An Optimal Equipment Replacement Model Using Logical Analysis of Data

In this study, Logical Analysis of Data LAD is used to propose an optimal equipment replacement model. Unlike most classification techniques, LAD has the advantage of not relying on any statistical theory which enables it to overcome the conventional problems concerning the statistical properties of datasets. LAD is employed to estimate the equipment's survival and failure probabilities. These probabilities are then used to build a dynamic programming model to minimize the average long-term replacement cost of the equipment. The proposed method is successfully applied on Prognostics and Health Management challenge dataset provided by NASA Ames Prognostics Data Repository. The performance of the model is compared to that of the well-known Proportional Hazards Model.