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国外文献 > 1.轴承 > 5. 监测与诊断 > 寿命与维护

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标 题： A Data-Driven Approach Based Health Indicator for Remaining Useful Life Estimation of Bearings
基于数据驱动方法的轴承剩余使用寿命健康指标
作 者： Mufutau AKURUYEJO
Samuel KOWONTAN
Jaouher BEN ALI
关键词： Vibration analysis  [振动分析]
Prognostic and health management  [ 预后与健康管理]
Remaining useful life  [ 剩余使用寿命]
Rolling element bearing  [ 滚动轴承]
Support vector regression  [ 支持向量回归]
出版年： 2017
会议名： 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA 2017)
第18届自动控制与计算机工程科学与技术国际会议（STA 2017）
届 次： 18th
摘 要： Accurate state of health monitoring remains an important challenge in the modern industry. Failures in rolling element bearings account for almost half of all machine breakdowns. Consequently, the monitoring of this component is very required for arranging repair work and reducing unexpected production outage. In this sense, this paper presents a new data-driven approach for bearing remaining useful life estimation. Considering the nonlinear and non-stationary bearing vibration signals, the empirical mode decomposition method was used as a pre-processing step for feature extraction. Then, the principal component analysis algorithm was used for feature reduction. Finally, the support vector regression algorithm is proposed to estimate the remaining useful life of the tested bearing. In fact, the proposed method presents a new health indicator that defines accurately the state of health of bearings. Experimental results based on real bearing vibration signals under real operating conditions confirm the reliability and the effectiveness of the proposed method.

精确的健康状态监测仍是现代工业中的一个重要挑战。滚动轴承故障约占所有设备停机事故的一半，因此对这一部件进行有效监测对于安排维修工作、减少突发性生产中断至关重要。本文提出一种基于数据的新方法，用于估算轴承剩余使用寿命。针对轴承振动信号的非线性和非平稳特性，采用经验模分解法作为预处理步骤以提取特征，随后利用主成分分析算法进行特征降维。最后，提出支持向量回归算法来估算被测轴承的剩余使用寿命。事实上，该方法提供了一种新的健康指标，能够准确反映轴承的健康状态。基于真实运行条件下实际轴承振动信号的实验结果证实了该方法的可靠性和有效性。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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