Sensor Fault Diagnosis Method based on One Dimensional Local Ternary Pattern

2021 ◽  
Author(s):  
Kun Zhang ◽  
Feiyun Xu ◽  
Susheng Cao
Keyword(s):  
Fault Diagnosis ◽  
Sensor Fault ◽  
One Dimensional ◽  
Local Ternary Pattern ◽  
Diagnosis Method

An Adaptive Multi-Sensor Fault Diagnosis Method for High-Speed Train Traction Converters

2020 ◽  
pp. 1-1
Author(s):  
Honghui Dong ◽  
Fuzhao Chen ◽  
zhipeng wang ◽  
Limin Jia ◽  
Yong Qin ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Sensor Fault ◽  
Diagnosis Method

scholarly journals Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jingli Yang ◽  
Tianyu Gao ◽  
Shouda Jiang ◽  
Shijie Li ◽  
Qing Tang
Keyword(s):  
Fault Diagnosis ◽  
Noise Immunity ◽  
Rotating Machinery ◽  
Experimental Results ◽  
Vibration Signals ◽  
One Dimensional ◽  
Noise Interference ◽  
Diagnosis Method ◽  
Diagnosis Model ◽  
Short Time

In actual engineering applications, inevitable noise seriously affects the accuracy of fault diagnosis for rotating machinery. To effectively identify the fault classes of rotating machinery under noise interference, an efficient fault diagnosis method without additional denoising procedures is proposed. First, a one-dimensional deep residual shrinkage network, which directly takes the raw vibration signals contaminated by noise as input, is developed to realize end-to-end fault diagnosis. Then, to further enhance the noise immunity of the diagnosis model, the first layer of the model is set to a wide convolution layer to extract short time features. Moreover, an adaptive batch normalization algorithm (AdaBN) is introduced into the diagnosis model to enhance the adaptability to noise. Experimental results illustrate that the fault diagnosis model for rotating machinery based on one-dimensional deep residual shrinkage network with a wide convolution layer (1D-WDRSN) can accurately identify the fault classes even under noise interference.

A Sensor Fault Diagnosis Method for Gas Turbine Control System Based on EMD and SVM

2021 ◽  
Author(s):  
Daogang Peng ◽  
Shihao Yun ◽  
Debin Yin ◽  
Bin Shen ◽  
Chao Xu ◽  
...  
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Sensor Fault ◽  
Diagnosis Method

Bridge Deflection Sensor Fault Diagnosis Base on ICA

2013 ◽  
Vol 344 ◽  
pp. 103-106
Author(s):  
Shun Ren Hu ◽  
Rui Ping Li ◽  
Li She
Keyword(s):  
Fault Diagnosis ◽  
Process Model ◽  
Element Model ◽  
Process Variables ◽  
Sensor Fault ◽  
Multivariate Statistical ◽  
Diagnosis Method ◽  
Pca Method ◽  
Diagnosis Accuracy ◽  
The Relationship

Fault diagnosis accuracy is not high for traditional multivariate statistical methods ,in this paper , the deflection sensor fault diagnosis method based on independent component analysis was proposed .This method not only removed the relationship between process variables , but also make full use of higher order statistical properties of process variables ,establish the independent element model of the process information, finally ,simulation the multivariate process model in the system ，the simulation result shows that this method can quickly and accurately detect the abnomal occurred in the system ,verify the validity of this method and advantage compared with taditional PCA method.

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.

scholarly journals Current and Speed Sensor Fault Diagnosis Method Applied to Induction Motor Drive

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 38660-38672
Author(s):  
Cuong Dinh Tran ◽  
Petr Palacky ◽  
Martin Kuchar ◽  
Pavel Brandstetter ◽  
Bach Hoang Dinh
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Sensor Fault ◽  
Speed Sensor ◽  
Diagnosis Method

Wireless Sensor Network Fault Diagnosis Method of Optimization Research and Simulation

2013 ◽  
Vol 347-350 ◽  
pp. 955-959
Author(s):  
Qiang Li
Keyword(s):  
Wireless Sensor Network ◽  
Fault Diagnosis ◽  
Sensor Network ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Wireless Sensor ◽  
Sensor Fault ◽  
Diagnosis Method ◽  
Network Fault

The traditional method of wireless sensor network fault diagnosis based on linear modeling rather than actually complicated and nod-linear relationship results in error data and leads to wrong decisions, therefore, this paper presents a sensor fault diagnosis method based on ARIMA and LSSVM integration control system which used to diagnosis sensor fault whose results are input LSSVM to fusion and get the final results of fault diagnosis. The simulation test results show that the proposed method improves the sensor node fault diagnosis accuracy, reduce the false negative rate and false positive rate.

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