Research on Fault Diagnosis Method of Rod Pumping Wells Based on CNN_APRCSO_SVM

2021 ◽  
pp. 339-350
Author(s):  
Mingxin Wang ◽  
Chuanzhi Zang ◽  
Zhenping Ji ◽  
Yunpeng He ◽  
Peng Zeng
Keyword(s):  
Fault Diagnosis ◽  
Pumping Wells ◽  
Diagnosis Method

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4370
Author(s):  
Yongze Jin ◽  
Guo Xie ◽  
Yankai Li ◽  
Xiaohui Zhang ◽  
Ning Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expectation Maximization ◽  
Monitoring Data ◽  
Sensor Data ◽  
Operating Environment ◽  
Mass Model ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Mass ◽  
Parameter Identifications

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

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