Complete Simulation and Fault Diagnosis of Sucker-Rod Pumping

2021 ◽  
pp. 1-14
Author(s):  
Bin Zhang ◽  
Xianwen Gao ◽  
Xiangyu Li
Keyword(s):  
Fault Diagnosis ◽  
Simulation Model ◽  
Oil Wells ◽  
Oil Field ◽  
Motor Power ◽  
Sucker Rod ◽  
Computer Aided ◽  
Valve Leakage ◽  
Information Construction ◽  
Diagnosis Model

Summary In this paper, we study the simulation and fault diagnosis of a conventional pumping unit under balanced conditions. As the energy input of sucker-rod pumping (SRP), the motor power contains abundant information about the whole pumping cycle under SRP. It is an important step in oilfield information construction to establish a computer-aided system that is based on motor power diagnosis. Hence, we propose an SRP simulation model for generating motor power. By analyzing the working conditions of six oil wells that contain normal or insufficient liquid supply, gas lock, traveling valve leakage, standing valve leakage, and parting rod, we simulate the motor power of the six wells. In addition, we verify the simulation model using a test well with favorable performance and establish the motor power template set (MPTS) using this simulation model. To allow for computer-aided diagnosis, we propose the use of the area proportion method to extract motor power features. We establish a diagnosis model of oilwell conditions that is based on oblique decision tree and train the diagnosis model using the MPTS. Through the verification of six oil wells in the actual production of the oil field, the diagnosis model shows a favorable response. The test results show that the methods of establishing MPTS and oilwell working-condition diagnosis are feasible.

scholarly journals Application of Deep Learning in Fault Diagnosis of Rotating Machinery

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 919
Author(s):  
Wanlu Jiang ◽  
Chenyang Wang ◽  
Jiayun Zou ◽  
Shuqing Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Generative Adversarial Networks ◽  
Extraction Ability ◽  
One Dimensional ◽  
Adversarial Networks ◽  
Diagnosis Model ◽  
The One

The field of mechanical fault diagnosis has entered the era of “big data”. However, existing diagnostic algorithms, relying on artificial feature extraction and expert knowledge are of poor extraction ability and lack self-adaptability in the mass data. In the fault diagnosis of rotating machinery, due to the accidental occurrence of equipment faults, the proportion of fault samples is small, the samples are imbalanced, and available data are scarce, which leads to the low accuracy rate of the intelligent diagnosis model trained to identify the equipment state. To solve the above problems, an end-to-end diagnosis model is first proposed, which is an intelligent fault diagnosis method based on one-dimensional convolutional neural network (1D-CNN). That is to say, the original vibration signal is directly input into the model for identification. After that, through combining the convolutional neural network with the generative adversarial networks, a data expansion method based on the one-dimensional deep convolutional generative adversarial networks (1D-DCGAN) is constructed to generate small sample size fault samples and construct the balanced data set. Meanwhile, in order to solve the problem that the network is difficult to optimize, gradient penalty and Wasserstein distance are introduced. Through the test of bearing database and hydraulic pump, it shows that the one-dimensional convolution operation has strong feature extraction ability for vibration signals. The proposed method is very accurate for fault diagnosis of the two kinds of equipment, and high-quality expansion of the original data can be achieved.

Study on Remote Control and Fault Diagnosis for Ultrahigh Speed Grinding

2007 ◽  
Vol 359-360 ◽  
pp. 518-522
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu ◽  
Xing Yu Jiang ◽  
Jian Yu Yang
Keyword(s):  
Remote Control ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Rough Set ◽  
Theoretical Research ◽  
Reduction Rule ◽  
Diagnosis System ◽  
Linear Speed ◽  
Fault Diagnosis System ◽  
Diagnosis Model

Remote control and fault diagnosis of ultrahigh speeding grinding is studied, which is based on the theory of rough set. Knowledge acquisition and reduction rule of fault diagnosis, realization method of remote control for ultrahigh speed grinding are studied, diagnosis model is established. Based on the theoretical research and ultrahigh speed grinder with a linear speed of 250 m/s, the remote control and fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running show that the environment is improved, the mental pressure of workers is relieved and the efficiency is improved. At the same time, it proves that the ability to diagnosis and the accuracy of diagnosis for the ultrahigh speed grinding are improved and the time for diagnosis is shortened by applying rough set.

Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

2021 ◽  
pp. 1748006X2110093
Author(s):  
Qing Zhang ◽  
Heng Li ◽  
Xiaolong Zhang ◽  
Haifeng Wang
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Feature Space ◽  
High Dimensional ◽  
Fisher Discriminant Analysis ◽  
Vibration Signals ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Diagnosis Accuracy ◽  
Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

Study and Comparison of Vibration-Based Intrusive and Non-Intrusive Sand Monitoring System

2013 ◽  
Vol 701 ◽  
pp. 440-444
Author(s):  
Gang Liu ◽  
Peng Tao Liu ◽  
Bao Sheng He
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Oil Production ◽  
Oil Wells ◽  
Oil Field ◽  
Sand Production ◽  
The Status

Sand production is a serious problem during the exploitation of oil wells, and people put forward the concept of limited sand to alleviate this problem. Oil production with limited sanding is an efficient mod of production. In order to complete limited sand exploitation, improve the productivity of oil wells, a real-time sand monitoring system is needed to monitor the status of wells production. Besides acoustic sand monitoring and erosion-based sand monitoring, a vibration-based sand monitoring system with two installing styles is proposed recently. The paper points out the relationships between sand monitoring signals collected under intrusive and non-intrusive installing styles and sanding parameters, which lays a good foundation for further study and actual sand monitoring in oil field.

Diagnosability and Diagnostic Algorithm for Pancake Graph under the Comparison Model

2015 ◽  
Vol 15 (01n02) ◽  
pp. 1550005
Author(s):  
WENJUN LIU ◽  
CHENG-KUAN LIN
Keyword(s):  
Fault Diagnosis ◽  
Interconnection Networks ◽  
Linear Time ◽  
Hamiltonian Path ◽  
Diagnostic Algorithm ◽  
Efficient Algorithms ◽  
Comparison Model ◽  
Diagnosis Model

Fault diagnosis is important for the reliability of interconnection networks. This paper addresses the fault diagnosis of n-dimensional pancake graph Pn under the comparison diagnosis model. By the concept of local diagnosability, we first prove that the diagnosabitly of Pn is n − 1, and it has strong local diagnosability property even if there are n − 3 faulty edges. Furthermore, we present efficient algorithms to locate extended star and Hamiltonian path structures in Pn, respectively. According to the works of Li et al. and Lai, the extended star and Hamiltonian path structures can be used to identify all faulty vertices in linear time, provided the number of faulty vertices is no more than n − 1.

Computer-Aided Analysis and Measurement of an Interconnection System

2011 ◽  
Vol 308-310 ◽  
pp. 2279-2285
Author(s):  
Wei Chen Lee ◽  
Hill Wu
Keyword(s):  
Simulation Model ◽  
Frequency Domain ◽  
Insertion Loss ◽  
Return Loss ◽  
Design Phase ◽  
Electrical Characteristics ◽  
Impedance Mismatch ◽  
Measurement Results ◽  
Computer Aided Analysis ◽  
Computer Aided

The electrical characteristics of an interconnection system, which include impedance, insertion loss, and return loss, can greatly affect its performance as the signal speed increases. The objective of this research was to understand the discrepancy between the computer-aided analysis and measurement results of an interconnection system, so that a more accurate prediction of the electrical characteristics of this system can be made during the design phase. It was discovered that in both the time and frequency domain the computer-aided analysis results were consistent with the measurement results. Given these conclusions the simulation model was modified to improve the impedance mismatch within the interconnection system. It was found that by properly designing the antipad, the impedance mismatch can be greatly reduced.

Sign in / Sign up

Export Citation Format

Share Document