基于无监督判别投影的滚动轴承故障诊断

摘要/Abstract

摘要： 针对滚动轴承故障样本不平衡和故障特征存在冗余性问题，提出了基于无监督判别投影（UDP）的滚动轴承故障诊断方法。该方法首先从时域和时频域提取多个特征参数，从而构造一个原始的高维特征集，随后运用UDP算法从该特征集中提取最敏感的低维流形特征，最后利用K-近邻分类器识别出滚动轴承的运行状态。将该方法分别应用于轴承故障类型和内圈故障严重性的识别，并与传统方法进行了比较，验证了该方法的可行性和优越性。

关键词: 故障诊断, 特征提取, 流形学习, 无监督判别投影

Abstract: Aiming at the imbalanced fault samples and redundant fault features of rolling bearings, a rolling bearing fault diagnosis method was proposed based on UDP. The method firstly extracted several feature parameters from time domain and time-frequency domain. Thus, the raw high-dimensional feature set was constructed. Subsequently, the most sensitive low-dimensional manifold features were extracted from the feature set by employing UDP algorithm. Finally, K-nearest neighbor classifier was utilized to recognize the operating conditions of rolling bearings. The method was applied to the identification of bearing fault categories and inner fault severities separately. Compared with the traditional methods, the feasibility and superiority were validated.

Key words: fault diagnosis, feature extraction, manifold learning, unsupervised discriminant projection(UDP)

中图分类号:

江丽, 郭顺生. 基于无监督判别投影的滚动轴承故障诊断[J]. 中国机械工程.

Jiang Li, Guo Shunsheng. Fault Diagnosis of Rolling Bearings Based on Unsupervised Discriminant Projection[J]. China Mechanical Engineering.

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http://www.cmemo.org.cn/CN/Y2016/V27/I16/2202

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