Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM

This paper describes two algorithms for feature extraction from the Poincaré plot which is constructed with the vibration signals measured in roller bearings and gearboxes. The extracted features are used for classifying 10 types of fault conditions in a gearbox and 7 types of fault conditions a rol...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2022-04, Vol.33 (4), p.1031-1055
Main Authors: Medina, Rubén, Macancela, Jean Carlo, Lucero, Pablo, Cabrera, Diego, Sánchez, René-Vinicio, Cerrada, Mariela
Format: Article
Language:English
Subjects:
Algorithms
Bearings
Business and Management
Classification
Control
Datasets
Feature extraction
Gearboxes
Machines
Manufacturing
Mechatronics
Processes
Production
Robotics
Roller bearings
Signal processing
Support vector machines
Vibration
Vibration measurement
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Summary:This paper describes two algorithms for feature extraction from the Poincaré plot which is constructed with the vibration signals measured in roller bearings and gearboxes. The extracted features are used for classifying 10 types of fault conditions in a gearbox and 7 types of fault conditions a roller bearings. Both vibration signal datasets were acquired at different loads and speeds. The feature extraction using Algorithm 1 performs the feature calculation from the Poincaré plot constructed with the raw vibration signals. In contrast, the Algorithm 2 requires an additional stage where the vibration signal is pre-processed for identifying the peaks of the signal. This peak sequence is equivalent to a non-uniform sub-sampling of the vibration signal that retains relevant information useful for fault classification. The fault classification is attained by using a multi-class Support Vector Machine. The proposed method is tested using the tenfold cross-validation. Results show that both algorithms could attain classification accuracies as high as 99.3% for the gearbox dataset and 100% for the roller bearings. The results are compared to other classification approaches performed on the same datasets by using other different features. The comparison shows that the approach in this paper has a performance as good as obtained by using well-known statistical features.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-020-01712-9