Local Mean Decomposition Based on Rational Hermite Interpolation and Its Application for Fault Diagnosis of Reciprocating Compressor

Zhao, Haiyang

doi:10.3901/jme.2015.01.083

Cited by 11 publications

(7 citation statements)

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“…Feature map X (4) Feature map X (3) Feature map X (2) Feature map X (1) Flatten In order to further verify the diagnostic performance of the 1DCNN model, the proportion of training samples and test samples in the data is adjusted, and the proportions of training samples are set to 70%, 50%, and 30%. e best three sets of results are selected from the training results of these three sample sets, and the loss curve and accuracy curve of the three sets of training results are compared, as shown in Figure 8.…”

Section: Resultsmentioning

confidence: 99%

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

Guo

Zhang

Wang

et al. 2020

Mathematical Problems in Engineering

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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.

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Section: Resultsmentioning

confidence: 99%

“…It can compress a variety of gases and has a wide range of applications, high compression efficiency, and stable working pressure. However, due to its complicated structure and many vulnerable parts, once a failure cannot be detected and eliminated in time, it will bring huge losses to the enterprise [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Zhang

Wang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Zhao et al [36] developed an improved LMD method to restrain the end effects of LMD and applied it to classify faults of a hoister. Zhao et al [120] employed rational Hermite interpolation to generate the envelope-line and demonstrated the advantages of the improved LMD in fault diagnosis of reciprocating compressors. Zhao et al [49] proposed a compound interpolation envelope LMD by using a novel envelope construction method called monotonic piecewise cubic Hermite interpolation.…”

Section: Applications Using Improved Lmd Methodsmentioning

confidence: 99%

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

Liu

et al. 2019

Journal of Systems Engineering and Electronics

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Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given. The review is divided into four parts: fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, respectively. We give a summary of this review and some future potential topics at the end.

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“…Reciprocating compressors are important equipment in the oil and gas industry. Once failures cannot be detected and eliminated in time, they will cause huge losses to enterprises [1][2][3][4][5][6][7]. e valve is an important part of the reciprocating compressor.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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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.

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Local Mean Decomposition Based on Rational Hermite Interpolation and Its Application for Fault Diagnosis of Reciprocating Compressor

Cited by 11 publications

References 0 publications

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

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

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

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

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