Ball bearing multiple failure diagnosis using feature-selected autoencoder model

Ren Chi Cheng, Kuo Shen Chen

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)


Recently, with the advance in information technology, pure data-driven approaches such as machine learnings have been widely applied in status diagnosis. However, the accuracy of those predictions strongly relies on the original data, which largely depends on the selected sensors and signal features. Furthermore, for unsupervised machine learning schemes, although it could avoid the concern of labeling in training, it lacks a quantified evaluation of the prediction results. These concerns significantly limit the effectiveness of modern machine learning and thus should be investigated. Meanwhile, ball bearings are fundamental key machine elements in rotating machinery and their condition monitoring should be critical for both quality control and longevity assessment. In this paper, by utilizing ball bearing failure diagnosis as the main theme, the flow of feature selection and evaluation, as well as the evaluation flow for multiple failure diagnosis, is developed for accessing the status of bearings in their imbalance, lubrication, and grease contamination levels based on unsupervised machine learning. The experimental results indicated that with proper feature selection, the failure identification could be more definite. Finally, a novel model based on the second norm to quantify the classification level of each cluster in hyperspace is proposed as the measure for unsupervised machine learning as the basis for performance evaluation and optimization of unsupervised machine learning schemes and should benefit related machine reliability evaluation studies and applications.

頁(從 - 到)4803-4819
期刊International Journal of Advanced Manufacturing Technology
出版狀態Published - 2022 6月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 軟體
  • 機械工業
  • 電腦科學應用
  • 工業與製造工程


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