**6. Conclusions**

This paper mainly studies the fault diagnosis of hydraulic valves. Based on the status monitoring data of the measured inlet and outlet pressure signals of the hydraulic valve, PCA was adopted to reduce the dimensions of the data, and the XGBoost algorithm was used to construct a machine learning model for hydraulic valve fault diagnosis. By testing the evaluation indexes of the machine learning model, the effectiveness and superiority of the above method are proved. The main conclusions are as follows.

(1) In this study, the pressure signals of the hydraulic valve are utilized as the sample data for fault diagnosis in order to realize accurate diagnosis and classification of hydraulic valve faults. Then, a novel fault diagnosis method for hydraulic valves based on the variation characteristics of pressure signals is proposed.

(2) PCA dimensionality reduction for the original data set of feature vectors can not only significantly reduce the dimension of the feature vector, but also remove redundant information in the original data set. The principal component feature set after dimensionality reduction is used to train the XGBoost machine learning, in order to construct the fault diagnosis model for the hydraulic valve. The test results indicate that the precision mean of the model is 96.9%, the recall rate mean is 96.7%, and the *F*1 score mean is 96.6% on the test set. Compared with the decision tree and random forest models, the constructed model has higher accuracy.

(3) This research builds a fault diagnosis model for the hydraulic valve in the visual workflow of HUAWEI Cloud MLS, and carries out data processing, model training, evaluation, and prediction. In this way, hydraulic valve fault diagnosis, machine learning algorithms, and HUAWEI cloud are organically combined together, which can provide a theoretical basis and practical guidance for the remote fault diagnosis of hydraulic components and the predictive maintenance of hydraulic systems.

**Author Contributions:** W.J. and A.J. conceived and designed the method. S.Z. and H.N. analyzed the data. Y.L. and Y.Z. wrote the paper.

**Funding:** This research was funded by The National Natural Science Foundation of China grant number 51875498, 51475405, 51805214; This research was funded by Key Program of Hebei Natural Science Foundation grant number E2018203339; This research was funded by Innovation Foundation for Graduate Students of Hebei Province grant number CXZZBS2018045; This research was funded by China Postdoctoral Science Foundation grant number 2019M651722; This research was funded by Young Problems in the Special Project of Basic Research of Yanshan University grant number 15LGB005. And The APC was funded by 51875498.

**Conflicts of Interest:** The authors declare no conflict of interest.
