Discovery of Useful and Hidden Patterns from the Equipment Computerize Maintenance Management System Data Along with Estimation of Equipment Costs and Discovering outliers

A few years after the implementation of computerize maintenance management systems (CMMS), showing the analysis results of the collected data in these systems is necessary and important for continuing the usage of these systems. Equipment downtime and cost data can identify patterns of equipment faults and equipment costs. It can also show deficiencies in the implementation of maintenance systems. Detection of failures and important factors of failures such as the type of mission, geographical conditions, quality of parts and soon helped to classify and determine the cost norms based on the type of mission. These analyses can help managers to make appropriate decisions more accurately and make proposals for improving the systems.Data mining can provide a view of equipment readiness and the factors that affecting them. This view can be used to estimate the costs of maintenance and repair of equipment. Knowing the time of certain failures can be used to plan maintenance program. The issues are useful for improving knowledge and culture in the implementation of equipment. These analyses caused better and more accurate maintenance and usage of equipment.In this paper, we introduce some data mining applications in maintenance and repairs activities. It shows some hidden rules discovered from data of equipment maintenance systems in a case. Clustering models, neural networks, decision trees, visualization and descriptive statistics are used in this case study and some of results introduce in this paper. The findings show that there are certain pattern depending on the type of mission and how to use the equipment.