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Sparsity-based algorithm for detecting faults in rotating machines

机译:基于稀疏性的旋转机械故障检测算法

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摘要

This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.
机译:本文致力于检测振动信号中的周期性瞬变,以检测旋转机械中的故障。为此,我们提出了一种在噪声中估计周期稀疏信号的方法。该方法基于凸优化问题的表述。为此,给出了一种快速迭代算法。模拟信号被公式化以验证所提出的用于周期性特征提取的方法的性能。通过RMSE值和接收机工作特性(ROC)曲线,将比较方法的检测性能与建议方法的检测性能进行了比较。最后,将该方法应用于机车轴承的单故障诊断和电机轴承的复合故障诊断。处理结果表明,该方法可以有效地检测和提取轴承外圈和内圈缺陷的有用特征。

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