Reciprocating Compressor Valve Research Articles

Research and development of innovative bidirectional control plate valve for reciprocating compressor

Currently, the reciprocating compressor valve operates on a single working principle, where the valve structure relies on the combined force of its own elasticity and gas thrust to drive the movement of the valve disc, and continues to open and close in the way of impact. Due to the defects of the conventional plate valve, such as delayed opening, premature closing, and insufficient full opening time, in combination with the self-acting valve structure and constant volume cylinder where artificial adjustment of compressed air volume is not feasible. Therefore an innovative bidirectional control plate valve has been developed in this study, which employs a linear motor to regulate the valve disc and eliminates structural components such as springs, buffer discs, and valve lift guard. Throughout the entire opening and closing cycle, precise control of the valve disc enables swift operation with prolonged full open state, minimal resistance loss, and absence of impact, vibration or rebound. Its ideal movement mode can directly and accurately achieve stepless capacity regulation. The primary suction valve of the industrial 3LW-5-8 reciprocating compressor is taken as the research subject in this paper. Fluent software is utilized to design the structural parameters of an innovative plate valve, and its feasibility and effectiveness are validated through tests conducted on an automated multi-functional experiment table and an L-type reciprocating compressor. The results demonstrate that the new valve exhibits a 21.52% increase in effective flow area compared to the conventional plate valve, along with a 3.02% increase in exhaust volume and a potential reduction of 2.32% in power consumption when replaced one of the four. Moreover, the innovative bidirectional control plate valve demonstrates precise control over high-frequency motion of the valve disc, effectively addressing inherent limitations of traditional plate valves and exhibiting promising application prospects.

Research on fault diagnosis method of reciprocating compressor valve based on IVMD-CMS model

Research on fault diagnosis method of reciprocating compressor valve based on IVMD-CMS model

Analysis of Reciprocating Compressor Valve

Abstract: A compressor running at even moderate speeds requires for each valve to open and close over one million times a day. It follows that valve design must bear in mind that it needs to be highly reliable and operate efficiently even in adverse conditions, such as in applications where there are liquids and debris in the gas stream. The fundamental challenge in the design of a compressor valve lies in the opening and closing phase of the valve, where an effort is made to allow for the most desirable dynamic behavior as possible. For instance, lowering the pressure loss across the valve by increasing the flow area leads to a higher possibility of unsuitable dynamic behavior and thus decreases its lifetime.This thesis is devoted to a theoretical study of reciprocating compressor valve dynamics. The analysis of the influence of the valve parameters on its dynamic behavior is also present in this thesis. This study has been undertaken in the compressor valve generally a flap valve and their CAD modelling with analysis of compressor valve equivalent stress, temperature and heat flux.

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve.

In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.

Early warning of reciprocating compressor valve fault based on deep learning network and multi-source information fusion

An early warning method of compressor valve fault based on multi-parameter signals (vibration, pressure, and temperature) is presented in this work. Due to the complexity working condition, the run data of the compressor are of problems like noise and feature aliasing, which makes it difficult to extract useful features and find out the running law from the original signals. In this work, an improved deep learning network Multi-Level Fusion long short-term memory based on Component Evaluating Empirical Mode Decomposition and Fuzzy C-Means (CEEMD-FCM & MLF-LSTM) for parameter prediction of reciprocating compressor and an information fusion strategy is proposed for compressor valve fault warning. The CEEMD-FCM & MLF-LSTM network consists of data processing block, information learning block, and prediction output block, which is mainly responsible for parameter prediction. In the data processing block, the CEEMD-FCM algorithm is used for parameter decomposition, noise removal, and fuzzy mode (FM) reconstruction, which generates the input for the information learning block to ensure the predicting accuracy and reduce model complexity. MLF-LSTM is constructed to predict the parameter in the future by learning the temporal and spatial characteristics of FMs of the run data. Then, an early warning strategy for compressor valve fault based on multi-source information fusion is developed. Experimental results have verified that the proposed CEEMD-FCM & MLF-LSTM model and early warning strategy could realize early warning of compressor valve fault effectively.

