Application of ensemble machine learning methods for predicting the technical state of an object

To ensure the reliable functioning of a technical object, it is necessary to predict its state for the upcoming time interval. Let the technical state of the object be characterized at a certain point in time by a set of parameters established by the technical documentation for the object. It is assumed that for certain values of these parameters, the object may be in a good or faulty state. It is required by the values of these parameters to estimate the state of the object in the upcoming time interval. Supervised machine learning methods can be applied to solve this problem. However, to obtain good results in predicting the state of an object, it is necessary to choose the correct training model. One of the disadvantages of machine learning models is high bias and too much scatter. In this paper, to reduce the scatter of the model, it is proposed to use ensemble machine learning methods, namely, the bagging procedure. The main idea of the ensemble of methods is that with the right combination of weak models, more accurate and robust models can be obtained. The purpose of bagging is to create an ensemble model that is more reliable than the individual models that make up it. One of the big advantages of bagging is its concurrency, since different ensemble models are trained independently of each other. The effectiveness of the proposed approach is shown by the example of predicting the technical state of an object by eight parameters of its functioning. To assess the effectiveness of the application of ensemble machine learning methods for predicting the technical state of an object, the quality criteria of binary classification are used: accuracy, completeness, and F-measure. It is shown that the use of ensemble machine learning methods can improve the accuracy of predicting the state of a technical object by 4% -9% in comparison with basic machine learning methods. This approach can be used by specialists to predict the technical condition of objects in many technical applications, in particular, in aviation.