An Adaptive Filtering-based Adjustment Method for Reliability Parameters of Vehicle Systems During Their Lifecycle

• Main Authors: O. M. Lurie, K. E. Byakov, Tihon Dmitrievich Pozdnyakov
• Format: Article
• Language: Russian
• Published: MGTU im. N.È. Baumana 2017-01-01
• Series: Nauka i Obrazovanie
• Subjects: reliability parameters; automotive systems; Kalman filtering; non-parametric definition of the distribution of failure probability; system lifecycle
• Online Access: http://technomag.edu.ru/jour/article/view/1179

<p>The paper considers a problem of difficult accessibility and low quality of data on the reliability parameters of the vehicle system components and the difficulties arising from this problem to estimate the reliability parameters of the systems themselves as statutorily required and in terms of international standards (e.g. ISO 26262). As a problem solution, the paper proposes a method for adjustment of the system reliability estimates based on the field observation of system failures. The method based on a Kalman filter uses non-parametric definition of the failure probability distribution (quantile «folding» of the distribution) with subsequent «unfolding» via Monte Carlo.</p><p>A mathematical model shows how to use this method.  For clarity, the estimates of reliability parameters are given at the time of rollout (100 % of systems are in working order) and upon the failure of 25%, 50%, 75% and 100% of produced systems, respectively. A КК plot shows that the reliability estimates gradually become close to the field reliability data.</p><p>The method allows, by varying filter parameters, a more conservative estimate of the reliability parameters or an estimate, which is more in accord with the field data. Thus, the results can be used at all stages of the system lifecycle, namely when developing, manufacturing and upon completing production for the aftermarket services.</p>