Early Fault Diagnosis Model Design of Reciprocating Compressor Valve Based on Multiclass Support Vector Machine and Decision Tree

According to the character of frequent fault occurrence, difficult diagnosis of large reciprocating compressor valves, an early fault diagnosis model of reciprocating compressor valve based on multiclass support vector machine and decision tree is designed. A series of simulation experiments of the suction valve and exhaust valve on a large-scale reciprocating compressor experimental bench are made and the valve fault principle is analyzed. Using the advantages of fast and efficient decision tree classification and the prominent characteristics of support vector machine in small sample binary classification, a multivariate classification and recognition model is constructed. The typical characteristic parameters of gearbox vibration signal are extracted as the fault feature vector training model under different fault conditions, and the samples are tested. The experimental results show that the recognition effect of this method is significantly better than that of the neural network method in the case of small samples, and the recognition efficiency is improved more than that of the conventional multivariate support vector machine method which can be effectively applied to reciprocating compressor valve fault diagnosis.

Research on transient dynamic behavior and stress of reciprocating compressor valve under air volume regulation

By establishing the motion equation of the air valve under the condition of air volume adjustment, the motion law of the air valve in the process of expansion, compression, suction and exhaust of the reciprocating compressor under different loads is calculated, and the reciprocating compressor with stepless air volume adjustment is used. The compressor unit verifies the calculation results. The maximum stress of the valve plate under different loads is analyzed, and the results show that in the ideal state where the fork pressure disappears instantaneously, the adjustment method of the partial stroke pressure opening suction valve has a greater influence on the stress concentration of the valve plate than the normal condition; in the partial stroke pressure opening suction valve adjustment method, the free retraction speed and stress of the valve plate will increase as the compressor load decreases.

A p−V Diagram Based Fault Identification for Compressor Valve by Means of Linear Discrimination Analysis

The pressure-volume diagram (p−V diagram) is an established method for analyzing the thermodynamic process in the cylinder of a reciprocating compressor as well as the fault of its core components including valves. The failure of suction/discharge valves is the most common cause of unscheduled shutdowns, and undetected failure may lead to catastrophic accidents. Although researchers have investigated fault classification by various estimation techniques and case studies, few have looked deeper into the barriers and pathways to realize the level determination of faults. The initial stage of valve failure is characterized in the form of mild leakage; if this is identified at this period, more serious accidents can be prevented. This study proposes a fault diagnosis and severity estimation method of the reciprocating compressor valve by virtue of features extracted from the p−V diagram. Four-dimensional characteristic variables consisting of the pressure ratio, process angle coefficient, area coefficient, and process index coefficient are extracted from the p−V diagram. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were applied to establish the diagnostic model, where PCA realizes feature amplification and projection, then LDA implements feature dimensionality reduction and failure prediction. The method was validated by the diagnosis of various levels of severity of valve leakage in a reciprocating compressor, and further, applied in the diagnosis of two actual faults: Mild leakage caused by the cracked valve plate in a reciprocating compressor, and serious leakage caused by the deformed valve in a hydraulically driven piston compressor for a hydrogen refueling station (HRS).

