Интеллектуальная технология оценки остаточного ресурса сложных технических систем

Abstract

The task of estimating the residual life of complex technical systems has recently become increasingly important. For the pre-trustworthy estimation of this indicator it is required to process large arrays of data on the current state of the system under study. At the same time the task of reconstruction of the model of degradation processes development leading to the occurrence of failures requires solving a number of problems. In this regard, there is a need to use intelligent methods of data processing, which include methods of time series anomaly analysis. Purpose of the study: development of a method for detecting contextual anomalies of time series, allowing to determine the degree of development of degradation processes that lead to the occurrence of failures. Methods. An analogy was established between machine learning methods in supervised, semi-supervised and unsupervised modes and groups of anomaly detection methods differing depending on the degree of availability of labels that characterize the properties and attributes of the corresponding time series, which made it possible to evaluate the features of simple and complex anomalies from unified positions. Results. A spectral method for the analysis of contextual anomalies of time series has been developed, which, in contrast to the known spectral methods involving frequency analysis of time series, uses a special basis of exponential functions; the methodology for calculating the spectral coefficients of the investigated time series, on the basis of which a generalized attribute is calculated, allowing to attribute the investigated case to a normal or anomalous group, is outlined. Conclusion. The proposed method of estimating the residual life of complex technical systems based on the analysis of time series anomalies allows timely detection of the occurrence and development of degradation processes leading to the occurrence of failures, which increases the reliability and safety of technical systems, as well as reduces costs and time for their maintenance.