Analysis of field failure data when modeled Weibull slope increases with time

Rand, Daniel; McLinn, James A.

doi:10.1002/qre.1096

Cited by 5 publications

(5 citation statements)

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“…This section has the 3 following objectives: (1) To show that from the Mohr circle analysis, it is not possible to estimate neither the Weibull family that represents the stressed system nor the designed R(t) index. (2) To show that because the estimated principal stresses and the eigenvalues are the same, then the Mohr circle analysis and the eigensystem analysis are equivalent each other.…”

Section: Principal Stress and Eigenvalues Generalitiesmentioning

confidence: 99%

“…The objective of this section is (1) to present the 3 stress/ strength relationships used to determine the minimal S y − min and the average μ M material's strength to be selected, (2) to present the steps to determine both the S y − min and the μ M values for which the designed element fulfills with a desired R(t) index, and (3) to present the steps to determine the R(t) index that corresponds to specific used μ M material's strength average. Let us start with the stress/strength relationships.…”

Section: Selection Of the Material's Strength For Constant Stressmentioning

confidence: 99%

“…Because in Weibull analysis, the shape parameter β characterizes the ageing of the system, 1 then in the Weibull reliability analysis, the used β value plays a key role to accurately determine the reliability R(t) of the analyzed product. Even more, because in practice, β cannot be accurately estimated, [2][3][4] then it is generally selected from historical data or from line base (similar products). 5 As a consequence, the accuracy of the estimated R(t) depends on the accuracy on which the selected β value represents the analyzed process.…”

Section: Introductionmentioning

confidence: 99%

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Weibull stress distribution for static mechanical stress and its stress/strength analysis

Piña‐Monarrez

2017

Quality & Reliability Eng

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The method's steps to estimate the Weibull shape (β) and scale (η) parameters, based only on the ratio of the maximal and minimal principal stresses (σ 1 /σ 2 ) and on the designed reliability (R(t)) are given in Section 4.1. The method's efficiency is based on the following facts: (1) The square root of σ 1 /σ 2 represents the base life on which the Weibull lifetimes are estimated (see Equation 61).(2) The mean of the logarithms of the expected lifetimes (g(x)) is completely determined by the determinant of the analyzed stress matrix (see Equation 13).(3) The Weibull distribution is a circle centered on the arithmetic mean (μ), and it covers the whole principal stresses' span (see Figure 5). (4) σ 1 /σ 2 and g(x) completely determine the σ 1i and σ 2i values, which correspond to any lifetime in the Weibull analysis (see Equation 54). And (5) σ 1 /σ 2 and η completely determine the minimal and maximal lifetime, which corresponds to any σ 1i and σ 2i values (see Equation 57). Additionally, by using the addressed stress β and η parameters, when the stress is either constant or variable, the formulation to estimate the designed R(t) index is given. The steps to determine both the material's strength average (μ M ) for a desired R(t) index and the R(t) index, which corresponds to a used μ M value, are given.

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Section: Principal Stress and Eigenvalues Generalitiesmentioning

confidence: 99%

Section: Selection Of the Material's Strength For Constant Stressmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Weibull stress distribution for static mechanical stress and its stress/strength analysis

Piña‐Monarrez

2017

Quality & Reliability Eng

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“…The comparison of alternatives was made based on system reliability estimations by using the "reliability block diagram" method. The fundamental concepts of the systems reliability theory were pointed out, and the reliability model was defined as a parallel arrangement with redundancy in passive reserve and without maintenance for any component.Assuming Weibull time-to-fail distributions cannot be correctly estimated from field data when manufacturing populations from different vintages have different failure modes,Rand & McLinn (2011) investigated the pitfalls of ongoing parameter estimation and analyzed two cases, based upon real events. Assessment of the mixed population at each month of calendar time resulted in an increasing Weibull shape parameter estimate at each assessment.…”

mentioning

confidence: 99%

El Sistema hombre-máquina del área de maquinado y la evaluación de su confiabilidad

