Reliability and robustness assessment of diagnostic systems from warranty data

Diagnostic systems are software-intensive built-in-test systems, which detect, isolate and indicate the failures of prime systems. The use of diagnostic systems reduces the losses due to the failures of prime systems and facilitates the subsequent correct repairs. Therefore, they have found extensive applications in industry. Without loss of generality, this paper utilizes the on-board diagnostic systems of automobiles as an illustrative example. A failed diagnostic system generates /spl alpha/ or /spl beta/. /spl alpha/ error incurs unnecessary warranty costs to manufacturers, while /spl beta/ error causes potential losses to customers. Therefore, the reliability and robustness of diagnostic systems are important to both manufacturers and customers. This paper presents a method for assessing the reliability and robustness of the diagnostic systems by using warranty data. We present the definitions of robustness and reliability of the diagnostic systems, and the formulae for estimating /spl alpha/, /spl beta/ and reliability. To utilize warranty data for assessment, we describe the two-dimensional (time-in-service and mileage) warranty censoring mechanism, model the reliability function of the prime systems, and devise warranty data mining strategies. The impact of /spl alpha/ error on warranty cost is evaluated. Fault tree analyses for /spl alpha/ and /spl beta/ errors are performed to identify the ways for reliability and robustness improvement. The method is applied to assess the reliability and robustness of an automobile on-board diagnostic system.