneumatic brake in a simulated experiment. The results show robust fault detection and diagnostic performance.