Application of Petri nets to reliability prediction of occupant safety systems with partial detection and repair

This paper presents an application of stochastic Petri nets (SPN) to calculate the availability of safety critical on-demand systems. Traditional methods of estimating system reliability include standards—based or field return-based reliability prediction methods. These methods do not take into account the effect of fault-detection capability and penalize the addition of detection circuitry due to the higher parts count. Therefore, calculating system availability, which can be linked to the system's probability of failure on demand (Pfd), can be a better alternative to reliability prediction. The process of estimating the Pfd of a safety system can be further complicated by the presence of system imperfections such as partial-fault detection by users and untimely or uncompleted repairs. Additionally, most system failures cannot be represented by Poisson process Markov chain methods, which are commonly utilized for the purposes of estimating Pfd, as these methods are not well-suited for the analysis of non-Poisson failures. This paper suggests a methodology and presents a case study of SPN modeling adequately handling most of the above problems. The model will be illustrated with a case study of an automotive electronics airbag controller as an example of a safety critical on-demand system.