Fault Diagnosis and Reliability Growth for Information System

2014 ◽  
Vol 590 ◽  
pp. 763-767
Author(s):  
Zhi Hui Huang
Keyword(s):  
Information System ◽  
Bayesian Network ◽  
System Reliability ◽  
Functional Module ◽  
Decision Problems ◽  
Reliability Growth ◽  
Failure Data ◽  
Manufacturing Execution Systems ◽  
Growth Planning ◽  
Manufacturing Execution

This paper aiming at the zero-failure data and uncertain-decision problems exist in the information system reliability growth process, it proposes to build the Bayesian network topology of FMEA. It adopts Leaky Noisy-OR model, and it analyses the probability that the subsystem functional module will go wrong in quantity. It solves the problem of identifying the vague and incomplete information exists in the complex system rapidly and accurately, laying the foundation for further study of the reliability growth comprehensive ability assessment of system based on the Bayesian network. In this paper, on the background of Manufacturing Execution Systems (MES) engineering, aimed at research on models and evaluation methods of reliability growth for MES, enclosing reliability of MES task and design target, reliability growth test and analysis methods, it proposes the goal of MES reliability growth planning.

Analysis of Manufacturing Execution Systems Reliability Growth Based on Bayesian Information Fusion

2013 ◽  
Vol 336-338 ◽  
pp. 2324-2328
Author(s):  
Zhi Hui Huang
Keyword(s):  
Information Fusion ◽  
Systems Engineering ◽  
System Reliability ◽  
Reliability Growth ◽  
Manufacturing Execution Systems ◽  
Bayesian Fusion ◽  
Information Research ◽  
Function Module ◽  
Priori Information ◽  
Manufacturing Execution

On the background of Manufacturing Execution Systems engineering, based on Bayesian information fusion evaluation methods research system reliability growth. Exploreing MES function of test data is not easy to obtain, it used bayesian fusion method which is based on the type of conjugate prior distribution, assessment the MES reliability of each function module. Utilize Fuzzy priori information, research the MES function module failure rate of the Bayes estimate, to reveals the complex system under the condition of the cause of the problem is not easy to determine, which can be carried out system reliability growth test.

SYSTEM RELIABILITY GROWTH ANALYSIS USING COMPONENT FAILURE DATA

1994 ◽  
Vol 01 (01) ◽  
pp. 71-83 ◽  
Author(s):  
M. XIE ◽  
T.N. GOH
Keyword(s):  
System Reliability ◽  
Growth Analysis ◽  
Simple Approach ◽  
System Level ◽  
Reliability Growth ◽  
System Failure ◽  
Component Level ◽  
Component Failure ◽  
Failure Data ◽  
Growth Estimation

In this paper the problem of system-level reliability growth estimation using component-level failure data is studied. It is suggested that system failure data should be broken down into component, or subsystem, failure data when the above problems have occurred during the system testing phase. The proposed approach is especially useful when the system is not unchanged over the time, when some subsystems are improved more than others, or when the testing has been concentrated on different components at different time. These situations usually happen in practice and it may also be the case even if the system failure data is provided. Two sets of data are used to illustrate the simple approach; one is a set of component failure data for which all subsystems are available for testing at the same time and for the other set of data, the starting times are different for different subsystems.

scholarly journals System Reliability Growth Analysis during Warranty

2019 ◽  
Vol 4 (1) ◽  
pp. 85-94 ◽  
Author(s):  
James Li ◽  
Greg Collins ◽  
Ravi Govindarajulu
Keyword(s):  
System Reliability ◽  
Growth Analysis ◽  
Growth Models ◽  
Primary Objective ◽  
Reliability Growth ◽  
Failure Data ◽  
Duane Model ◽  
Actual Field ◽  
Reliability Performance ◽  
Field Failure

This paper presents system reliability growth analysis using actual field failure data. The primary objective of the system reliability growth is to improve the achievement of system reliability performance during system reliability demonstration, in order to achieve the predicted or contractually required system reliability commitment. An effective reliability growth model can be utilized to predict when the reliability target can be achieved based on previous reliability performance. In this paper, the system reliability growth analysis is illustrated using the Duane and AMSAA reliability growth models to determine applicability and aid in choice determination. The Duane model is a better choice for failure terminated reliability growth while AMSAA is a better choice for time terminated reliability growth. Comparisons of the Duane versus AMSAA model are carried out by conducting the statistical analysis on the observed field failures.

