scholarly journals Health State Prediction of Aero-Engine Gas Path System Considering Multiple Working Conditions Based on Time Domain Analysis and Belief Rule Base

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 26
Author(s):  
Xiaojing Yin ◽  
Guangxu Shi ◽  
Shouxin Peng ◽  
Yu Zhang ◽  
Bangcheng Zhang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Working Conditions ◽  
Time Domain ◽  
Monitoring Data ◽  
Rule Base ◽  
Health State ◽  
Health States ◽  
State Prediction ◽  
Belief Rule Base ◽  
Path System

The gas path system is an important part of an aero-engine, whose health states can affect the security of the airplane. During the process of aircraft operation, the gas path system will have different working conditions over time, owing to the change of control parameters. However, the different working conditions which change the symmetry of the system will affect parameters of the health state prediction model for the gas path system. The symmetry of the system will also change. Therefore, it is important to consider the influence of variable working conditions when predicting the health states of gas path system. The accuracy of the health state prediction results of the gas path system will be low if the same evaluation standard is used for different working conditions. In addition, the monitoring data of the gas path system’s health state feature quantity is huge while the fault data which can reflect the health states of the gas path system are poor. Thus, it is difficult to establish a health state prediction model only by using the monitoring data of the gas path system. In order to avoid problems, this paper proposes a health state prediction model considering multiple working conditions based on time domain analysis and a belief rule base. First, working condition is divided by using time domain characteristics. Then, a belief rule base (BRB) theory-based health state prediction model is built, which can fuse expert knowledge and fault monitoring data to improve modeling accuracy. The reference value of the feature is given by the fuzzy C-means algorithm in a model. To decrease the uncertainty of expert knowledge, the covariance matrix adaptive evolution strategy (CMA-ES) is used as the optimization algorithm. Finally, a NASA public dataset without labels is used to verify the proposed health state model. The results show that the proposed health prediction model of a gas path system can accurately realize health state prediction under multiple working conditions.

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 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.

Value of Information for Continuous Monitoring Systems in Recurrent Maintenance Decision Scenarios

2021 ◽  
Author(s):  
Xinyang Liu ◽  
Pingfeng Wang
Keyword(s):  
Decision Making ◽  
Continuous Monitoring ◽  
Monitoring Data ◽  
Monitoring Systems ◽  
Engineering System ◽  
Health State ◽  
System Failure ◽  
State Prediction ◽  
First Case ◽  
Decision Making Framework

Abstract Monitoring systems play a crucial role in improving system failure resilience and preventing tragic consequences brought by unexpected system failure and saving the consequential high cost. Continuous monitoring systems have been applied to diversified systems for well-informed operations. Although plenty research has devoted to predicting system states using the continuous data flow, there still lacks a systematic decision-making framework for system designers and engineering system owners to maximize their benefits on adopting monitoring systems. This paper constructs such a decision-making framework, with which system owners can evaluate the operation cost change under specific operation modes considering the effectiveness of continuous monitoring systems in predicting system failures. Two case studies have been conducted to illustrate the value evaluation of the monitoring information and the system maintenance process with the aid of different prognostic results based on the monitoring data. The first case study considers a health-state prediction with fixed accuracy while the second one incorporates the accuracy improvement as the monitoring data accumulates. Results show that the value of monitoring systems will be influenced by the deviation among the equipment group, the accuracy of system-state prediction, and different types of cost involved in the operating process. And the adjustment of maintenance actions based on monitoring and prognosis information will help improve the value of monitoring systems.

A New Prediction Model Based on Belief Rule Base for System's Behavior Prediction

2011 ◽  
Vol 19 (4) ◽  
pp. 636-651 ◽  
Author(s):  
Xiao-Sheng Si ◽  
Chang-Hua Hu ◽  
Jian-Bo Yang ◽  
Zhi-Jie Zhou
Keyword(s):  
Prediction Model ◽  
Rule Base ◽  
Behavior Prediction ◽  
Model Based ◽  
Belief Rule Base

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 Health State Prediction and Performance Evaluation of Belt Conveyor Based on Dynamic Bayesian Network in Underground Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiangong Li ◽  
Yuzhi Zhang ◽  
Yu Li ◽  
Yujie Zhan ◽  
Lin Yang
Keyword(s):  
Performance Evaluation ◽  
Bayesian Network ◽  
Evaluation Model ◽  
Dynamic Bayesian Network ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Health State ◽  
Belt Conveyor ◽  
State Prediction ◽  
And Performance

To deal with the problem of weak prediction and performance evaluation capabilities of traditional prediction and evaluation methods, a method of health state prediction and performance evaluation of belt conveyor based on Dynamic Bayesian Network (DBN) is proposed. First, the belt conveyor sensor monitoring data are preprocessed to obtain the feature data set with labels. At the same time, qualitative and quantitative analyses and interval discretization are carried out from belt conveyor fault-causing elements to construct the DBN network. Then, the sample data are applied to the DBN network for training, and the DBN-based prediction and performance evaluation model is established. Finally, taking the real-time monitoring data of a belt conveyor in an underground mine as an example, a DBN-based belt conveyor health prediction and evaluation model is constructed to evaluate and predict the health of the equipment. The results show that the model could identify different operating conditions and failure modes and further improves the prediction accuracy.

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