Modern products frequently feature monitors designed to detect actual or impending malfunctions. Typically, monitoring algorithms consist of real-time processing of a stream of incoming data, for example with a cumulative sum (Cusum) or an exponentially weighted moving average (EWMA), and setting an alarm when an appropriate statistic exceeds a threshold. False alarms (Type I errors) or excessive delays in detecting real malfunctions (Type II errors) can seriously reduce monitor utility. Sound engineering practice includes physical evaluation of error rates. Type II error rates are relatively easy to evaluate empirically. However, adequate evaluation of a low Type I error rate is difficult without using accelerated testing concepts, inducing false alarms using artificially low thresholds and then selecting production thresholds by appropriate extrapolation, as outlined here. Decisions regarding the desired magnitudes of both accelerated testing and follow-on model verification can be performed by following two recommendations of Meeker and Escobar: (a) simulate data collection and analysis for a wide range of plausible situations for reality and for the test plan; (b) use Bayesian reliability theory to pool information from similar monitors across a variety of products to obtain tighter estimates of the false-alarm rate from limited testing. This will help decision makers formally balance the costs and risks of a variety of procedures. This acceleration methodology allows for informed determination of detection thresholds and confidence in monitor performance with substantial reductions over current alternatives in time and cost required for monitor development.
Accelerated testing of on-board diagnostics
Beschleunigter Test der bordeigenen Diagnostik
Quality and Reliability Engineering International ; 23 , 2 ; 189-201
2007
13 Seiten, 6 Bilder, 20 Quellen
Aufsatz (Zeitschrift)
Englisch
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