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25 / 2021-06-22 12:36:39
A comprehensive review on blade damage detection and prediction
AI Applications in Engineering
终稿
Yemei Xia / Hunan University of Technology
Zhongsheng chen / Hunan University of Technology
High-speed rotating blades are widely used in modern industry, such as compressor blades, turbine blades, and fan blades. During working, rotating blades are often exposed to severe environments including strong vibrations, large centrifugal forces, thermal stresses, and so on. In this case, different kinds of damages often appear in rotating blades due to high or low cycle fatigues. Statistic data have shown that over 60% of the overall faults are caused by vibrations. Furthermore, more than 70% of blade faults suffer from vibrations. Thus, it is much necessary to identify blade damages as early as possible. By now, many studies have been done to investigate blade damage detection and prediction. The aim of this paper is to give a comprehensive review on existing blade damage detection and prediction. Firstly, common types of blade damages are summarized and the corresponding causes and consequences are analyzed. Then different measurement techniques of blade conditions are compared, the corresponding signal preprocessing and feature extraction algorithms are presented. Next, several classical machine learning-based blade detection and prediction methods are presented and compared. Also the state of the art of deep learning-based methods and their applications in blade detection and prediction is reviewed. Compared with classical machine learning-based methods, deep learning-based methods doesn’t need great efforts on designing sensitive features and can mine inherent relationships between output responses and blade damages. In the end, detailed prospects are discussed on future directions. This paper will provide a whole vision for future newcomers into this field.
重要日期

  • 会议日期

    10月21日

    2021

    至

    10月23日

    2021

  • 10月26日 2021

    注册截止日期

主办单位
Southeast University, China
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