Fault detection and isolation based on bond graph modeling and empirical residual evaluation

Fault detection and isolation are critical for safety related complex systems like aircraft, trains, automobiles, power plants and chemical plants. In order to realize a robust and real time monitoring and diagnosis for these types of multi-energy domain systems, this paper presents a novel scheme that integrates bond graph modeling for fault signatures establishment, and a multivariate state estimation technique-based empirical estimation for residual generation followed by a Sequential Probability Ratio Test-based residual evaluation for monitoring alarm. Once a fault is detected and alerted, a synthesized non-null coherence vector is created, and then matched with the pre-designed fault signatures matrix to isolate possible faults. To identify the effectiveness of the proposed methodology, a simulation for pneumatic equalizer control unit of locomotive electronically controlled pneumatic brake is conducted. The experimental results show that satisfied performance of fault detection and isolation can be obtained with lower miss alarm and timely response, which make it suitable for complex systems modeling and intelligent maintenance.