一种基于特征聚类和评价的轴承寿命预测新方法

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摘要在预测轴承寿命时，使提取的特征和剩余寿命保持高相关性，并使不同的特征之间保持低相关性，是有利于提升轴承寿命预测精度的。为解决单一的特征评价方法对后者考虑不足的问题，提出了一种基于相关性改进Kmeans聚类算法(Correlation-based improved Kmeans cluster algorithm， Corr-Kmeans)和初始聚类中心确定方法，并与特征评价相结合，最终提出一种基于特征聚类和评价的轴承寿命预测新方法。首先利用卷积自编码对频域信息提取初始特征，用Corr-Kmeans对初始特征按相关性进行聚类，使得聚类后的特征类内相关性高，而类间相关性低；其次，使用相关性、单调性和鲁棒性3个指标来综合评价每一类中的特征，按照筛选阈值将得分较高的特征从每一类中分别选出，组成用于训练与预测的特征子集；最后采用LSTM(Long Short-Term Memory, LSTM)网络对轴承剩余寿命进行预测。在一个轴承加速寿命实验的公开数据集上使用留一法进行验证，利用对比实验证明了所提方法在预测轴承剩余寿命上的有效性。

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	李海浪
	邹益胜
	曾大懿
	刘永志
	赵市教
	宋小欣

关键词 ：轴承, 寿命预测, Corr-Kmeans, 聚类, 特征评价

Abstract：When predicting the bearing life, it is beneficial to improve the accuracy of bearing life prediction by keeping high correlation between extracted features and remaining life and low correlation between different features. In order to solve the problem that the latter is insufficiently considered by a single feature evaluation method, an improved Kmeans cluster algorithm (Corr-Kmeans) based on Correlation and the initial cluster center determination method are proposed, which are combined with the feature evaluation, and finally a new method for bearing life prediction based on feature clustering and evaluation is proposed. First, the convolution self-coding is used to extract the initial features of the frequency domain information, and corr-Kmeans is used to cluster the initial features according to correlation, so that the correlation within the feature class after clustering is high, while the correlation between the classes is low. Secondly, three indicators of relevance, monotonicity and robustness were used to comprehensively evaluate the features in each category, and the features with high scores were selected from each category according to the screening threshold to form the feature subset for training and prediction. Finally, LSTM(Long Short-Term Memory) network is used to predict the remaining life of the bearing. By using the retention method on the open data set of a bearing accelerated life experiment, the effectiveness of the proposed method in predicting the remaining life of the bearing is proved by the comparative experiment.

Key words： bearing life prediction Corr-Kmeans Clustering feature evaluation

收稿日期: 2020-11-02 出版日期: 2022-03-15

引用本文:

李海浪,邹益胜,曾大懿,刘永志,赵市教,宋小欣. 一种基于特征聚类和评价的轴承寿命预测新方法[J]. 振动与冲击, 2022, 41(5): 141-150.
LI Hailang, ZOU Yisheng, ZENG Dayi, LIU Yongzhi, ZHAO Shijiao, SONG Xiaoxin. A new method of bearing life prediction based on feature clustering and evaluation. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(5): 141-150.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I5/141

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