LAPSE:2019.1178
Published Article
LAPSE:2019.1178
Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost
Yafei Lei, Wanlu Jiang, Anqi Jiang, Yong Zhu, Hongjie Niu, Sheng Zhang
November 24, 2019
A novel fault diagnosis method is proposed, depending on a cloud service, for the typical faults in the hydraulic directional valve. The method, based on the Machine Learning Service (MLS) HUAWEI CLOUD, achieves accurate diagnosis of hydraulic valve faults by combining both the advantages of Principal Component Analysis (PCA) in dimensionality reduction and the eXtreme Gradient Boosting (XGBoost) algorithm. First, to obtain the principal component feature set of the pressure signal, PCA was utilized to reduce the dimension of the measured inlet and outlet pressure signals of the hydraulic directional valve. Second, a machine learning sample was constructed by replacing the original fault set with the principal component feature set. Third, the MLS was employed to create an XGBoost model to diagnose valve faults. Lastly, based on model evaluation indicators such as precision, the recall rate, and the F1 score, a test set was used to compare the XGBoost model with the Classification And Regression Trees (CART) model and the Random Forests (RFs) model, respectively. The research results indicate that the proposed method can effectively identify valve faults in the hydraulic directional valve and have higher fault diagnosis accuracy.
Keywords
extreme gradient boosting (XGBoost), fault diagnosis, HUAWEI Cloud machine learning service (MLS), hydraulic valve, principal component analysis (PCA)
Suggested Citation
Lei Y, Jiang W, Jiang A, Zhu Y, Niu H, Zhang S. Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost. (2019). LAPSE:2019.1178
Author Affiliations
Lei Y: College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China [ORCID]
Jiang W: College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
Jiang A: College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Zhu Y: Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
Niu H: Qinhuangdao Shouqin Metal Materials Co., Ltd., Qinhuangdao 066009, China
Zhang S: College of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
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Journal Name
Processes
Volume
7
Issue
9
Article Number
E589
Year
2019
Publication Date
2019-09-03
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr7090589, Publication Type: Journal Article
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LAPSE:2019.1178
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doi:10.3390/pr7090589
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Nov 24, 2019
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CC BY 4.0
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Nov 24, 2019
 
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Nov 24, 2019
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Original Submitter
Calvin Tsay
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