scholarly journals A BRB Based Fault Prediction Method of Complex Electromechanical Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bangcheng Zhang ◽  
Xiaojing Yin ◽  
Zhanli Wang ◽  
Xiaoxia Han ◽  
Zhi Gao
Keyword(s):  
Optimal Algorithm ◽  
Quantitative Information ◽  
Numerical Control ◽  
Fault Prediction ◽  
Rule Base ◽  
Cnc Milling ◽  
Electromechanical Systems ◽  
Belief Rule Base ◽  
Qualitative Knowledge ◽  
Complex Electromechanical Systems

Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems. In order to use the quantitative information and qualitative knowledge efficiently to predict the fault, a new model is proposed on the basis of belief rule base (BRB). Moreover, an evidential reasoning (ER) based optimal algorithm is developed to train the fault prediction model. The screw failure in computer numerical control (CNC) milling machine servo system is taken as an example and the fault prediction results show that the proposed method can predict the behavior of the system accurately with combining qualitative knowledge and some quantitative information.

scholarly journals Fault Prediction Method for Wireless Sensor Network Based on Evidential Reasoning and Belief-Rule-Base

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 78930-78941 ◽  
Author(s):  
Wei He ◽  
Chuan-Qiang Yu ◽  
Guo-Hui Zhou ◽  
Zhi-Jie Zhou ◽  
Guan-Yu Hu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Prediction Method ◽  
Evidential Reasoning ◽  
Fault Prediction ◽  
Wireless Sensor ◽  
Rule Base ◽  
Belief Rule Base

scholarly journals A new health prediction model for a sensor network based on belief rule base with attribute reliability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaohua Li ◽  
Jingying Feng ◽  
Wei He ◽  
Ruihua Qi ◽  
He Guo
Keyword(s):  
Prediction Model ◽  
Sensor Network ◽  
Expert Knowledge ◽  
Average Distance ◽  
Rule Base ◽  
Distance Method ◽  
Oil Storage ◽  
Belief Rule Base ◽  
Qualitative Knowledge ◽  
Monitoring Information

AbstractHealth prediction plays an essential role in improving the reliability of a sensor network by guiding the network maintenance. However, affected by interference factors in the real operational environment, the reliability of the monitoring information about the sensor network tends to decline, which affects the health prediction accuracy. Furthermore, the lack of monitoring information and high complexity of the network increase the difficulty of health prediction. To solve these three problems, this paper proposes a new sensor network health prediction model based on the belief rule base model with attribute reliability (BRB-r). The BRB-r model is an expert system that fully considers the qualitative knowledge and quantitative data of the sensor network. In addition, it can address the fuzziness and nondeterminacy of this qualitative knowledge. In the new model, the unreliable monitoring information of the sensor network is handled by the attribute reliability mechanism. The reliability of the sensor is calculated by the average distance method. Due to the effect of the fuzziness and nondeterminacy of expert knowledge, the health status of the sensor network cannot be accurately estimated by the initial health prediction model. Consequently, the optimization model for the health prediction model is established. Finally, a case study regarding a sensor network for oil storage tanks is conducted, and the validity of this method is demonstrated.

scholarly journals Reliability Assessment Model for Industrial Control System Based on Belief Rule Base

2019 ◽  
Vol 14 (3) ◽  
pp. 419-436 ◽  
Author(s):  
Yuhe Wang ◽  
Peili Qiao ◽  
Zhiyong Luo ◽  
Guanglu Sun ◽  
Guangze Wang
Keyword(s):  
Control System ◽  
Reliability Assessment ◽  
Quantitative Information ◽  
Assessment Method ◽  
Assessment Model ◽  
Rule Base ◽  
Industrial Control System ◽  
Industrial Control ◽  
Proposed Model ◽  
Belief Rule Base

This paper establishes a novel reliability assessment method for industrial control system (ICS). Firstly, the qualitative and quantitative information were integrated by evidential reasoning(ER) rule. Then, an ICS reliability assessment model was constructed based on belief rule base (BRB). In this way, both expert experience and historical data were fully utilized in the assessment. The model consists of two parts, a fault assessment model and a security assessment model. In addition, the initial parameters were optimized by covariance matrix adaptation evolution strategy (CMA-ES) algorithm, making the proposed model in line with the actual situation. Finally, the proposed model was compared with two other popular prediction methods through case study. The results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex ICSs.

