APPROACH TO DATA DIMENSIONALITY REDUCTION AND DEFECT CLASSIFICATION BASED ON VIBRATION ANALYSIS FOR MAINTENANCE OF ROTATING MACHINERY
DOI:
https://doi.org/10.15588/1607-3274-2025-1-8Keywords:
dimensionality reduction, significant features, defect detection, MobileNetV2Abstract
Context. The actual problem of effective intelligent diagnostics of malfunctions of rotating equipment is solved. The object of study is the process of data dimensionality reduction and defect classification based on vibration analysis for maintenance of rotating machines. The subject of study is the methods of dimension reduction and defect classification by vibration analysis.
Objective. Development of an approach to data dimensionality reduction and defect classification based on vibration analysis for maintenance of rotating machines
Method. The comprehensive approach to data dimensionality reduction and defect classification based on vibration analysis is proposed, which solves the problem of data dimensionality reduction for training classifiers and defect classification, and also solves the problem of building a neural network classifier capable of ensuring the speed of fault classification without loss of accuracy on data of reduced dimensionality. The approach differs from the existing ones by the possibility of using optional union and intersection operators when forming a set of significant features, which provides flexibility and allows to adapt to different contexts and data types, ensuring classification efficiency in cases of large-dimensional data.
A denoising method allows to preserve important information, avoiding redundancy and improving the quality of data for further analysis. It involves calculating the signal-to-noise ratio, setting thresholds, and applying a fast Fourier transform that separates relevant features from noise. Applying the LIME method to a set of machine learning models allows to identify significant features with greater accuracy and interpretability. This contributes to more reliable results, as LIME helps to understand the influence of each feature on the final model solution, which is especially important when working with large datasets, where the importance of individual features may not be obvious. The implementation of optional operators of union and intersection of significant features provides additional flexibility in choosing an approach to defining important features. This allows the method to be adapted to different contexts and data types, ensuring efficiency even in cases with a large number of features.
Results. The developed method was implemented in software and examined when solving the problem of defect classification based on vibration analysis for maintenance of rotating machines.
Conclusions. The conducted experimental studies confirmed the high efficiency and workability of the proposed approach for
reducing the dimensionality of data and classifying defects based on vibration analysis in the aspect of maintenance of rotating machines. Prospects for further research will be directed to the search for alternative neural network architectures and their training to reduce training time
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