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No / Enhancement in productivity and cost effectiveness of high value manufacturing requires a process based management strategy. This paper introduces a Manufacturing Failure Mode Avoidance (MFMA) framework based on 4 high level process steps and underpinned by a sequence of engineering and process analysis tools to support a structured function-based decomposition of complex manufacturing processes and a continuous flow of information towards the development of robust control plans. The approach draws from experience in the automotive industry, where Failure Mode Avoidance (FMA) has been strategically adopted to achieve a step change in the effectiveness of business and engineering processes associated with the product creation processes. The paper presents a case study of the deployment of the MFMA framework to an aircraft manufacturing process followed by a broader discussion of the strength of the approach and its generic applicability to complex high value manufacturing engineering.

Equipment behavior is often discussed in terms of age and use. For example, an automobile is frequently referred to 3 years old with 30,000 miles. Bivariate failure modeling provides a framework for studying system behavior as a function of two variables. This is meaningful when studying the reliability/availability of systems and equipment. This thesis extends work done in the area of bivariate failure modeling. Four bivariate failure models are selected for analysis. The study includes exploration of bivariate random number generation. The random data is utilized in estimating the bivariate renewal function and bivariate availability function. The two measures provide insight on system behavior characterized by multiple variables. A method for generating bivariate failure and repair data is developed for each model. Of the four models, two represent correlated random variables; the other two, stochastic functionally dependent variables. Also, methods of estimating the bivariate renewals function and bivariate availability function are constructed. The bivariate failure and repair data from the four failure models is incorporated into the estimation processes to study various failure scenarios. / Master of Science

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ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / Current failure analysis practices use diagnostic technology developed over the past 100 years of designing and manufacturing electrical and mechanical equipment to identify root cause of equipment failure requiring expertise with the equipment under analysis. If the equipment that failed had telemetry embedded, prognostic algorithms can be used to identify the deterministic behavior in completely normal appearing data from fully functional equipment used for identifying which equipment will fail within 1 year of use, can also identify when the presence of deterministic behavior was initiated for any equipment failure.

Diss. (sammanfattning) Stockholm : Karol. inst., 2005. / Härtill 5 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2006. / Härtill 5 uppsatser.