A hidden fault prediction model based on the belief rule base with power set and considering attribute reliability

2019 ◽  
Vol 62 (10) ◽  
Author(s):  
Zhijie Zhou ◽  
Zhichao Feng ◽  
Changhua Hu ◽  
Xiaoxia Han ◽  
Zhiguo Zhou ◽  
...  
Keyword(s):  
Prediction Model ◽  
Fault Prediction ◽  
Rule Base ◽  
Model Based ◽  
Belief Rule Base ◽  
Power Set ◽  
Hidden Fault

scholarly journals Circuit Tolerance Design Using Belief Rule Base

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiao-Bin Xu ◽  
Zheng Liu ◽  
Yu-Wang Chen ◽  
Dong-Ling Xu ◽  
Cheng-Lin Wen
Keyword(s):  
High Speed ◽  
Nonlinear Modeling ◽  
Optimization Method ◽  
Quantitative Information ◽  
Rule Base ◽  
Nonlinear Relationship ◽  
Highly Nonlinear ◽  
Belief Rule Base ◽  
Feasibility Region ◽  
Brb System

A belief rule-based (BRB) system provides a generic nonlinear modeling and inference mechanism. It is capable of modeling complex causal relationships by utilizing both quantitative information and qualitative knowledge. In this paper, a BRB system is firstly developed to model the highly nonlinear relationship between circuit component parameters and the performance of the circuit by utilizing available knowledge from circuit simulations and circuit designers. By using rule inference in the BRB system and clustering analysis, the acceptability regions of the component parameters can be separated from the value domains of the component parameters. Using the established nonlinear relationship represented by the BRB system, an optimization method is then proposed to seek the optimal feasibility region in the acceptability regions so that the volume of the tolerance region of the component parameters can be maximized. The effectiveness of the proposed methodology is demonstrated through two typical numerical examples of the nonlinear performance functions with nonconvex and disconnected acceptability regions and high-dimensional input parameters and a real-world application in the parameter design of a track circuit for Chinese high-speed railway.

A model for online failure prognosis subject to two failure modes based on belief rule base and semi-quantitative information

2014 ◽  
Vol 70 ◽  
pp. 221-230 ◽  
Author(s):  
Zhi-Jie Zhou ◽  
Chang-Hua Hu ◽  
Xiao-Xia Han ◽  
Hua-Feng He ◽  
Xiao-Dong Ling ◽  
...  
Keyword(s):  
Failure Modes ◽  
Quantitative Information ◽  
Rule Base ◽  
Belief Rule Base ◽  
Failure Prognosis

scholarly journals A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bincheng Wen ◽  
Mingqing Xiao ◽  
Guanghao Wang ◽  
Zhao Yang ◽  
Jianfeng Li ◽  
...  
Keyword(s):  
Life Prediction ◽  
Particle Filters ◽  
Remaining Useful Life ◽  
Rule Base ◽  
Belief Rule Base ◽  
Useful Life

A new approximate belief rule base expert system for complex system modelling

2021 ◽  
pp. 113558
Author(s):  
You Cao ◽  
Zhijie Zhou ◽  
Changhua Hu ◽  
Shuaiwen Tang ◽  
Jie Wang
Keyword(s):  
Complex System ◽  
Expert System ◽  
Rule Base ◽  
System Modelling ◽  
Belief Rule Base

New health-state assessment model based on belief rule base with interpretability

2021 ◽  
Vol 64 (7) ◽  
Author(s):  
Zhijie Zhou ◽  
You Cao ◽  
Guanyu Hu ◽  
Youmin Zhang ◽  
Shuaiwen Tang ◽  
...  
Keyword(s):  
State Assessment ◽  
Assessment Model ◽  
Rule Base ◽  
Health State ◽  
Model Based ◽  
Belief Rule Base

scholarly journals An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty

2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Experimental Result ◽  
Rule Base ◽  
Rule Based ◽  
Belief Rule Base

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

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