A Framework for Model-Based Diagnostics and Prognostics of Switched-Mode Power Supplies

Honglei Li, Xuerong Ye, Cen Chen, and George J. Vachtsevanos
Submission Type: 
Full Paper
AttachmentSizeTimestamp
phmc_14_071.pdf613.05 KBSeptember 19, 2014 - 10:03pm

With electrical power supplies playing an important role in the operation of aircraft systems and sub-systems, flight and ground crews need health state awareness and prediction tools that accurately diagnose faults, predict failures, and project remaining life of these onboard power supplies. Among onboard power supplies, switch-mode power supplies are commonly used where their weight, size, and efficiency make them preferable to conventional transformer-based power supplies. In this paper, we present a framework of diagnostics and prognostics methodology based on an equivalent circuit system simulation model developed from a commercially available switch-mode power supply, and empirical component degradation models. In industrial applications, case-specified modifications can be made according to specific experimental or service conditions of different commercial products. First, the developed simulation model is validated through experimental testing. Then, a series of data are collected from simulation to build the baseline and fault databases under a fixed load profile. Next, promising features are extracted from sensed parameters, and further data analysis are conducted to estimate the current health condition and to predict the remaining useful life of the target system. Some highlights of the work are included but not only limited to the following aspects: first, the methodology is based on electronic system simulation instead of traditional accelerated testing by employing a high-fidelity system simulation model and empirical critical component degradation models; second, efforts are made in this preliminary work to adapt proven prognostics and health management techniques from machinery to electronic health management, with the goal of expanding the realm of electronic diagnostics and prognostics.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
071
Page Count: 
8
Submission Keywords: 
electronics PHM
model-based diagnosis and prognosis
fault detection
fault diagnosis
failure prognosis
particle filter
Submission Topic Areas: 
PHM for electronics
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