Fault Feature Extraction Method of Reciprocating Compressor Valve Based on SPA-MF

Aiming at the problem that the traditional multifractal detrended fluctuation analysis (MFDFA) using the least squares method to fit the trend term is prone to overfitting and takes a long time, this paper proposes a new non-stationary signal analysis method—smoothed prior analysis multifractal (SPA-MF).Firstly, the time sequence data is adaptively decomposed by smooth prior analysis (SPA) to eliminate the local trends of sequence data at different scales, and then the multifractal analysis is performed on the detrended data obtained by the decomposition. At the same time, the sparrow search algorithm (SSA) is used to optimize the parameter of the SPA, so as to eliminate the trend item data more accurately. Through the simulation signal which composed of the BMS signal and noise signal, the feasibility of SPA-MF for feature extraction is proved. Finally, SPA-MF is applied to extract the features of the reciprocating compressor valve vibration signal, and the extracted reciprocating compressor valve features are input into support vector machine (SVM) for classification and recognition. Through the analysis of the experimental results, it can be seen that the recognition rate of the valve features obtained by the traditional MFDFA method is only 87.5%, and the recognition rate of the SPA-MF method proposed in this paper reaches 96.87%, and the time spent on feature extraction using SPA-MF is only about 36% of that of MFDFA method, which proves the SPA-MF method is a feature extraction method with high accuracy and effectiveness.

Application based comparison of statistical versus deep learning approaches to reciprocating compressor valve condition monitoring

This paper presents a vibration-based condition monitoring approach for early assessment of valve wear in an industrial reciprocating compressor. Valve seat wear is a common fault mode that is caused by repeated impact and accelerated by chatter. Seeded faults consistent with valve seat wear are installed on the head-side discharge valves of a Dresser-Rand ESH-1 industrial reciprocating compressor. Due to the cyclostationary nature of these units a time-frequency analysis is employed where targeted crank angle positions can isolate externally mounted, non-invasive, vibration measurements. A region-of-interest (ROI) is then extracted from the time-frequency analysis and used to train a suitably sized convolutional neural network (CNN). The proposed deep learning method is then compared against a similarly trained discriminant classifier using the same ROIs where features are extracted using texture and shape image statistics. Both methods achieve > 90% success with the CNN classification strategy nearing a perfect result.

Failure Mode Analysis of Reciprocating Compressor Valve Stability

Failure Mode Analysis of Reciprocating Compressor Valve Stability

Fault Diagnosis of Reciprocating Compressor Valve Based on Transfer Learning Convolutional Neural Network

Reciprocating compressors play a vital role in oil, natural gas, and general industrial processes. Their safe and stable operation directly affects the healthy development of the enterprise economy. Since the valve failure accounts for 60% of the total failures when the reciprocating compressor fails, it is of great significance to quickly find and diagnose the failure type of the valve for the fault diagnosis of the reciprocating compressor. At present, reciprocating compressor valve fault diagnosis based on deep neural networks requires sufficient labeled data for training, but valve in real-case reciprocating compressor (VRRC) does not have enough labeled data to train a reliable model. Fortunately, the data of valve in laboratory reciprocating compressor (VLRC) contains relevant fault diagnosis knowledge. Therefore, inspired by the idea of transfer learning, a fault diagnosis method for reciprocating compressor valves based on transfer learning convolutional neural network (TCNN) is proposed. This method uses convolutional neural network (CNN) to extract the transferable features of gas temperature and pressure data from VLRC and VRRC and establish pseudolabels for VRRC unlabeled data. Three regularization terms, the maximum mean discrepancy (MMD) of the transferable features of VLRC and VRRC data, the error between the VLRC sample label prediction and the actual label, and the error between the VRRC sample label prediction and the pseudolabel, are proposed. Their weighted sum is used as an objective function to train the model, thereby reducing the distribution difference of domain feature transfer and increasing the distance between learning feature classes. Experimental results show that this method uses VLRC data to identify the health status of VRRC, and the fault recognition rate can reach 98.32%. Compared with existing methods, this method has higher diagnostic accuracy, which proves the effectiveness of this method.

