Thermal Model Based Fault Detection and Isolation of Power Inverter IGBT Module

Author(s):  
Madi Zholbaryssov ◽  
Azeem Sarwar

Penetration of electrified vehicles has increased steadily over the last decade due to unstable fuel prices, and the ability of such vehicle to offer lower cost per mile for transportation. At the same time, strict fuel emission standards continue to motivate the auto industry to invest resources on developing new technologies, which allow economically feasible electrification of vehicles and enable mass production. In electric vehicles, the electric drive system converts electrical energy into mechanical energy that powers the vehicle wheels. In this article, we present thermal model based fault detection and isolation methodology for power inverter insulated gate bipolar transistor (IGBT) modules, which play a key role in converting DC power from the battery into AC power that goes into the electric motor and drives the wheels through the transmission module. We do not propose any additional sensing capability, and make use of what is typically available in most of the production vehicles today across the industry. Results are presented from simulation studies that highlight the effectiveness of our proposed method.




Author(s):  
L. Tesar ◽  
L. Berec ◽  
G. Dolanc ◽  
G. Szederkenyi ◽  
J. Kadlec ◽  
...  


Aerospace ◽  
2019 ◽  
Vol 6 (9) ◽  
pp. 94 ◽  
Author(s):  
Matteo D. L. Dalla Vedova ◽  
Alfio Germanà ◽  
Pier Carlo Berri ◽  
Paolo Maggiore

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.





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