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

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fu-Yan Guo ◽  
Yan-Chao Zhang ◽  
Yue Wang ◽  
Pei-Jun Ren ◽  
Ping Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Recognition Rate ◽  
Vital Role ◽  
Stable Operation ◽  
Reciprocating Compressor ◽  
Gas Temperature ◽  
Label Prediction

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.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Fu-yan Guo ◽  
Yan-chao Zhang ◽  
Yue Wang ◽  
Ping Wang ◽  
Pei-jun Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Oil And Gas ◽  
Recognition Rate ◽  
Reciprocating Compressor ◽  
One Dimensional ◽  
Healthy Development ◽  
Fault Recognition ◽  
Diagnosis Model ◽  
Artificial Neural Network Ann

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.

scholarly journals Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989721 ◽  
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Qiang Fu ◽  
Xiaomei Ni
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Rolling Bearing ◽  
Operating Conditions ◽  
Feature Representation ◽  
Target Domain ◽  
Rolling Bearings ◽  
Variable Operating Conditions

Rolling bearings are the vital components of rotary machines. The collected data of rolling bearing have strong noise interference, massive unlabeled samples, and different fault features. Thus, a deep transfer learning method is proposed for rolling bearings fault diagnosis under variable operating conditions. To obtain robust feature representation, the denoising autoencoder is used to denoise and reduce dimension of unlabeled rolling bearing signals. For those unlabeled target domain signals, a feature matching method based on multi-kernel maximum mean discrepancies between source domain and target domain is adopted to get enough labeled target domain samples. Then, these rolling bearing signals are converted to multi-dimensional graph samples and fed into a convolutional neural network model for fault diagnosis. To improve the generalization of convolutional neural network under variable operating conditions, we combine model-based transfer learning with feature-based transfer learning to initialize and optimize the convolutional neural network parameters. The effectiveness of the proposed method is validated through several comparative experiments of Case Western Reserve University data. The results demonstrate that the proposed method can learn features adaptively from noisy data and increase the accuracy rate by 2%–8% comparing with other models.

scholarly journals Application of Multi-Dimension Input Convolutional Neural Network in Fault Diagnosis of Rolling Bearings

2019 ◽  
Vol 9 (13) ◽  
pp. 2690 ◽  
Author(s):  
Tao Zan ◽  
Hui Wang ◽  
Min Wang ◽  
Zhihao Liu ◽  
Xiangsheng Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Rolling Bearing ◽  
Diagnostic Model ◽  
Original Signal ◽  
Proposed Model ◽  
Convolution Model ◽  
The Stability

Aiming at the problem of poor robustness of the intelligent diagnostic model, a fault diagnosis model of rolling bearing based on a multi-dimension input convolutional neural network (MDI-CNN) is proposed. Compared with the traditional convolution neural network, the proposed model has multiple input layers. Therefore, it can fuse the original signal and processed signal—making full use of advantages of the convolutional neural networks to learn the original signal characteristics automatically, and also improving recognition accuracy and anti-jamming ability. The feasibility and validity of the proposed MDI-CNN are verified, and its advantages are proved by comparison with the other related models. Moreover, the robustness of the model is tested by adding the noise to the test set. Finally, the stability of the model is verified by two experiments. The experimental results show that the proposed model improves the recognition rate, robustness and convergence performance of the traditional convolution model and has good generalization ability.

scholarly journals Frequency-Domain Fusing Convolutional Neural Network: A Unified Architecture Improving Effect of Domain Adaptation for Fault Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 450
Author(s):  
Xudong Li ◽  
Jianhua Zheng ◽  
Mingtao Li ◽  
Wenzhen Ma ◽  
Yang Hu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Transfer Learning ◽  
Network Architecture ◽  
Domain Adaptation ◽  
Training Dataset ◽  
Testing Dataset ◽  
Feature Extractor

In recent years, transfer learning has been widely applied in fault diagnosis for solving the problem of inconsistent distribution of the original training dataset and the online-collecting testing dataset. In particular, the domain adaptation method can solve the problem of the unlabeled testing dataset in transfer learning. Moreover, Convolutional Neural Network (CNN) is the most widely used network among existing domain adaptation approaches due to its powerful feature extraction capability. However, network designing is too empirical, and there is no network designing principle from the frequency domain. In this paper, we propose a unified convolutional neural network architecture from a frequency domain perspective for a domain adaptation named Frequency-domain Fusing Convolutional Neural Network (FFCNN). The method of FFCNN contains two parts, frequency-domain fusing layer and feature extractor. The frequency-domain fusing layer uses convolution operations to filter signals at different frequency bands and combines them into new input signals. These signals are input to the feature extractor to extract features and make domain adaptation. We apply FFCNN for three domain adaptation methods, and the diagnosis accuracy is improved compared to the typical CNN.

Sign in / Sign up

Export Citation Format

Share Document