Fault Detection of Reciprocating Compressor Valve Based on One-Dimensional Convolutional Neural Network

Reciprocating compressors are important equipment in oil and gas industries which closely relate with the healthy development of the enterprise. It is essential to detect the valve fault because valve failures account for 60% in total failures. For this field, an artificial neural network (ANN) is widely used, but a complex network is not suitable for its low accuracy and easy overfitting. This paper proposes a fault diagnosis model of a reciprocating compressor valve based on a one-dimensional convolutional neural network (1DCNN). This method takes the differential pressure and differential temperature of each compressor stage as the input of 1DCNN, using the characteristics of the CNN to extract the features and finally using Softmax to classify the fault. In order to verify this method, it is compared with LM-BP, RBF, and BP neural networks. The results show that the fault recognition rate of 1DCNN reaches 100%, which proves the effectiveness and feasibility of the proposed method.

Reciprocating compressor valve damage estimation under varying speeds through the acoustic emission technique

Purpose The purpose of this paper is to examine the effect of reciprocating compressor speeds and valve conditions on the roor-mean-square (RMS) value of burst acoustic emission (AE) signals associated with the physical motion of valves. The study attempts to explore the potential of AE signal in the estimation of valve damage under varying compressor speeds. Design/methodology/approach This study involves the acquisition of AE signal, valve flow rate, pressure and temperature at the suction valve of an air compressor with speed varrying from 450 to 800 rpm. The AE signals correspond to one compressor cycle obtained from two simulated valve damage conditions, namely, the single leak and double leak conditions are compared to those of the normal valve plate. To examine the effects of valve conditions and speeds on AE RMS values, two-way analysis of variance (ANOVA) is conducted. Finally, regression analysis is performed to investigate the relationship of AE RMS with the speed and valve flow rate for different valve conditions. Findings The results showed that AE RMS values computed from suction valve opening (SVO), suction valve closing (SVC) and discharge valve opening (DVO) events are significantly affected by both valve conditions and speeds. The AE RMS value computed from SVO event showed high linear correlation with speed compared to SVC and DVO events for all valve damage conditions. As this study is conducted at a compressor running at freeload, increasing speed of compressor also results in the increment of flow rate. Thus, the valve flow rate can also be empirically derived from the AE RMS value through the regression method, enabling a better estimation of valve damages. Research limitations/implications The experimental test rig of this study is confined to a small pressure ratio range of 1.38–2.03 (free-loading condition). Besides, the air compressor is assumed to be operated at a constant speed. Originality/value This study employed the statistical methods namely the ANOVA and regression analysis for valve damage estimation at varying compressor speeds. It can enable a plant personnel to make a better prediction on the loss of compressor efficiency and help them to justify the time for valve replacement in future.

Fault feature extraction method based on EWT-SMF and MF-DFA for valve fault of reciprocating compressor

According to the nonlinearity and nonstationarity characteristics of reciprocating compressor vibration signal, a fault feature extraction method of reciprocating compressor based on the empirical wavelet transform (EWT) and state-adaptive morphological filtering (SMF) is proposed. Firstly, an adaptive empirical wavelet transform was used to divide the Fourier spectrum by constructing a scale-space curve, and an appropriate orthogonal wavelet filter bank was constructed to extract the AM-FM component with a tightly-supported Fourier spectrum. Then according to the impact characteristic of the reciprocating compressor vibration signal, the morphological structural elements were constructed with the characteristics of the signal to perform state-adaptive morphological filtering on the partitioned modal functions. Finally, the MF-DFA method of the modal function was quantitatively analyzed and the fault identification was performed. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve.

An Intelligent Fault Diagnosis Method for Reciprocating Compressors Based on LMD and SDAE.

