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.

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.

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.

Comparison of Glucose Concentrations between Flash and Continuous Monitoring Systems in Patients with Diabetes Mellitus

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 917-P
Author(s):  
RYO KUMAGAI ◽  
AIKO MURAMATSU ◽  
MASANAO FUJII ◽  
YUKINO KATAKURA ◽  
KEIKO FUJIE ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Continuous Monitoring ◽  
Monitoring Systems ◽  
Patients With Diabetes

Experience of Operation of the Mine Winding Plants Equipped with Continuous Monitoring Systems

2019 ◽  
pp. 52-58
Author(s):  
G.D. Trifanov ◽  
◽  
A.A. Knyazev ◽  
A.P. Filatov ◽  
V.V. Lauk ◽  
...  
Keyword(s):  
Continuous Monitoring ◽  
Monitoring Systems

THE INTEGRAL INDEX OF PROBLEMS IN THE EVALUATION OF THE HEALTH STATE

2018 ◽  
pp. 12-14
Author(s):  
A.E. Vlasenko ◽  
N.M. Zhilina ◽  
A.A. Kozhevnikov ◽  
G.I. Chechenin
Keyword(s):  
Decision Making ◽  
Population Health ◽  
Economic Indicators ◽  
Health State ◽  
Integral Index ◽  
Municipal District ◽  
Risk Areas

The article presents the algorithm for calculating the integral index of problems in the evaluation of indicators of population health and identifying risk areas. The integral indices for Novokuznetsk municipal district were calculated. The index can be used by specialists of various levels and regions in assessing the level of health, environmental and socio-economic indicators for appropriate decision-making.

A BIM-based decision-making framework for optimal seismic retrofit of existing buildings

2021 ◽  
Vol 242 ◽  
pp. 112544
Author(s):  
Nicola Caterino ◽  
Iolanda Nuzzo ◽  
Antonio Ianniello ◽  
Giorgio Varchetta ◽  
Edoardo Cosenza
Keyword(s):  
Decision Making ◽  
Seismic Retrofit ◽  
Existing Buildings ◽  
Decision Making Framework

Clinical Decision-making Framework against Over-testing based on Modeling Implicit Evaluation Criteria

2021 ◽  
pp. 103823
Author(s):  
Yang Yang ◽  
Hongxing Huo ◽  
Jingchi Jiang ◽  
Xuemei Sun ◽  
Yi Guan ◽  
...  
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Evaluation Criteria ◽  
Clinical Decision ◽  
Decision Making Framework

scholarly journals Online Learning Algorithms for the Real-Time Set-Point Tracking Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6620
Author(s):  
Arman Alahyari ◽  
David Pozo ◽  
Meisam Farrokhifar
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Tracking Problem ◽  
Smart Meters ◽  
Set Point ◽  
Point Tracking ◽  
Power Set ◽  
Decision Making Framework

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.

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