Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space
1
Computational Intelligence Research Group, Institute of Computer Science, University of Wroclaw, 50-383 Wroclaw, Poland
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Faculty of Mining and Geoengineering, AGH University of Science and Technology, 30-059 Cracow, Poland
3
Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, 50-421 Wroclaw, Poland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 5979; https://doi.org/10.3390/s20215979
Received: 3 September 2020
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Revised: 15 October 2020
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Accepted: 16 October 2020
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Published: 22 October 2020
(This article belongs to the Special Issue Sensing Technology and Data Interpretation in Machine Diagnosis and Systems Condition Monitoring)
Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset.
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Keywords:
planetary gearbox; condition monitoring; vibration; spectral analysis; non-stationary operations; multidimensional symptom space; decision trees
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MDPI and ACS Style
Lipinski, P.; Brzychczy, E.; Zimroz, R. Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space. Sensors 2020, 20, 5979. https://doi.org/10.3390/s20215979
AMA Style
Lipinski P, Brzychczy E, Zimroz R. Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space. Sensors. 2020; 20(21):5979. https://doi.org/10.3390/s20215979
Chicago/Turabian StyleLipinski, Piotr, Edyta Brzychczy, and Radoslaw Zimroz. 2020. "Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space" Sensors 20, no. 21: 5979. https://doi.org/10.3390/s20215979
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