Failure predictions in repairable multi-component systems

Multi-component repairable systems cannot be modeled by continuous distributions, such as the Weibull. Failures occurring in repairable systems are examples of a series of discrete events which occur randomly in a continuum. These situations which are stochastic point processes are analyzed using the statistics of event series. The Crow model, or power law nonhomogeneous Poisson process, is recognized by the reliabilty community as being one of the best models for repairable systems. In this research, failure data from multiple versions of certain mechanical systems are modelled by Crow’s nonhomogeneous Poisson process, NHPP. The expected number of failures predicted for the respective system by the Crow model are compared to predictions using a Monte Carlo simulation that utilizes Weibull parameters of the major components of the system. The objective is to prove that a simulation based on Weibull parameters of the major component failure modes is able to duplicate the overall system prediction that the Crow NHPP model gives. The simulations, based on fitting component failure data to a continuous distribution such as the Weibull, are more valuable in that they give failure prediction results that can be traced to individual components. The advantage here is that only the major component failure modes will need to be included in the simulation and the NHPP model can be utilized as a gauge to determine when the simulation has the appropriate component failure modes included. The uniqueness in this research lies in the use of the simulation approach in conjunction with the Crow NHPP model so that the failure modes can be identified. The Crow model predicts when the overall system will be down, and then the simulation predicts the number of failures from each of the included components. The simulation can identify a finite number of parts that contribute to the overall system downtime. This information can be used to design an optimum preventive maintenance program or guide research into more reliable components.