Field repairable system modeling with missing failure information

Many systems, ranging from military vehicles to drink dispensers used in restaurants, are repairable. Operation and failure data from this type of system are often collected by manufacturers and customers. The data are used to monitor system performance as well as for reliability prediction and system improvement. However, due to errors in collecting the data, including human errors, raw field data are rarely suitable for reliability modeling. Data cleanup and certain assumptions have to be made in order to use existing statistical modeling technologies. This is the main challenge the authors have encountered when they tried to model a fleet of fielded repairable systems. An example is a situation where multiple machines are located at the same site, and data on the site's location, instead of the failed machine's ID, are collected. Without knowing which particular machine had the failure, the existing non-homogeneous Poisson process (NHPP) modeling method cannot be applied [1-3]. This type of missing data is called masked data for repairable systems. In this paper, a method for modeling masked failure data is proposed and its application is illustrated using a case study. The proposed method can be used to predict the number of failures and the confidence bounds at a given operation time.