IDENTIFICATION OF EQUIPMENT DEGRADATION PHASE IN PREVENTATIVE MAINTENANCE SYSTEMS

Abstract

Subject of Research. The paper proposes a novel organization technique for preventive maintenance systems (including condition-based and predictive maintenance systems) based on the use of modern machine learning methods. The systems are operating using an original, non-parametric identification method for the current degradation phase of serviced equipment. Method. The proposed method comprises reducing the task of the current phase identification of the equipment degradation phase to interval estimation of the value of the so-called “health index” parameter of the equipment. This parameter is represented as a step function with the arguments in terms of a set of the measurable equipment objective parameters. The current equipment degradation phase is determined by classification approach. At this, based on the analysis of the observed data, it is decided upon what class (state phase) these data correspond to. Measurements from a group of sensors, in general, of various physical nature, which are located both on the surface and inside the equipment being monitored are used as data for identification of the equipment degradation stage. Mathematically, the proposed approach is reduced to a weighted combination of two classifiers. One of the classifiers of this combination is based on solving a group of binary classification problems. The second classifier is based on “Remaining Useful Life” parameter estimation by the method of nonparametric regression. Main Results. As distinguished from traditional approaches, the proposed approach uses a minimum of a priori information about the principles of operation and the internal structure of the equipment being serviced. The approach is based on the usage of the “health index” equipment parameter presented in the form of a step function. The novelty of the approach lies in the simultaneous use of the “health index” step function and the weighted combination of two classifiers with various structure. The proposed method showed good results when being tested on the C-MAPPS Dataset database, which contains data on failures of turbofan engines modeled using a thermodynamic simulation model. The pre-failure status of the equipment is identified with the probability of 99%. Practical Relevance. The obtained results and algorithms can be used in preventive maintenance systems aimed at reliable identification of the equipment degradation current stage.

Keywords

• predictive maintenance
• condition-based maintenance
• machine learning
• ml pdm
• xgboost