Performance Evaluation of Hybrid Production Lines with Multiple Stochastic Failure Modes and Unreliable Limited Buffers

2011 ◽  
Vol 58-60 ◽  
pp. 410-416 ◽  
Author(s):  
Jun Liu ◽  
Zhi Yuan Rui ◽  
Rui Cheng Feng ◽  
Chun Li Lei
Keyword(s):  
Performance Evaluation ◽  
Manufacturing System ◽  
Failure Modes ◽  
Solution Technique ◽  
Production Lines ◽  
Hybrid Production ◽  
System A ◽  
Limited Buffers ◽  
Stochastic Events ◽  
Two Machines

A hybrid manufacturing system which consists of two machines is examined. Unreliable buffers and multiple stochastic failure modes of the machines are introduced to the system. A new method of the system performance evaluation is presented. The states of the system are analyzed in detail based on a discrete model and a new solution technique is given to determine the state probabilities. The method can be used to analyze the cases arising from two or more stochastic events or longer (or more complex)production lines. Numerical results are also offered to testify the method and show some characteristics of the system.

An Extended System Aggregation Method for Hybrid Production Lines with Multiple Failure Rate and Unreliable Limited Buffers

2012 ◽  
Vol 459 ◽  
pp. 432-436
Author(s):  
Jun Liu ◽  
Ye Nan Wang ◽  
Jian Hua Li ◽  
Rui Shen Chen
Keyword(s):  
Evaluation Method ◽  
Failure Modes ◽  
Extended System ◽  
Production Lines ◽  
Aggregation Method ◽  
Hybrid Production ◽  
Production Models ◽  
Limited Buffers ◽  
Performance Evaluation Method ◽  
Stochastic Events

In this study, a new system performance evaluation method is introduced to the two-machine line. After that, the extended system aggregation model is developed and corresponding aggregation formulations are deduced.Different from traditional production models, the production line features unreliable buffers and multiple stochastic failure modes of the machines. The method is applicable to analyzing the cases arising from two or more stochastic events or more complex production lines

scholarly journals A Case Study in View of Developing Predictive Models for Water Supply System Management

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3305
Author(s):  
Katarzyna Pietrucha-Urbanik ◽  
Barbara Tchórzewska-Cieślak ◽  
Mohamed Eid
Keyword(s):  
Water Supply ◽  
Predictive Models ◽  
Failure Modes ◽  
Supply System ◽  
Water Supply System ◽  
System Management ◽  
System A ◽  
Operational Failure ◽  
Operational Data

Initiated by a case study to assess the effectiveness of the modernisation actions undertaken in a water supply system, some R&D activities were conducted to construct a global predictive model, based on the available operational failure and recovery data. The available operational data, regarding the water supply system, are the pipes’ diameter, failure modes, materials, functional conditions, seasonality, and the number of failures and time-to-recover intervals. The operational data are provided by the water company responsible of the supply system. A predictive global model is proposed based on the output of the operational data statistical assessment. It should assess the expected effectiveness of decisions taken in support of the modernisation and the extension plan.

scholarly journals IMPROVING THE RELIABILITY OF BODY ASSEMBLY LINE EQUIPMENT

1996 ◽  
Vol 03 (01) ◽  
pp. 11-24 ◽  
Author(s):  
KAKURO AMASAKA ◽  
HIROHISA SAKAI
Keyword(s):  
Conventional Method ◽  
Assembly Line ◽  
Failure Modes ◽  
Lead Times ◽  
Reliability Engineering ◽  
Production Lines ◽  
Line Production ◽  
Equipment Reliability ◽  
New Models ◽  
Short Time

It is necessary to establish higher levels of equipment reliability in a short time, the market demands ever shorter lead times for the release of new models. Also, the demand for new-model cars is very strong immediately after their introduction. The conventional method for enhancing equipment reliability is by screening alone. However, this requires screening operations on production lines and so has been an obstacle to line production and prevented shortening of lead times. We are now able to dramatically enhance equipment reliability in a very short time by detecting failure modes and forecasting the number of occurrences using a scientific technique based on reliability engineering.

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