Abstract
This paper describes a comparison of three types of feature sets. The feature sets were intended to classify 13 faults in a centrifugal pump (CP) and 17 valve faults in a reciprocating compressor (RC). The first set comprised 14 non-linear entropy-based features, the second comprised 15 information-based entropy features, and the third comprised 12 statistical features. The classification was performed using random forest (RF) models and support vector machines (SVM). The experimental work showed that the combination of information-based features with non-linear entropy-based features provides a statistically significant accuracy higher than the accuracy provided by the Statistical Features set. Results for classifying the 13 conditions in the CP using non-linear entropy features showed accuracies of up to 99.50%. The same feature set provided a classification accuracy of 97.50% for the classification of the 17 conditions in the RC.
| Original language | English |
|---|---|
| Article number | 3033 |
| Journal | Mathematics |
| Volume | 10 |
| Issue number | 17 |
| DOIs | |
| State | Published - Sep 2022 |
Bibliographical note
Funding Information:This research was funded by the MoST Science and Technology Partnership Program (KY201802006) and National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, and by the Universidad Politécnica Salesiana through the GIDTEC research group.
Publisher Copyright:
© 2022 by the authors.
Keywords
- approximate entropy
- fault classification
- non-linear systems
- phase space reconstruction
- random forest
- support vector machines