Prognosis of bearing and gear wears using convolutional neural network with hybrid loss function

Chang Cheng Lo, Ching Hung Lee*, Wen Cheng Huang

*此作品的通信作者

研究成果: Article同行評審

23 引文 斯高帕斯(Scopus)

摘要

This study aimed to propose a prognostic method based on a one-dimensional convolutional neural network (1-D CNN) with clustering loss by classification training. The 1-D CNN was trained by collecting the vibration signals of normal and malfunction data in hybrid loss function (i.e., classification loss in output and clustering loss in feature space). Subsequently, the obtained feature was adopted to estimate the status for prognosis. The open bearing dataset and established gear platform were utilized to validate the functionality and feasibility of the proposed model. Moreover, the experimental platform was used to simulate the gear mechanism of the semiconductor robot to conduct a practical experiment to verify the accuracy of the model estimation. The experimental results demonstrate the performance and effectiveness of the proposed method.

原文English
文章編號3539
頁(從 - 到)1-18
頁數18
期刊Sensors (Switzerland)
20
發行號12
DOIs
出版狀態Published - 6月 2020

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