预言
降级(电信)
包络线(雷达)
支持向量机
西格玛
算法
路径(计算)
滚动轴承
计算机科学
工程类
可靠性工程
人工智能
振动
电子工程
物理
电信
雷达
量子力学
程序设计语言
作者
Yubo Shao,Xiao He,Bangcheng Zhang,Chao Cheng,Xiaopeng Xi
出处
期刊:IEEE Transactions on Reliability
[Institute of Electrical and Electronics Engineers]
日期:2023-03-17
卷期号:73 (1): 317-327
被引量:1
标识
DOI:10.1109/tr.2023.3252605
摘要
The degradation starting time is an important variable affecting the accuracy of degradation path prediction, but little work has been considered in existing studies. This article investigates the problem of predicting the performance of rolling element bearings based on early degradation analysis. Based on an improved dual linear structural support vector machine with envelope spectrum algorithm and $\mu +4\sigma$ criteria, a new health indicator is proposed to detect the degradation starting time. As well the detected time is sensitive to early anomalies. In addition, according to the degradation starting time, a convolutional neural network prediction model is established to predict the degradation path. Experiments show the effectiveness and superiority of the proposed method.
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