基于GLCM-HOG和WOA-ELM的往复压缩机气阀故障诊断方法

气阀是往复压缩机在工作过程中极易发生故障的部件。从气阀时频图分析的角度，提出了一种基于GLCM-HOG和WOA-ELM的往复压缩机气阀故障诊断方法。首先，通过小波变换对各运行状态气阀的振动信号进行处理，生成时频图；用GLCM和HOG分别提取气阀时频图特征，融合形成GLCM-HOG特征。然后，利用WOA方法对ELM模型的输入层节点权值和隐藏层节点阈值进行优化，构建气阀故障诊断模型。最后，将GLCM特征和GLCM-HOG特征分别输入到WOA-ELM模型中，来证明本文所提方法的有效性和优越性，从而实现往复压缩机气阀故障的诊断。实验结果表明：与GLCM特征相比，构造的GLCM-HOG特征更能准确全面地反映气阀时频图特征；与ELM模型相比，WOA-ELM模型诊断气阀故障的准确率更高。

Fault diagnosis method based on GLCM-HOG and WOA-ELM for reciprocating compressor valve

The reciprocating compressor valves are prone to fail during operation. From the perspective of the valve time frequency images analysis, it is proposed that fault diagnosis method based on GLCM-HOG and WOA-ELM for reciprocating compressor valves. First, the vibration signal of each operating valve is processed by wavelet generation time frequency images. The GLCM and the HOG were used to extract the time frequency image features of the valve, and fused to form GLCM-HOG features. Then, the WOA is used to optimize the ELM model for input layer node weight and hidden layer node and the valve fault diagnosis model is constructed. Finally, the GLCM features and GLCM-HOG features are fed into the WOA-ELM model to demonstrate the effectiveness and superiority of the proposed method for the diagnosis of reciprocating compressor valve fault. The experimental results show that compared with the GLCM features, the constructed GLCM-HOG features can accurately and comprehensively reflect the valve time frequency image features. The WOA-ELM model diagnoses valve failure with higher accuracy.