Using machine learning methods in power equipment repair programs

The article discusses the process of planning the repair of energy equipment. Using a decision support system is proposed because of the large number of rules of comparing flow charts of technical defects. Such a system can speed up the planning process and reduce economic costs. A conceptual model of the system has been built; further it will be presented as a multi-label classification of cross-cutting classes. The “one-vs-all” approach has been used: each flow chart can use its individual classifier. Metrics are proposed for evaluating classifiers: a portion of accurately classified objects, precision, fullness and $F$-measure. To summarize the evaluation results the concept of micro-average was chosen. A defect classification algorithm has been described. An experiment was conducted using different classification algorithms: decision trees, Bayes classifier and multilayer perceptron. The results of the experiment proved that $80$–$90\%$ of the correctly classified objects were found (high values), but the average values of accuracy and fullness occurred low ($3$–$7\%$). There were found sets of data, where different output data corresponded to similar input data. Thus, machine learning can be used to support decision-making, but in some cases information about the order is not complete. Defect classification can be combined with manual clarifying of results or with different algorithms.