Affiliation:
1. School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China
Abstract
In the operation and maintenance of planetary gearboxes, the growth of monitoring data is often faster than its analysis and classification. Careful data analysis is generally considered to require more expertise. Rendering the machine learning algorithm able to provide more information, not just the diagnosis conclusion, is promising work. This paper proposes an analysis and diagnosis two-stage framework based on time-frequency information analysis. In the first stage, a U-net model is used for the semantic segmentation of vibration time-frequency spectrum to highlight faulty feature regions. Shape features are then calculated to extract useful information from the segmented image. In the second stage, the decision tree algorithm completes the health state classification of the planetary gearboxes using the input of shape features. The real data of wind turbine planetary gearboxes and augmented data are utilized to verify the proposed framework’s effectiveness and superiority. The F1-score of segmentation and the classification accuracy reach 0.942 and 97.4%, respectively, while in the environmental robustness experiment, they reached 0.747 and 83.1%. Equipping the two-stage framework with different analytical methods and diagnostic algorithms can construct flexible diagnostic systems for similar problems in the community.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Liaoning Province
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference43 articles.
1. Deep Residual Networks with Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes;Zhao;IEEE Trans. Ind. Electron.,2018
2. Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review;Wang;Mech. Syst. Signal Process.,2019
3. Planetary gearbox fault diagnosis via rotary encoder signal analysis;Feng;Mech. Syst. Signal Process.,2021
4. Fu, Y., Liu, Y., and Yang, Y. (2022). Multi-Sensor GA-BP Algorithm Based Gearbox Fault Diagnosis. Appl. Sci., 12.
5. Comparative analysis of fuzzy classifier and ANN with histogram features for defect detection and classification in planetary gearbox;Hameed;Appl. Soft Comput.,2021
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