The effective fault diagnosis in the prognostic and health management of reciprocating compressors has been a research hotspot for a long time. The vibration signal of reciprocating compressors is nonlinear and non-stationary. However, the traditional methods applied to processing such signals have three issues, including separating the useful frequency bands from overlapped signals, extracting fault features with strong subjectivity, and processing the massive data with limited learning abilities. To address the above issues, this paper, which is based on the idea of deep learning, proposed an intelligent fault diagnosis method combining Local Mean Decomposition (LMD) and the Stack Denoising Autoencoder (SDAE). The vibration signal is firstly decomposed by LMD and reconstructed based on the cross-correlation criterion. The virtual noise channel is constructed to reduce the noise of the vibration signal. Then, the de-noised signal is input into the trained SDAE model to learn the fault features adaptively. Finally, the conditions of the reciprocating compressor valve are classified by the proposed method. The results show that classification accuracy is 92.72% under the condition of a low signal-noise ratio, which is 5 percentage points higher than that of the traditional methods. This shows the effectiveness and robustness of the proposed method.

Observations of changes in acoustic emission parameters for varying corrosion defect in reciprocating compressor valves

Abstract Acoustic Emission (AE) technology is probably one of the most recent entries in the field of machinery condition monitoring. This paper investigates the application of AE parameters for valve faults detection in reciprocating compressor. The defective valves were designed by emulating the actual valve corrosion with varying sizes such that defects could be applied onto the reciprocating compressor. A set of experiments was performed to acquire the AE signal. The primary source of AE signal was verified using waveform analysis. The AE parameters versus different operational and valve condition were illustrated individually. In addition, the significance of the change and sensitivity of AE parameters versus different experimental conditions was verified using MANOVA and coefficient of variance analysis. It is concluded that the AE signal parameters can be used to detect the valve faults in the reciprocating compressor with the consideration of the variation in the AE parameters sensitivity.

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

According to the nonlinearity, nonstationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, a fault diagnosis method of reciprocating compressor valve based on the modified multiscale entropy (MMSE) and global distance evaluation (GDE) is proposed. Firstly, to eliminate the noise interference, the VMD method with superior anti-interference performance was utilized to a strong non-stationarity vibration signal for all fault states. To overcome the obstacle of the conventional multiscale entropy (MSE) may yield undefined entropy or an imprecise estimation of entropy by the large scale factor, the MMSE method provided for moving-average procedure by replacing mean-average coarse-grained procedure was developed to the vibration signals, and then the GDE method of overall parameter selection was introduced to evaluate the extracted MMSE and select the optimal sensitivity scale feature. Finally, binary tree of Support Vector Machine (BTSVM), suitable for small sample classification, was selected as the classifier to identify the reciprocating compressor valve fault type. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve.

Abrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failure

One of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM), which is founded on the principles of diagnostics, and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. The SOM technique is employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques.

A new feature extraction approach using improved symbolic aggregate approximation for machinery intelligent diagnosis

Feature extraction from vibration signals is considerably significant for condition monitoring and fault diagnosis. The Symbolic Aggregate approXimation (SAX) technique essentially transforming a real-valued time series into a symbol sequence, has been proven as a potential tool of feature extraction for machinery intelligent diagnosis. However, challenge still exists that the SAX cannot fulfill feature extraction tasks well since it is carried out only on the basis of mean value in time domain. To overcome this limitation, an improved SAX (ISAX) is proposed in this paper. This new method substitutes the feature of mean value in time domain with multiple features extracted from time, frequency and time-frequency domains in order to obtain comprehensive fault information. With the ISAX transformation, a vibration signal can be transformed into various symbol sequences according to the multi-domain features. Next the Shannon entropy technique is conducted on a symbol sequence to capture sequential patterns in local signals and then the Shannon entropy value is used as the eigenvalue of the symbol sequence. Various eigenvalues are obtained to describe a vibration signal from different perspectives, which leads to a better feature extraction. These eigenvalues are then fed into the Kernel Principal Component Analysis (KPCA) to reduce dimensions and extract principal features for classification tasks. Compared with SAX, the most significant advantage of ISAX is extracting comprehensive signal characteristics from multi-domain. Moreover, the ISAX captures fault information better considering the local fault patterns. The effectiveness and superiority of ISAX were validated by experimental studies using the fault signals of rolling bearings and reciprocating compressor valves with remarkably high classification rates.