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Most preventive maintenance (PM) models require, as inputs, information on the behaviour of the equipment under failure-only maintenance (FM). Once a schedule of PM has been applied, data arising from failures are affected by the PM. Over life the behaviour that would occur under FM changes. But PM also tends to delay aging processes. This paper examines: 1) how data collected under four kinds of PM policy can be modified to re-assess the FM characteristics, and 2) how to tell whether an apparent change in them is important. The approach takes account of engineering as well as statistical factors. It concludes that: 1. Estimation of distributions and costs as they would be under FM from data collected under PM cannot be accurate but a suboptimal policy can be worse. 2. A different type of PM policy might be required following changes to costs and/or distribution. The optimum under one model might not be the overall optimum. 3. The solutions suggested in the paper remain unproved due to the ``data problem''. Mathematical research alone can never produce workable procedures. A viable methodology will need years of experimentation with real systems. 4. The potential savings are huge.