Reliability Assessment of Motors and Planning for Accelerated Life Tests under Constrained Sample Size
Date Issued
2015
Date
2015
Author(s)
Lai, Hung-Yi
Abstract
To understand the life distribution and reliability of electrical motors in short time, accelerated life testing (ALT) has been applied widely in industry. In traditional accelerated tests, linear regression tends to be an appropriate method to analyze the relationship between the logarithm life and surrounding temperature of a motor, and the standard deviation of the logarithm life is regarded as a constant in accordance with linear regression and for the sake of convenience. However, due to requirements for decreasing life prediction errors of motor after ALT, one of the research objects of this thesis is to estimate the mean and standard deviation of life of a motor based on nonlinear regression. The result indicates that a quadratic regression model is the most appropriate one than others to estimate the mean of the logarithm life, while an exponential regression model performs optimally to fit the standard deviation of the logarithm life of a certain kind of motor. In order to conduct an accelerated life test accurately and efficiently with constrained resources in practice, sample allocation proportions under different temperatures should be determined carefully at the design stage. These decision variables primarily affect the accuracy of life prediction of motors. To improve the accuracy, this thesis proposes and illustrates a method using real test data of motor to optimize an objective function under a few constrained resources based on Fisher information matrix. The result indicates that there is no need to assign test samples at temperature levels other than the lowest and highest ones. In fact, experimenters should allocate more samples at the highest temperature level.
Subjects
reliability
accelerated life testing
nonlinear regression
nonconstant standard deviation
sample allocation
Type
thesis
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