Adaptive Clustering for Distribution Parameter Estimation in Technical Diagnostics

Abstract

A novel approach has been introduced to estimate the parameters of exponential and DN distributions during the rejection testing of electronic devices, accompanied by a detailed procedure for its implementation. This innovative method enhances noise immunity and minimizes the error associated with the rejection process through the application of a clustering technique involving wavelet transform. The effectiveness of the method has been verified using resistors, employing criteria such as noise level and stability. The substantial improvement in noise immunity and the reduction in rejection procedure errors are achieved by incorporating an adaptive clustering method coupled with wavelet transform. Notably, in clustering with a signal-to-noise ratio by amplitude of 1.17, the relative error in determining the minimum of the test function was reduced to 8.32%. These promising outcomes substantiate the recommendation of the developed method for the automated selection of resistors, particularly those designated for long-term operational equipment with critical applications. The presented method thus contributes significantly to enhancing the reliability and accuracy of electronic device testing and selection processes.

Keywords

• astd
• automated systems for technical diagnostics
• electronic components
• reliability parameters
• adaptive clustering
• wavelet transform
• noise immunity
• rejection systems
• exponential distribution
• dn distribution
• small data
• noisy data
• complex ecs
• accelerated tests
• degradation processes