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.