Function Based Risk Assessment: Mapping Function to Likelihood

Lough, Katie Grantham; Stone, Robert B.; Tumer, Irem Y.

doi:10.1115/detc2005-85053

Cited by 15 publications

(13 citation statements)

References 14 publications

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“…The risk in early design (RED) theory [16][17][18] translates recorded information about function and failure into categorized risk likelihood and consequence elements for a product. Table 1 summarizes the likelihood and consequence calculations of the RED theory.…”

Section: The Risk In Early Design Methods (Red)mentioning

confidence: 99%

Failure Prevention in Design Through Effective Catalogue Utilization of Historical Failure Events

Lough

Stone

Tumer

2008

J Fail. Anal. and Preven.

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The science of failure prevention relies heavily on the experience of personnel on a project. As the nation is about to face a tremendous decline in the experienced workforce due to the baby boomer generation's retirement, it is critical to begin focusing on capturing their knowledge. Cataloging and communicating the knowledge of potential failures is critical to prevent engineering disasters. Many companies have adopted failure-reporting systems that allow them to record their engineering failures to promote failure prevention. While recording this information is vital to learning from past mistakes, often the information is not stored so that engineers and designers can easily recall this valuable linguistic information and use it to improve designs. Therefore, more effective systems for cataloging and utilizing corporate memory of recorded failure events are needed. This article presents the design of a computational linguistic database to support the failure prevention tool, the risk in early design (RED) method. RED promotes failure prevention by identifying failure risks as early as the conceptual phase of product design, where impacts of failure prevention are greatest. It uses a database populated by historical failure event information to present specific areas that are at risk of failure in a product.

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Section: The Risk In Early Design Methods (Red)mentioning

confidence: 99%

Failure Prevention in Design Through Effective Catalogue Utilization of Historical Failure Events

Lough

Stone

Tumer

2008

J Fail. Anal. and Preven.

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“…The Risk in Early Design (RED) [25] theory mathematically maps product functions and failures to likelihood and consequence elements based on cataloged historical failure information. Equation 1is used to obtain the function-failure mode matrix of the RED theory using cataloged historical failure modes.…”

Section: Red Theorymentioning

confidence: 99%

Identifying Risk at the Conceptual Product Design Phase: A Web-Based Software Solution and Its Evaluation

Grantham

Elrod²,

Flaschbart³

et al. 2012

MME

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This paper introduces a web-based application that enables engineers or designers to identify risks during the conceptual phase of a product design. This software application was developed based on the Risk in Early Design (RED) method developed by Author, Stone, and Tumer. The RED method demonstrates that risks can be identified in the early phase of product design by relating recorded historical failure information to product functions. Based on the concept of RED theory and current industry needs, the goals of a web-based RED application were defined. These goals are the intended benefits or functionalities that the web-based RED application would provide. In addition, a multi-level evaluation framework was adopted to determine how well the application meets the needs of various organizations. As part of the evaluation, a questionnaire was developed and administered to a sample industrial and academic user group. The results of the evaluation indicate that the web-based RED software application meets many of the goals to help an organization in performing product risk analysis

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“…and S DA (consistency 2 ) ≥ S DA (consistency 1 ) when Consistency 2 > Consistency 1 (monotone increase) (16) Eq. (13), (14), (15) and (16) basically state that when the consistency measure is zero, we completely ignore the data set (F v , S v ). When the consistency measure is one, we completely accept the data set (F v , S v ) which is what the traditional Bayesian updating process does.…”

Section: Figure 5 Details Of the Data Adjustment Stepmentioning

confidence: 99%

“…Huang and Jin [14] provided a comprehensive survey of reliability prediction data sources as potential data inputs to BRA. Grantham Lough et al [15][16] correlated the historical failure data to the functions of system during functional design to assist risk assessment. Wang and Jin [17] developed a functional design approach which utilized Bayesian Network technique with uniform distributions as inputs to assess individual function's influence on the system failure probability.…”

Section: Introductionmentioning

confidence: 99%

A Prior and Data Validation and Adjustment Scheme for Bayesian Reliability Analysis in Engineering Design

Huang

Jin

2010

Volume 5: 22nd International Conference on Design Theory and Methodology; Special Conference on Mechanical Vibration and Noise

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Bayesian reliability analysis (BRA) technique has been actively used in reliability assessment for engineered systems. However, there are two key controversies surrounding the BRA, that is, the reasonableness of the prior, and the consistency among all data sets. These issues have been debated in Bayesian analysis for many years, and as we observed, they have not been resolved satisfactorily. These controversies have seriously hindered the applications of BRA as a useful reliability analysis tool to support engineering design. In this paper, a Bayesian reliability analysis methodology with a prior and data validation and adjustment scheme (PDVAS) is developed to address these issues. In order to do that, a consistency measure is defined first that judges the level of consistency among all data sets including the prior. The consistency measure is then used to adjust either the prior or the data or both to the extent that the prior and the data are statistically consistent. This prior and data validation and adjustment scheme is developed for Binomial sampling with Beta prior, called Beta-Binomial Bayesian model. The properties of the scheme are presented and discussed. Various forms of the adjustment formulas are shown and a selection framework of a specific formula, based on engineering design and analysis knowledge, is established. Several illustrative examples are presented which show the reasonableness, effectiveness and usefulness of PDVAS. General discussion of the scheme is offered to enhance the Bayesian Reliability Analysis in engineering design for reliability assessment.

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Function Based Risk Assessment: Mapping Function to Likelihood

Cited by 15 publications

References 14 publications

Failure Prevention in Design Through Effective Catalogue Utilization of Historical Failure Events

Failure Prevention in Design Through Effective Catalogue Utilization of Historical Failure Events

Identifying Risk at the Conceptual Product Design Phase: A Web-Based Software Solution and Its Evaluation

A Prior and Data Validation and Adjustment Scheme for Bayesian Reliability Analysis in Engineering Design

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