Amaya-Toral

Reyes-Martínez

Piña‐Monarrez

et al. 2022

RIDE

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Este trabajo contiene el sistema hombre-máquina del área de maquinado, basado en la configuración serie-paralelo y en las relaciones componente-subsistema. Se utilizó el análisis de causas y un sistema de clasificación mediante el Análisis de Conglomerados, para analizar las piezas no conformes ocurridas en 3 talleres de maquinado del sector metalmecánico y diseñar el modelo del sistema hombre-máquina. El propósito del modelo del sistema hombre-máquina es proveer las condiciones adecuadas para aplicar algunos modelos estadísticos para evaluar la confiabilidad, con en el enfoque de los sistemas serie-paralelo y bajo los supuestos de una tasa de falla constante y aditiva de las distribuciones Exponencial y Weibull respectivamente. Como una entrada del primer modelo estadístico se utilizaron los tiempos para las fallas de las piezas no conformes y pruebas de bondad de ajuste, para evaluar la confiabilidad de las relaciones componente-subsistema. Para el segundo modelo estadístico, se utilizaron las confiabilidades de las relaciones componente-subsistema, para evaluar la confiabilidad de los subsistemas, con la configuración de sistemas en serie. Las entradas del tercer modelo estadístico, se utilizaron las confiabilidades de los subsistemas, para evaluar la confiabilidad del sistema hombre-máquina, con la configuración de un sistema en paralelo. El sistema hombre-máquina y los modelos estadísticos fueron implementados y validados en una compañía del sector metalmecánico con una tasa de producción de bajo volumen y alta mezcla, cuyas operaciones son realizadas por máquinas convencionales y de Control Numérico (CNC). Con el Sistema hombre-máquina fue posible conocer la estructura formal de las áreas de maquinado desde las piezas no conformes del área de scrap e identificar las subcausas de falla que afectan al sistema (componentes). Los componentes identificados fueron: mantenimiento/set up, condiciones institucionales/actitud, perfil de ingreso, información/comunicación, calibración y proveedor. También fueron identificados los lugares más comunes donde ocurrieron las fallas (subsistemas). Los subsistemas identificados fueron: máquina, herramienta, proceso/operación, medición/instrumentación, programación/diseño y material. A través de los modelos estadísticos fue posible obtener los índices de confiabilidad para cada relación componente-subsistema, para cada subsistema, así como para el sistema hombre-máquina, identificando así áreas para la mejora. La compañía donde se implementó y validó la metodología, presentaba un alto índice de scrap en el área de maquinado y fue posible identificar que el subsistema proceso/operación y los componentes mantenimiento/set up y condiciones institucionales/actitud tuvieron la confiabilidad más baja y fueron consideradas como áreas de oportunidad para la mejora. Durante 7 horas la confiabilidad hombre-máquina es de 0,1409 (la probabilidad de encontrar un producto sin fallos para el que debe ser desechado es del 14.09%). En el taller de maquinado, los problemas que causaron estos bajos niveles de confiabilidad se atribuyen al insuficiente programa de mantenimiento preventivo y predictivo. Las acciones para la mejora están consideradas en otra investigación. Este tipo de investigación estudia un problema poco estudiado, como es la confiabilidad del sistema hombre-máquina en áreas de maquinado e investiga desde una perspectiva innovadora, además de preparar el campo para nuevos estudios en el sector metalmecánico.

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“…Design of experiments, reliability engineering and statistical quality control methods can make these industries more productive and provide safer, more efficient and more reliable products. Just in this volume we have published a number of potentially useful articles on these topics; for examples, see Chatfield et al 3, Emmett et al 4, Rand and McLinn5, Steenackers et al 6, and Ulusoy et al 7.…”

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confidence: 99%

Innovation and quality technology

2011

Quality & Reliability Eng

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In 2008 I wrote an editorial 'Does Six-Sigma Stifle Innovation?' in response to an article that had appeared in Business Week 1 . I have thought more about this but in a broader context than just six sigma.In the early 1970s about one in four US workers were in manufacturing; today its about one in ten. Only about 30% of US GDP derives from manufacturing. The numbers in Europe are approximately comparable. I dont think that this should be a huge concern. In the early 1800s about 70% of the US workforce was involved in agriculture, compared to less than 5% today. Yet the agriculture productivity of the US is unsurpassed.Most of the really important work now and in the future will be done by knowledge workers. This includes new technology creation, the design and development of products and services based on this new technology, and improvements in productivity in existing technologies. In other words, innovation. I think there are two kinds of innovation; breakthrough innovation that creates new technology, and incremental innovation that improves existing technology to increase productivity. Energy, pharmaceuticals, transportation and communications are examples of areas where breakthrough innovation will lead to economic growth and development. Automobile and medical device manufacturing are examples of incremental innovation.Quality technology should play a major role in this. Unfortunately, quality technology is most often thought of in terms of its application to manufacturing. But many aspects of quality technology are essentially knowledge generation tools (designed experiments are an obvious example), so with a shift in focus the profession should be able to position itself to play a key role in the knowledge economy. Quality technology and the associated statistical techniques are an integral part of the general process of innovation. Let me give a few examples.Almost all businesses use computers for scheduling, inventory management, supply chain management, accounting, customer record-keeping and other related activities. This is a rich source of data that can be mined with statistical tools for information that can be used to determine how these activities can be improved and controlled to provide better value for their customers.Six sigma has already made important contributions to healthcare. This needs to become more widespread to control costs and improve service. Frisén 2 provides an excellent survey of how statistical quality control tools are being used in business, finance, healthcare and public health. Many medical devices collect real-time data on the patient, and with the intelligent use of SPC-like algorithms could allow physicians to instantly monitor the condition of their patients or in some instances for patients to continuously monitor their own health. Fraud detection in finance is another area where the techniques discussed by Frisén have considerable utility.The medical device industry is one that thrives on incremental innovation. So is the emerging solar energy industry. Design of...

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Analysis of field failure data when modeled Weibull slope increases with time

Cited by 5 publications

References 12 publications

Weibull stress distribution for static mechanical stress and its stress/strength analysis

Weibull stress distribution for static mechanical stress and its stress/strength analysis

El Sistema hombre-máquina del área de maquinado y la evaluación de su confiabilidad

Innovation and quality technology

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