چكيده به لاتين
Predictive maintenance is a tactic that monitors equipment performance and condition during normal operation to reduce the risk of failure. This maintenance strategy is very important in terms of cost savings perspective. It also reduces scheduled downtime, increases equipment life, optimizes employee productivity, and increases organizational revenue.
One of the barriers to apply predictive maintenance in organizations is the inability to analyze processes that lead to breakdowns that ultimately lead to equipment failure. On the other hand, due to the growth of technology and more data production in the world, the use of data analysis techniques and process analysis tools is increasing day by day. In this regard, the focus of this study is on providing a model based on process mining algorithms that can be used to predict the breakdown and failure of devices and machines and thus avoid its costs .
The methodology used in this research is PM2, which is designed to support process mining projects. This methodology consists of 6 stages, each of them has a number of inputs, activities and outputs. These 6 steps are planning, extraction, data processing, mining and analysis, evaluation, process improvement and support .
According to this methodology, these steps were applied to the data set of Japan's Nexperia Company and three process models of failures and errors of one of the equipments based on three algorithms of alpha mining, heuristic causal net and genetic mining were discovered and identified. Then, these three models were evaluated using 4 criteria of simplicity, precision, generalizability and fitness, and based on this, the heuristic causal net model was selected as the appropriate and desirable model of this research. The causal relationships of this model can be used to predict future breakdowns and failures of the equipment under study. Through these relationships and also by finding frequent errors, it is possible to dynamically study the behavior of equipment, more accurate planning, supply and allocation of appropriate resources for equipment replacement, necessary specialist, financial resources, etc. In addition to the mentioned achievements, the results of this research can be used to create a smart platform for net operations for this equipment.
