Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods

Evgeny Lisin,Andrey Rogalev,Wadim Strielkowski,Marek Dvořák,Ivan Shcherbatov,Grigory Tsurikov

doi:10.3390/electronics10243145

Evgeny Lisin, Andrey Rogalev + Show 4 more

Open Access

https://doi.org/10.3390/electronics10243145

Copy DOI

Abstract

Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment’s technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect “clogging of drainage channels” showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried out. The result of the application of the model was the forecast of the technical condition index achievement and the limiting characteristic according to the current time data on its values. The developed model accurately predicted the behavior of the technical condition index at time intervals of 3 and 10 h, which made it possible to draw a conclusion about its applicability for early identification of the investigated defect in the automatic control system of the turbine. Thus, we can conclude that the joint solution of regression and classification problems using an information parameter in the form of a technical condition index allows one to develop systems for predicting defects, one significant advantage of which is the ability to early determine the development of degradation phenomena in power equipment.

Highlights

The production assets of the electric power industry include many units of power equipment interconnected by a single technological process for the production and conversion of energy at power plants
The hypothesis we put forward about the possibility of studying the developing degradation situation in time by jointly solving the problems of predicting equipment technical condition index and determining the probability that its current technical state belongs to the state of defects turned out to be correct
An approach to its improvement based on the assessment of equipment technical condition index and time determination to reach its limiting state is proposed, which makes it possible to increase the information content of the predictive analytics system

Summary

Introduction

The production assets of the electric power industry include many units of power equipment interconnected by a single technological process for the production and conversion of energy at power plants. The determination of the technical condition index in accordance with Formulas (1) and (2), both for a unit of technological equipment and for a group of its functional units on the basis of technological parameters characterizing the technical state of the equipment, makes it possible to assess the arising technological disturbances in the operation of power equipment and, as a result of this, to identify the development of a manufacturing defect or the occurrence of a failure This approach is based on the determination of the probability that the current technical condition of the equipment belongs to the class of defects. The problem of predicting the probability of belonging to the current technical state to a class of defects is solved on the basis of ensembles of “risky” (the method of gradient boosting of decision trees) and “cautious” (method of logistic regression) methods of machine learning, followed by a visually understandable and interpretable definition and forecasting of TCI and the time to reach it limiting value. The bias is a hyperparameter of the neural network model

Approbation of the Results and Discussion of Results

Findings

Conclusions