Robust reliability design of diagnostic systems

Diagnostic systems are software-based built-in-test systems which detect, isolate and indicate the failures of the prime systems. The use of diagnostic systems reduces the losses due to the failures of the prime systems and facilitates subsequent repairs. Thus diagnostic systems have found extensive applications in industry. The algorithms performing operations for diagnosis are important parts of the diagnostic systems. If the algorithms are not adequately designed, the systems will be sensitive to noise sources, and commit type I error (a) and type II error (/spl beta/). This paper is to improve the robustness and reliability of the diagnostic systems through robust design of the algorithms by using reliability as an experimental response. To conduct the design, we define the reliability and robustness of the systems, and propose their metrics. The influences of /spl alpha/ and /spl beta/ errors on reliability are evaluated and discussed. The effects of noise factors on robustness are assessed. The classical P-diagram is modified; a generic P-diagram containing both prime and diagnostic systems is created. Based on the proposed dynamic reliability metric, we describe the steps for robust reliability design and develop a method for experimental data analysis. The robustness and reliability of the diagnostic systems are maximized by choosing optimal levels of algorithm parameters. An automobile example is presented to illustrate how the proposed design method is used. The example shows that the method is efficient in defining, measuring and building robustness and reliability.