奇异值分解与深度学习在轴承故障诊断中的应用

Application of Singular Value Decomposition and Deep Learning in Bearing Fault Diagnosis

摘要: 滚动轴承作为机械设施不可或缺的零部件，为了避免不堪设想的后果我们应该及时发现故障。为了解决这一问题，本文提出由奇异值分解(SVD)对波形进行特征提取，并将其提取的特征作为深度信念网络(DBN)的输入并进行故障诊断。该方法先对原始输入波形信号进行采样、重构和奇异值分解，然后将分解后的值视为特征，将其输入模型进行诊断。实验结果表明：经过多次实验后，与支持向量机(SVM)方法比较，本文提出方法的诊断故障准确率均值为98.4%，方差为0.42，诊断速度为0.3秒，而现有SVM诊断方法的诊断准确率均值为94.7%，方差为0.50，诊断速度为0.6秒。说明本文方法有很好的精确性、稳定性和快速性，与传统方法比较该方法优势在于精确性有了进一步提高。

Abstract: Rolling bearings are important parts of mechanical equipment, if not timely detection of failure will cause significant losses. In order to solve this problem, this paper proposes the feature extraction of the waveform by singular value decomposition (SVD), and input the extracted feature into the deep belief network (DBN) for fault diagnosis. Firstly, the input signal is reconstructed and is decomposed by singular value, and then the singular value is taken as the characteristic, which is taken as the input of the deep belief network (DBN) for fault diagnosed. Experiments show: In many experiments, comparison with existing support vector machine (SVM) fault diagnosis method, the accuracy of the proposed method is 98.4%, the variance is 0.42, the diagnostic speed is 0.3 seconds, and the diagnostic accuracy of the existing SVM diagnostic method is 94.7%, the variance is 0.50, and the diagnostic speed is 0.6 second. The method proposed has good accuracy, stability and fastness.