Computational methods for reliability data analysis

Abstract

Many practitioners of component and system reliability are not aware that powerful statistical tools for the analysis of reliability data have been made practical by the availability of inexpensive desk top computers. Software and computational power are available to apply computationally intensive statistical and graphical techniques to reliability data analysis problems. This benefits the industrial statistician or reliability engineer by allowing the use of versatile and accurate methods that apply to many different types of data that are encountered in reliability data analysis. In this paper we apply some of the most useful statistical and graphical techniques to examples of life data, accelerated test data, and repairable system data using new software available in the SAS system. The trend of applying computationally intensive techniques to reliability data analysis will undoubtably continue as more workers recognize the need for creative software to address problems in reliability data analysis.