System Reliability Prediction - Towards a General Approach Using a Neural Network

Breitler, Alan L.; Sloan, Crystal D.

doi:10.2514/6.2005-7611

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…Predicting the reliability of multiple-component systems is gaining popularity, as systems become more complex and more difficult to analyze and assess for reliability (Brietler and Sloan, 2005). Although neural networks have been used to predict failure modes in systems, there are other aspects and relationships that need to be considered, with a goal of developing a generalized approach to predict system reliability.…”

Section: Neural Network Modulementioning

confidence: 99%

A Decision Support System for Predicting the Reliability of a Robotic Dispensing System

Sturek¹,

Ramakrishnan²,

Nagula³

et al. 2007

Proceedings of the Ninth International Conference on Enterprise Information Systems

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Decision Support Systems (DSS) are information systems designed to support individual and collective decision-making. This research presents the development of a DSS to facilitate the prediction of the reliability of a Robotic Dispensing System (RDS). While it is extremely critical for design teams to identify the potential defects in the product before releasing them to the customers, predicting reliability is extremely difficult due to the absence of actual failure data. Design teams often adopt tools such as Failure Mode Effects and Analysis (FMEA) to analyze the various failure modes in the product. There are commercial softwares that facilitate predicting reliability and conducting FMEA. However, there are limited approaches that combine these two critical aspects of product design. The objective of this research is to develop a DSS that would help design teams track the overall system reliability, while concurrently using the data from the alpha testing phase to perform the FMEA. Hence, this DSS is capable of calculating the age-specific reliability value for a Robotic Dispensing System (RDS), in addition to storing the defect information, for the FMEA process. The Risk Priority Number (RPN) calculated using the data gathered serves as the basis for the design team to identify the modifications to the product design. The tool, developed in Microsoft Access ® , would be subsequently utilized to track on-field performance of the RDS. This would facilitate continuous monitoring of the RDS from the customer site, especially during its "infant mortality" period.

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Section: Neural Network Modulementioning

confidence: 99%

A Decision Support System for Predicting the Reliability of a Robotic Dispensing System

Sturek¹,

Ramakrishnan²,

Nagula³

et al. 2007

Proceedings of the Ninth International Conference on Enterprise Information Systems

View full text Add to dashboard Cite

show abstract

“…The social control of system confide on the judgement taken throughout coming up with, implementing in operation and maintaining. Several researcher studied the price and liableness of system earlier [3,4,5,7]. For complex system, it is tedious to solve the mathematical equations using the traditional approach.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Reliability Factors in Chocolate Manufacturing Plant using Boolean Function Technique and Neural Networking.

Gupta¹,

Naithani²,

Chandra³

2019

IJITEE

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A simple approach of solving a mathematical model depicting the reliability of a system is by doing its mathematical formulation using Boolean algebra technique and neural networking approach in simplication of complex system. Various reliability parameters are computed on applying Boolean and neural network approach. The author focus in this paper is to calculate the cost and factors of reliability of chocolate manufacturing plant using feed forward neural network approach using MATLAB.

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“…Also cost factor, significantly, reveals the economical conditions that help in decision making. The cost and reliability of multi-component system have been studied by many earlier researchers [3,4,5,7]. Current developments in advanced traffic control techniques are giving rise to an increasing requirement for reliable near-future forecasts of traffic flow.…”

Section: Introductionmentioning

confidence: 99%

Operational Readiness of Traffic Signal System with Human Error under Environmental Conditions Using Neural Network Approach

Sharma¹,

Gupta²,

Ekata³

2017

IOSR JM

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System Reliability Prediction - Towards a General Approach Using a Neural Network

Cited by 4 publications

References 1 publication

A Decision Support System for Predicting the Reliability of a Robotic Dispensing System

A Decision Support System for Predicting the Reliability of a Robotic Dispensing System

Assessment of Reliability Factors in Chocolate Manufacturing Plant using Boolean Function Technique and Neural Networking.

Operational Readiness of Traffic Signal System with Human Error under Environmental Conditions Using Neural Network Approach

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