Mission Reliability Evaluation for Fuzzy Multistate Manufacturing System Based on an Extended Stochastic Flow Network

He, Yihai; Chen, Zhaoxiang; Zhao, Yuan; Han, Xiao; Zhou, Di

doi:10.1109/tr.2019.2957502

Cited by 58 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study on finding the equipment and equipment variables that affect the yield in multistage manufacturing processes, Ma et al applied a statistical method to the chemical vapor deposition process to increase the yield based on important variables that affect the quality variables [8]. In addition, a methodology for monitoring and predicting equipment status by analyzing data collected from sensors [9], a method for integrated maintenance according to equipment performance reduction [10], and a reliability evaluation method for a fuzzy multistate manufacturing system based on ESFN (extended stochastic flow network) are presented [11]. In addition, an intelligent control system that monitors process parameters and detects abnormalities [12] and a framework for recognizing and obtaining big data for each product manufacturing cycle were presented [13].…”

Section: Related Researchmentioning

confidence: 99%

Hierarchical Factor Analysis Methodology for Intelligent Manufacturing

Sim

2021

Complexity

View full text Add to dashboard Cite

To realize intelligent manufacturing, a controllable factory must be built, and manufacturing competitiveness must be achieved through the improvement of product quality and yield. The yield in the micromanufacturing process is gaining importance as a management factor used in deciding the production cost and product quality as product functions becomes more sophisticated. Because the micromanufacturing process involves manufacturing products through multiple steps, it is difficult to determine the process or equipment that has encountered failure, which can lead to difficulty in securing high yields. This study presents a structural model for building a factory integration system to analyze big data at manufacturing sites and a hierarchical factor analysis methodology to increase product yield and quality in an intelligent manufacturing environment. To improve the product yield, it is necessary to analyze the fault factors that cause low yields and locate and manage the critical processes and equipment factors that affect these fault factors. However, yield management is a difficult problem because there exists a correlation between equipment, and in the sequence of process equipment that the lot passed through, the downstream and the upstream cause complex faults. This study used data-mining techniques to identify suspected processes and equipment that affect the yield of products in the manufacturing process and to analyze the key factors of the equipment. Ultimately, we propose a methodology to find the key factors of the suspected process and equipment that directly affect the implementation of the intelligent manufacturing scheme and the yield of the product. To verify the effect of key parameters of critical processes and equipment on the yield, the proposed methodology was applied to actual manufacturing sites.

show abstract

Section: Related Researchmentioning

confidence: 99%

Hierarchical Factor Analysis Methodology for Intelligent Manufacturing

Sim

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…Equipment System. Fault tree analysis (FTA) is a method of system reliability analysis [11,12]. By analyzing various factors that cause system failure, the inverted tree logic diagram is established.…”

Section: Fault Tree Event Description Of the Terminal Air Defensementioning

confidence: 99%

Contribution Rate Evaluation Method of Equipment System of Systems Based on Fault Tree

Cheng

Dong

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, the terminal air defense equipment system of systems (TADESoS) is studied as an example. The TADESoS is an important part of the joint air defense equipment system of systems, which mainly carries out the combat task to the low altitude flight target. The contribution rate evaluation of the TADESoS can provide theoretical basis for guiding the tactical plan of the TADESoS. Aiming at the problems existing in the evaluation of contribution rate of TADESoS, such as the difficulty of describing the structure of system of systems, the strong subjectivity of the evaluation method, and the difficulty of application of the evaluation results, this paper proposes a method of evaluating the contribution rate of the TADESoS based on fault tree. The method describes the structure of the TADESoS by multiattribute nodes. The probability of the top event is calculated by using the probability of the bottom event. Finally, based on the importance of the bottom event, the contribution rate evaluation model of the TADESoS is established, which solves the existing problems in the current research. Finally, the feasibility of the method is verified by an example.

show abstract

“…With the rapid development of industrial technology, the complexity of engineering systems has increased significantly [1][2][3][4]. The increase in the failure cost of the system prompts people to pay more attention to the reliability and safety of the system [5]. Therefore, prognostics and health management (PHM) technology are widely studied and applied in the industry [3,4,6].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering

Guan

Cui

et al. 2020

Sensors

View full text Add to dashboard Cite

Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.

show abstract

Mission Reliability Evaluation for Fuzzy Multistate Manufacturing System Based on an Extended Stochastic Flow Network

Cited by 58 publications

References 29 publications

Hierarchical Factor Analysis Methodology for Intelligent Manufacturing

Hierarchical Factor Analysis Methodology for Intelligent Manufacturing

Contribution Rate Evaluation Method of Equipment System of Systems Based on Fault Tree

A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering

Contact Info

Product

Resources

About