Setting preventive maintenance schedules when data are sparse

When new production lines are established, little information is available about their reliability. The evaluation of such systems is a learning process and knowledge is continually updated as more information becomes available. This paper considers stochastic models when data are sparse, with emphasis on preventive maintenance intervention to avoid system failure. Bayesian methods are adopted, leading to optimal strategies under the model assumptions. This approach also includes prior knowledge about the manufacturing process and similar systems. Our approach is a first reconnaissance into a new field, exemplary of ways to solve these problems, rather than an algorithm that can be readily applied.