SETTING PREVENTIVE MAINTENANCE SCHEDULES WHEN DATA ARE SPARSE

When new production lines are established,little information is availa ble about their reliability. The evaluation of such systems is a learn ing process and knowledge is continually updated as more information b ecomes 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 stra tegies under the model assumptions. This approach also includes prior knowledge about the manufacturing process and similar systems. Our app roach is a first reconnaissance into a new held, exemplary of ways to solve these problems, rather than an algorithm that can be readily app lied.