METHOD OF PREVENTING FAILURES OF ROTATING MACHINES BY VIBRATION ANALYSIS USING MACHINE LEARNING TECHNIQUES

Abstract

Context. The problem of determining transitional conditions that precede the shift from an operating state to a non-operating state based on data obtained from the sensors of rotating machine elements is being solved. The object of the study is the process of detecting faults and states that indicate an approach to breakdown in rotating machine elements based on data obtained from sensors.
The subject of the study is the application of k-means and the elbow method algorithms for clustering and convolutional neural networks for classifying sensor data and detecting near-failure states of machine elements.
Objective. The purpose of the work is to create a method for processing sensor data from rotating machines using convolutional neural networks to accurately detect conditions close to failure in rotating machine elements, which will increase the efficiency of maintenance and prevent equipment failures.
Method. The proposed method of preventing failures of rotating machines by vibration analysis using machine learning techniques using a combination of clustering and deep learning methods. At the first stage, the sensor data undergoes preprocessing, including normalization, dimensionality reduction, and noise removal, after which the K-means algorithm is applied. To determine the
optimal number of clusters, the Elbow method is used, which provides an effective grouping of the states of rotating machine elements,
identifying states close to the transition to fault. A CNN model has also been developed that classifies clusters, allowing for the accurate separation of nominal, fault, and transitional conditions. The combination of clustering methods with the CNN model improves the accuracy of detecting potential faults and enables timely response, which is critical for preventing accidents and ensuring
the stability of equipment operation.
Results. A method of preventing failures of rotating machines by vibration analysis using machine learning techniques and a relevant software package have been developed. The implemented method allows us to identify not only normal and emergency
states but also to distinguish a third class – transitional, close to breakdown. The quality of clustering for the three classes is confirmed
by the value of the silhouette coefficient of 0.506, which indicates the proper separation of the clusters, and the Davis-Boldin index of 0.796, which demonstrates a high level of internal cluster coherence. Additionally, CNN was trained to achieve 99% accuracy for classifying this class, which makes the method highly efficient and distinguishes it from existing solutions.
Conclusions. A method of preventing failures of rotating machines by vibration analysis using machine learning techniques was
developed, the allocation of the third class – transitional, indicating a state close to breakdown – was proposed, and its effectiveness
was confirmed. The practical significance of the results lies in the creation of a neural network model for classifying the state of rotating elements and the development of a web application for interacting with these models.