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Evaluating the failure risk with and without failure data
Kim Kyungmee O.,Zuo Ming J. 한국통계학회 2022 Journal of the Korean Statistical Society Vol.51 No.1
Traditionally, the risk priority number (RPN) is used to compute the failure risk by multiplying occurrence, detection, and severity factors. Claiming that the key feature of multiplying the three factors together to get the RPN is a limitation of this method, existing studies have developed the multiple criteria decision making (MCDM) approach. In this paper, we first show that the multiplication of the three factors is indeed useful not only for evaluating the failure risk based on the trade-off between the improvement cost and risk reduction but also for identifying an appropriate action to reduce the risk of a fixed failure only if failure data is available for evaluating each of the three factors. We then develop a modified method to use the well-established multiplication operation even when each factor is evaluated by an expert. A numerical example is presented to illustrate the advantage of the modified method over the previous MCDM approach in determining the effectiveness of action plans for system risk reduction when only qualitative data is available.
Huang Hong Zhong,Tian Zhi Gang,Zuo Ming J. The Korean Society of Mechanical Engineers 2005 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.19 No.5
The preliminary design optimization of multi-stage spur gear reduction units has been a subject of considerable interest, since many high-performance power transmission applications (e.g., automotive and aerospace) require high-performance gear reduction units. There are multiple objectives in the optimal design of multi-stage spur gear reduction unit, such as minimizing the volume and maximizing the surface fatigue life. It is reasonable to formulate the design of spur gear reduction unit as a multi-objective optimization problem, and find an appropriate approach to solve it. In this paper an interactive physical programming approach is developed to place physical programming into an interactive framework in a natural way. Class functions, which are used to represent the designer's preferences on design objectives, are fixed during the interactive physical programming procedure. After a Pareto solution is generated, a preference offset is added into the class function of each objective based on whether the designer would like to improve this objective or sacrifice the objective so as to improve other objectives. The preference offsets are adjusted during the interactive physical programming procedure, and an optimal solution that satisfies the designer's preferences is supposed to be obtained by the end of the procedure. An optimization problem of three-stage spur gear reduction unit is given to illustrate the effectiveness of the proposed approach.
Hong-Zhong Huang,Zhi-Gang Tian,Ming J. Zuo 대한기계학회 2005 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.19 No.5
The preliminary design optimization of multi-stage spur gear reduction units has been a subject of considerable interest. since many high-performance power transmission applications (e.g.. automotive and aerospace) require high-performance gear reduction units. There are multiple objectives in the optimal design of multi-stage spur gear reduction unit. such as minimizing the volume and maximizing the surface fatigue life. It is reasonable to formulate the design of spur gear reduction unit as a multi-objective optimization problem. and find an appropriate approach to solve it. In this paper an interactive physical programming approach is developed to place physical programming into an interactive framework in a natural way. Class functions. which are used to represent the designer's preferences on design objectives, are fixed during the interactive physical programming procedure. After a Pareto solution is generated, a preference offset is added into the class function of each objective based on whether the designer would like to improve this objective or sacrifice the objective so as to improve other objectives. The preference offsets are adjusted during the interactive physical programming procedure. and an optimal solution that satisfies the designer's preferences is supposed to be obtained by the end of the procedure. An optimization problem of three-stage spur gear reduction unit is given to illustrate the effectiveness of the proposed approach.