A Data-Driven Fault Diagnosis Method for Static Processes with Periodic Disturbances

2018 ◽  
Author(s):  
Zhiwen Chen ◽  
Tao Peng ◽  
Chunhua Yang ◽  
Wenfeng Hu
Keyword(s):  
Fault Diagnosis ◽  
Data Driven ◽  
Periodic Disturbances ◽  
Diagnosis Method

scholarly journals A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

2021 ◽  
Vol 11 (23) ◽  
pp. 11116
Author(s):  
Ke Zheng ◽  
Guozhu Jia ◽  
Linchao Yang ◽  
Chunting Liu
Keyword(s):  
Fault Diagnosis ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Driven ◽  
Total Cost ◽  
Operation And Maintenance ◽  
Diagnosis Method ◽  
Overall Performance ◽  
Diagnosis Model ◽  
The Cost

In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

scholarly journals Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yu Ding ◽  
Qiang Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Kriging Model ◽  
Data Driven ◽  
Power Transformers ◽  
Global Approximation ◽  
The Neural Network ◽  
Modeling Accuracy ◽  
Diagnosis Method ◽  
Transformer Fault

A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil. In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging model with BP neural network is presented and is further extended using linearity weighted aggregation method. The presented method integrates characteristics of the global approximation of the neural network technology and the localized departure of the Kriging model, which improves modeling accuracy. Finally, the validity of this method is demonstrated by several numerical computations of transformer fault diagnosis problems.

scholarly journals Single Fault Diagnosis Method of Sensors in Cascade System Based on Data-Driven

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7340
Author(s):  
Wenbo Na ◽  
Siyu Guo ◽  
Yanfeng Gao ◽  
Jianxing Yang ◽  
Junjie Huang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Real Time ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Cascade System ◽  
The Real ◽  
Single Fault ◽  
Time Calibration ◽  
Diagnosis Method

The reliability and safety of the cascade system, which is widely applied, have attached attention increasingly. Fault detection and diagnosis can play a significant role in enhancing its reliability and safety. On account of the complexity of the double closed-loop system in operation, the problem of fault diagnosis is relatively complex. For the single fault of the second-order valued system sensors, a real-time fault diagnosis method based on data-driven is proposed in this study. Off-line data is employed to establish static fault detection, location, estimation, and separation models. The static models are calibrated with on-line data to obtain the real-time fault diagnosis models. The real-time calibration, working flow and anti-interference measures of the real-time diagnosis system are given. Experiments results demonstrate the validity and accuracy of the fault diagnosis method, which is suitable for the general cascade system.

A Smart Data-Driven Fault Diagnosis Method for Sustainable and Healthy Building System Operations

2021 ◽  
pp. 107-120
Author(s):  
Xuyuan Liu ◽  
Xinghua Wang ◽  
Cheng Fan ◽  
Bufu Huang ◽  
Jiayuan Wang
Keyword(s):  
Fault Diagnosis ◽  
Data Driven ◽  
Diagnosis Method ◽  
Smart Data ◽  
Building System
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