基于分离矩阵的机械故障盲信息提取

Hao Li, Yifan Tan, Y. Pu
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引用次数: 1

摘要

盲信号处理是一种有效的机械振动信号特征提取方法。然而,由于噪声的破坏,独立的源信号不能总是准确地从采集的传感器观测中恢复或分离出来。这样,从源信号中提取的特征信息就不能很自然地代表被检测机械设备的机器状态。一般在盲信号处理中,分离矩阵所包含的信息量与分离后的源信号所包含的信息量相当。通过奇异值分解(SVD)方法,可以直接对分离矩阵进行处理,提取其奇异值作为有用的特征信息。为此,提出了一种盲信息提取方法,提取分离矩阵的奇异值作为被检测机器的期望特征信息。齿轮泵的实验结果表明,该方法可用于机械设备的特征提取。
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Blind Information Extraction of Machine Faults Based on Separating Matrix
Blind signal processing is an effective feature extraction method for mechanical vibration signals. However due to noise corruption, independent source signals can't always be accurately recovered or separated from the acquired sensor observations. Then feature information extracted from source signals can't naturally represent machine states of the detected mechanical equipment. Generally in blind signal processing, the separating matrix may contain as much information contents as the separated source signals. The separating matrix can directly be processed to extract its singular values as useful feature information by the singular value decomposition (SVD) method. Thus a blind information extraction method was proposed to extract singular values of separating matrix as the desired feature information of the detected machine. The experimental results of gear pump indicate that this method can be applied to feature extraction of mechanical equipment.
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