A Bayesian-Based Fault Diagnosis Method for Reliability Improvement of Electric System
Date Issued
2015
Date
2015
Author(s)
Chang, Yu-Chen
Abstract
The reliability of electrical systems on modern vehicles has an increasing impact on the on-road safety with then increase of smart technology implementations. These electrical systems help detecting dangerous driver behaviors, alleviate driving errors, prevent unintended actions, as well as provide alternatives to internal combustion engines. The goals of having more efficient and safer vehicles could be undermined by the low reliability of electrical systems at severe driving environment. Existing reliability assessment methods, such as fault tree analysis and failure mode and effect analysis, focus on the cause and effects of component failure on system faults. On the other hands, Markov-chain based methods and reliability allocation techniques quantifies how these failure modes propagates within a complex system using reliability measure. Albeit abundant research activities, diagnosing the true origin of a system fault among all possible causes can be challenging. Incorporating these results for reliability improvements requires measurements that are costly in product development. Therefore in this research we develop a method to identifying the most likely origin of a electrical system fault with incremental data using Bayesian concept. We incorporate physics of failure in the welding joints of each components and consider the performance variations within each components. The result will be a subjective probability measure to rank the relative likely cause of a fault under varying environmental conditions. Designers can then use this result to sequentially identifying or measuring the health of each components until a conclusion is made. To deal with reliability with limited measurement samples, a reliability evaluating and updating scheme via Bayesian inference is established. We demonstrate the validity of the proposed method via a boost converter and an inverter. Consider the product development stage results in a electric layout that is to be realized. We show that due to the ambient temperature gradient and the rise of component temperature in operation, the reliability of a circuit rely on its configuration. We also use the examples to show the effect of the sample size on our probability measure. Combined with FMEA, the proposed method can help re-examine the electrical system in the earliest design stage toward high reliability target. For modern vehicles with a large number of complex electrical systems, our method can help improving the final reliability in real operation.
Subjects
Reliability
Vehicle electrical system
Physics of failure
Measurement data
Bayesian inference
Type
thesis
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