基于时频图像GLCM-HOG特征的往复压缩机故障诊断

往复压缩机的故障诊断技术能够为工业生产提供有效保障,针对传统方法诊断准确率不高的问题,提出了一种基于振动信号时频图像灰度共生矩阵-方向梯度直方图(GLCM-HOG)特征融合的往复压缩机故障诊断方法.首先,采用小波变换的方法处理往复压缩机的振动信号,生成时频图像;其次,利用灰度共生矩阵(GLCM)和方向梯度直方图(HOG)的方法提取时频图像特征,融合构建GLCM-HOG特征;最后,将融合特征输入支持向量机(SVM)进行分类,以判别往复压缩机的运行状态.实验结果表明,所提方法对设备的状态识别准确率可以达到92.33％,能够实现往复压缩机的准确诊断.

Fault diagnosis for reciprocating compressor based on GLCM-HOG features of time-frequency image

The fault diagnosis technology of reciprocating compressor can provide an effective guarantee for industrial production. Due to the low recognition accuracy of the traditional method, a fault diagnosis method of reciprocating compressor based on the time-frequency image GLCM-HOG features fusion of vibration signal is proposed. Firstly, the vibration signals of reciprocating compressor are processed by wavelet transform to generate time-frequency images. Secondly, the gray co-occurrence matrix (GLCM) features and directional gradient histogram (HOG) features are respectively extracted from the time-frequency images and fused. Finally, the GLCM-HOG features are input into support vector machine (SVM) to determine the state of the reciprocating compressor. The experimental results show that the accuracy rate can reach 92.33%, and the proposed method can accurately realize fault diagnosis for reciprocating compressor.