# An Automatic Fault Diagnosis Method for the Reciprocating Compressor Based on HMT and ANN
## 1 Introduction  
Reciprocating compressors (RCs) are extensively used in chemical and petroleum industries, such as chemical fertilizer synthesis process, natural gas transportation, etc., [1]. To ensure the compressor reliability and performance, the fault detection based on automatic condition monitoring systems for the compressor is necessary [2]. In fact, benefiting from the development of online monitoring technique, the automatic monitoring system for RCs tends to be mature [3]. Additionally, there are an army of physical parameters employed for automatic monitoring, for instance, vibration, temperature, piston rod position as well as pressure, etc. However, the automatic failure detection technologies of RCs are still progressing. An overview regarding various monitoring parameters of RCs has been made in [4]. It states that more than 50% of literature focused on vibration signal. Such as [5; 6; 7], Nevertheless, pressure-volume (\(p\)-\(V\)) diagram of a cylinder is also a significant parameter in compressor monitoring, which stems from crankshaft position and dynamic pressure in cylinder. Real time \(p\)-\(V\) diagrams can indicate the continuous situation in the RC cylinder effectively. By observing the shape change of the \(p\)-\(V\) diagram, various faults of the RC can be recognized [8]. However, the research about fault diagnosis based on \(p\)-\(V\) diagrams is still insufficient. Therefore, we choose a \(p\)-\(V\) diagram as the monitoring parameter in this study. The paring of the shape of the \(p\)-\(V\) diagram and failure type is usually carried out artificially in engineering, which often relies on a great deal of engineers' experience. To make the fault detection based on the \(p\)-\(V\) diagram automatic, some researchers havedone related studies. [PERSON] et al. [PERSON] et al. (2018) used 2D-Curvelet transform and nonlinear principal component analysis (NLPCA) as feature extraction tools first, then support vector machine (SVM) was adopted to classification. [PERSON] et al. [PERSON] et al. (2018) extracted seven invariant moments of the \(p\)-\(V\) diagram, then applied a SVM classifier to recognize five kinds of working conditions of the valve. In [PERSON] et al. (2018); [PERSON] et al. (2018), the gradient of the expansion curve in logarithmic coordinate and pressure differential between suction and discharge were used as features to train a SVM classifier which aims to discriminate faultless and leaking valve situations. In [PERSON] et al. (2018); [PERSON] et al. (2018), a dynamic pressure tensor was installed behind the discharge valve to measuring the pressure wave in the discharge cavity. First, a fast Fourier transform (FFT) was adopted to the raw data with windowing. Then, the FFT values were binned into some bins and a coordinate transformation was conducted to reduce the feature dimension. Finally, a Bayesian network was used to do classification. Inspired by [PERSON] et al. (2018); [PERSON] et al. (2018), the article intends to simulate the observation process by utilizing digital image processing (DIP) technique. Additionally, to catch the fault features accurately, two different DIP technologies were performed to extract the features of sectionalized \(p\)-\(V\) curves. Hit or miss transform was applied to extract two comprehensive gradients of the expansion and compression section and horizontal projection transform was adopted to extract six image features from discharge section. However, different from [PERSON] et al. (2018); [PERSON] et al. (2018), the features in this article have their physical meaning, i.e., gradients of the expansion and compression curves are closely related to the leakage and heat transfer in these two processes, and image features extracted by horizontal projection transform can reflect pressure fluctuations in suction and discharge curves. Although a constant gradient of the expansion curve obtained in logarithmic coordinate in [PERSON] et al. (2018); [PERSON] et al.