Research on gas sensor fault diagnosis based on wavelet analysis and VLBP neural network

2010 ◽  
pp. 221-227
Author(s):  
Junhao Wang ◽  
Xiangrui Meng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Gas Sensor ◽  
Sensor Fault

scholarly journals Gas sensor fault diagnosis based on Convolutional Neural Network

2020 ◽  
Vol 768 ◽  
pp. 062089
Author(s):  
Yongyi Sun ◽  
Yangyang Liu ◽  
Fang Ji ◽  
Gang Li ◽  
Yanjun Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Gas Sensor ◽  
Sensor Fault

scholarly journals Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

2011 ◽  
Vol 2-3 ◽  
pp. 117-122 ◽  
Author(s):  
Peng Peng Qian ◽  
Jin Guo Liu ◽  
Wei Zhang ◽  
Ying Zi Wei
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Energy Analysis ◽  
Sensor Faults ◽  
Analysis Method ◽  
Sensor Fault ◽  
Fault Type ◽  
Ideal Tool ◽  
Non Stationary Signal

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

Fault Diagnosis of Traction Inverter Based on Wavelet Analysis and Neural Network

2014 ◽  
Vol 1061-1062 ◽  
pp. 1025-1030
Author(s):  
Ya Fei Wang ◽  
Wen Ming Zhang ◽  
Xing Lai Ge ◽  
Yang Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Open Circuit ◽  
Phase Current ◽  
Three Phase ◽  
Simulation Results

Due to IGBT open-circuit fault of CRH2 EMU’s traction inverter, a method of its fault diagnosis with the three-phase current signals as detection objects is conducted. By applying the wavelet analysis, three-phase current signals are decomposed for four times. With the coefficients of each layer obtained, the energy values of layers are calculated as well as the vectors corresponding to failure modes. According to the vectors regarded as input and the expected output, a BP neural network is established. Through training the network, the parameters of network can be defined. In addition, to test and evaluate the performance of network, certain noise is added to the three-phase current signals. Simulation results show it is feasible for the fault diagnosis of traction inverter.

Sensor Fault Diagnosis of Control System Based on CMKPCA and BP Neural Network

2018 ◽  
Author(s):  
Yinsong Wang ◽  
Tianshu Sun ◽  
Bo Cai ◽  
Lu Lu ◽  
Xiongjie Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Sensor Fault

Sensor Fault Diagnosis Based on SOFM Neural Network

2014 ◽  
Vol 511-512 ◽  
pp. 193-196
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Accuracy ◽  
Classification Methods ◽  
Statistical Classification ◽  
Sensor Fault ◽  
Discriminant Functions ◽  
Fast Speed ◽  
Diagnosis Method ◽  
Simulation Results

Traditional sensor fault diagnosis is mainly based on statistical classification methods. The discriminant functions in these methods are extremely complex, and typical samples of reference modes are not easy to get, therefore it is difficult to meet the actual requirements of a project. In view of the deficiencies of conventional sensor fault diagnosis technologies, a fault diagnosis method based on self-organizing feature map (SOFM) neural network is presented in this paper. And it is applied to the fault diagnosis of pipeline flow sensor in a dynamic system. The simulation results show that the fault diagnosis method based on SOFM neural network has a fast speed, high accuracy and strong generalization ability, which verifies the practicality and effectiveness of the proposed method.

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