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首页> 外文期刊>Journal of vibration and control: JVC >A complementary approach for fault diagnosis of rolling bearing using canonical variate analysis based short-time energy feature
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A complementary approach for fault diagnosis of rolling bearing using canonical variate analysis based short-time energy feature

机译：基于Cononical变化的短时能量特征的滚动轴承故障诊断互补方法

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摘要

Signal decomposition is a meaningful and effective methodology which is widely used for fault diagnosis. Mode/feature selection is an inevitable topic for fault diagnosis of rolling bearing due to over-decomposition. In practical application, the selection of sensitive modes is a challenging task, so many valuable works have been performed to cope with it. However, the published works lack an effective approach to acquire few meaningful modes by avoiding the complicated mode selection procedures, prior to feature extraction. Moreover, selection of the modes of interest fails to take the residual part into account, which makes the diagnosis result sensitive to the number of modes/features retained. This paper proposes a complementary approach to extract fault features and avoid the selection of single mode of interest, which employs canonical variate analysis to convert the original variable into two complementary spaces; canonical variate space; and residual space. Then the complementary statistical indicators Hotelling T-2 statistic and Q statistic are used to provide important information about the conditions of the rolling bearing. Subsequently, a new feature index, complementary short-time energy extracted from the two statistics are used as fault features which are given as an input to a classifier such as a support vector machine. Two data sets collected from different test rigs are used for demonstration of the proposed work. The experimental result shows that the troublesome feature/mode selection issue is avoided, and the diagnosis result is not sensitive to the number of canonical variate retained. Besides, the proposed approach can identify various working conditions of rolling bearing accurately, which is simple and effective for fault diagnosis of rolling bearing, compared with the existing methods.
机译：信号分解是一种有意义且有效的方法，其广泛用于故障诊断。模式/特征选择是由于过度分解引起的滚动轴承故障诊断的必然主题。在实际应用中，敏感模式的选择是一个具有挑战性的任务，所以已经进行了许多有价值的作品来应对它。然而，公布的作品缺乏有效的方法，通过在特征提取之前避免复杂的模式选择程序来获取几种有意义的模式。此外，选择感兴趣模式未能考虑剩余部分，这使得诊断结果对保留的模式/特征的数量敏感。本文提出了一种提取故障特征的互补方法，避免选择单一兴趣模式，这采用规范变化分析将原始变量转换为两个互补空间;规范变化空间;和剩余空间。然后，互补的统计指标热控到T-2统计和Q统计数据用于提供有关滚动轴承条件的重要信息。随后，从两个统计数据中提取的新特征索引，从两个统计中提取的互补短时能量用作故障特征，其作为输入到诸如支持向量机的分类器的输入。从不同的试验台收集的两个数据集用于演示所提出的工作。实验结果表明，避免了麻烦的特征/模式选择问题，并且诊断结果对所保留的规范变化的数量不敏感。此外，与现有方法相比，所提出的方法可以确定地识别滚动轴承的各种工作条件，这对于滚动轴承的故障诊断简单有效。

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