Reliability Evaluation for Pressure Vessel Enterprises Manufacturing Execution System

2011 ◽  
Vol 179-180 ◽  
pp. 1356-1363
Author(s):  
Zhi Hui Huang ◽  
Shu Lin Kan ◽  
Liang Wen Yan ◽  
Jun Li ◽  
Qi Hong Chen
Keyword(s):  
Pressure Vessel ◽  
Functional Module ◽  
Low Cost ◽  
Reliability Growth ◽  
Manufacturing Execution System ◽  
Reliability Engineering ◽  
Service Goal ◽  
Production Automation ◽  
Product Quality Improvement ◽  
Manufacturing Execution

This paper discussed an integrating manufacturing execution system in pressure vessel industry. MES optimizations (MESO) is a reliability growth process which is applicable to manufacturing firms to unify the supply chain plan, the service and the production automation, transferring the company's service goal to the definite operation goal and realizing the multi-varieties, multi-batch, high quality and low-cost of the production demand. This system is a complex operating system which is composed of certain subsystems, and it could complete the specified function. Basing on the MES functional module, this paper has investigated on the enterprise productive plan and scheduling module to realise the reliability growth and product quality improvement as well as some key technical strategies. Combined with the quantitative evaluation and analysis method of the reliability engineering, the paper also made an analysis for the numerous resources entity and functional module of the overall system and formed a research subject on the theory relative to the reliability growth of the MES. The research on the case has a certain instruction function for the enterprise which is going to put the MES into effect.

A Co-Evolutionary Contract Net-Based Framework for Distributed Manufacturing Execution Systems

2010 ◽  
Vol 142 ◽  
pp. 6-10
Author(s):  
R. Wang
Keyword(s):  
Information System ◽  
Operating Costs ◽  
Machine Time ◽  
Manufacturing Execution System ◽  
Time Cost ◽  
Contract Net ◽  
Manufacturing Execution Systems ◽  
Multi Agent ◽  
Active Services ◽  
Manufacturing Execution

A manufacturing execution system (MES) is the information system of a process which actively collects processes and analyses the materials, semi-finished goods, finished goods, machine time, cost etc. on the production site in real time and monitors work in progress (WIP). In this work we present a service recommender system for active services according to service requestors’ goal in manufacturing execution system -workflows. The system is modeled as a multi-agent environment where clients and service agents negotiate using a Co-evolutionary Contract-Net (CeCN).It will improve the MES performance and capability and operating costs significantly.

scholarly journals Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Liyu Zhu ◽  
Shensi Xu
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
System Reliability ◽  
Distribution System ◽  
Complex Problem ◽  
Practical Significance ◽  
Correlation Degree ◽  
Urban Distribution ◽  
Distribution System Reliability ◽  
The City

Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analyzed. In the calculation process, we combined the traditional Bayesian algorithm and the Expectation Maximization (EM) algorithm, which made the Bayesian model able to lay a more accurate foundation. The results show that the Bayesian network can accurately reflect the dynamic relationship among the factors affecting the reliability of urban distribution system. Moreover, by changing the prior probability of the node of the cause, the correlation degree between the variables that affect the successful distribution can be calculated. The results have significant practical significance on improving the quality of distribution, the level of distribution, and the efficiency of enterprises.

Bayesian Networks Application in Multi-State System Reliability Analysis

2013 ◽  
Vol 347-350 ◽  
pp. 2590-2595 ◽  
Author(s):  
Sheng Zhai ◽  
Shu Zhong Lin
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
System Reliability ◽  
Prior Probability ◽  
State System ◽  
Weak Links ◽  
Uncertain Reasoning ◽  
Reliability Modeling ◽  
Line System ◽  
Reliability Indices

Aiming at the limitations of traditional reliability analysis theory in multi-state system, a method for reliability modeling and assessment of a multi-state system based on Bayesian Network (BN) is proposed with the advantages of uncertain reasoning and describing multi-state of event. Through the case of cell production line system, in this paper we will discuss how to establish and construct a multi-state system model based on Bayesian network, and how to apply the prior probability and posterior probability to do the bidirectional inference analysis, and directly calculate the reliability indices of the system by means of prior probability and Conditional Probability Table (CPT) . Thereby we can do the qualitative and quantitative analysis of the multi-state system reliability, identify the weak links of the system, and achieve assessment of system reliability.

scholarly journals Manufacturing Execution Systems: Examples of Performance Indicator and Operational Robustness Tools

2016 ◽  
Vol 70 (9) ◽  
pp. 616-620
Author(s):  
Yannick Gendre ◽  
Gérard Waridel ◽  
Myrtille Guyon ◽  
Jean-François Demuth ◽  
Hervé Guelpa ◽  
...  
Keyword(s):  
Performance Indicator ◽  
Manufacturing Execution Systems ◽  
Manufacturing Execution
Sign in / Sign up

Export Citation Format

Share Document