Kuei-Hu Chang

A novel efficient approach for DFMEA combining 2-tuple and the OWA operator

Design Failure Mode and Effect Analysis (DFMEA) is the application of the Failure Mode and Effects Analysis (FMEA) method specifically to product design. DFMEA is not only an important risk assessment technique but also a major task for enterprises in implementing production management. The purpose is to ensure that the product can achieve its designed functions under specific operating conditions. Most current DFMEA methods use the Risk Priority Number (RPN) value to evaluate the risk of failure. However, conventional RPN methodology has the serious problem of measurement scales and loses some valued information, which experts have to provide. In order to improve the method of RPN evaluation, this paper proposes a novel technique, combining 2-tuple and the Ordered Weighted Averaging (OWA) operator for prioritization of failures in a product DFMEA. A case of the Color Super Twisted Nematic (CSTN) that has been drawn from a midsized manufacturing factory is presented to further illustrate the proposed approach. After comparing the result that was obtained from the proposed method with the other two listed approaches, it was found that the proposed approach can effectively solve the problem of measurement scales and has not lost any expert to provide the useful information. As a result, stability of the product and process can be assured.

A risk assessment methodology using intuitionistic fuzzy set in FMEA

Most current risk assessment methods use the risk priority number (RPN) value to evaluate the risk of failure. However, conventional RPN methodology has been criticised as having five main shortcomings as follows: (1) the assumption that the RPN elements are equally weighted leads to over simplification; (2) the RPN scale itself has some non-intuitive statistical properties; (3) the RPN elements have many duplicate numbers; (4) the RPN is derived from only three factors mainly in terms of safety; and (5) the conventional RPN method has not considered indirect relations between components. To address the above issues, an efficient and comprehensive algorithm to evaluate the risk of failure is needed. This article proposes an innovative approach, which integrates the intuitionistic fuzzy set (IFS) and the decision-making trial and evaluation laboratory (DEMATEL) approach on risk assessment. The proposed approach resolves some of the shortcomings of the conventional RPN method. A case study, which assesses the risk of 0.15 µm DRAM etching process, is used to demonstrate the effectiveness of the proposed approach. Finally, the result of the proposed method is compared with the listing approaches of risk assessment methods.

Evaluate the orderings of risk for failure problems using a more general RPN methodology.

Risk assessment is a preventive analysis task of product design and the production planning process. The purpose is to assign limited resources to the most serious risk items and ensure that the product can achieve its designed functions under specific operating conditions. Most current failure mode and effects analysis (FMEA) methods use the risk priority number (RPN) value to evaluate the risk of failure. However, conventional RPN methodology has not considered the situation parameter and the relationship between components of a system with respect to its type (direct/indirect) and severity. Therefore, a more general and efficient algorithm to evaluate the risk of failure is needed. This paper proposes a more general RPN methodology, which combines the ordered weighted geometric averaging (OWGA) operator and the decision-making trial and evaluation laboratory (DEMATEL) approach for prioritization of failures in a product FMEA. A case of a thin film transistor liquid crystal display (TFT-LCD) product that has been drawn from a professional liquid crystal display manufacturer is presented to further illustrate the proposed approach. The result of the proposed method is compared with the listing approaches of risk assessment methods.

The reliability of general vague fault-tree analysis on weapon systems fault diagnosis

An algorithm of vague fault-tree analysis is proposed in this paper to calculate fault interval of system components from integrating experts knowledge and experience in terms of providing the possibility of failure of bottom events. We also modify Tanaka et als definition and extend the new usage on vague fault-tree analysis in terms of finding most important basic system component for managerial decision-making. In numerical verification, the fault of automatic gun is presented as a numerical example. For advanced experiment, a fault tree for the reactor protective system is adopted as simulation example and we compare the results with other methods. This paper also develops vague fault tree decision support systems (VFTDSS) to generate fault-tree, fault-tree nodes, then directly compute the vague fault-tree interval, traditional reliability, and vague reliability interval.

Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique

Most of the current failure mode, effects, and criticality analysis (FMECA) methods use the risk priority number (RPN) value to evaluate the risk of failure. However, the traditional RPN methodology has been criticized to have several shortcomings. These shortcomings are addressed in this paper. Therefore, an efficient and simplified algorithm to evaluate the risk of failure is needed. This paper proposes a new approach, which utilizes the intuitionistic fuzzy set ranking technique for reprioritization of failures in a system FMECA. The proposed approach has two major advantages: (1) it resolves some of the shortcomings of the traditional RPN method, and (2) it provides an evaluation of the redundancy place, which can assist the designer in making correct decisions to make a safer and more reliable product design. In numerical verification, an FMECA of a silane supply system is presented as a numerical example. After comparing results from the proposed method and two other approaches, this research found that the proposed approach can reduce more duplicate RPN numbers and get a more accurate, reasonable risk ranking.

ME-OWA based DEMATEL reliability apportionment method

The maximal entropy ordered weighted averaging (ME-OWA)-based decision making trial and evaluation laboratory (DEMATEL) method for reliability allocation has been examined. The assessment results show that most conventional reliability allocation methods have five fundamental problems. The first problem is the measurement scale; while the second problem is that the system allocation factors are not equally weighted to one another, the third problem is that most reliability allocations methods often neglect many important features, such as maintainability and risk issues. The fourth problem is that they do not consider indirect relations between subsystems or components, and the fifth problem is that they do not consider predicted failure rate in the apportionment process. This study evaluated reliability allocation using a fighter aircrafts digital flight control computer (DFLCC). The proposed method offers several benefits compared with current military and commercial approaches. The computational results clearly demonstrate the advantages of the proposed approach for solving the five fundamental problems.

Reliability assessment of an aircraft propulsion system using IFS and OWA tree

In conventional system reliability analysis, the failure probabilities of components of a system are treated as exact values when the failure probability of the entire system is estimated. However, it may be difficult or even impossible to precisely determine the failure probabilities of components as early as the product design phase. Therefore, an efficient and simplified algorithm to assess system reliability is needed. This article proposes a deductive top-down estimation methodology, which combines intuitionistic fuzzy set (IFS) and ordered weighted averaging (OWA) operators to evaluate system reliability. A case of an aircraft propulsion system from an aerospace company is presented to further illustrate the proposed approach. After comparing results from the proposed method and two other approaches, this research found that the proposed approach provides a more accurate and reasonable reliability assessment.

A more general risk assessment methodology using a soft set-based ranking technique

Process failure mode and effects analysis (PFMEA) is used in the high-tech industry to improve a product’s quality and robustness. It is not only an important risk assessment technique but also a valuable task for implementing production management. Its main purpose is to discover and prioritize potential failure modes. Most of the current PFMEA techniques use the risk priority number (RPN) value to evaluate the risk of failure. However, the traditional RPN methodology has a serious problem with regard to measurement scales, does not consider the direct and indirect relationship between potential failure modes and causes of failure, and loses potentially valuable expert-provided information. Moreover, there are unknown, partially known, missing, or nonexistent data identified during the process of collecting data for PFMEA; this increases the difficulty of risk assessment. Issues with incomplete information cannot be fully addressed using the traditional RPN methodology. In order to effectively address this problem, the current paper proposes a novel soft set-based ranking technique for the prioritization of failures in a product PFMEA. For verification of the proposed approach, a numerical example of the Xtal unit PFMEA was adopted. This study also compares the results of the traditional RPN and DEMATEL methods for dealing with incomplete data. The results demonstrate that the proposed approach is preferable for reflecting actual stages of incomplete data in PFMEA. As a result, product and process robustness can be assured.

Applying the concept of exponential approach to enhance the assessment capability of FMEA

Failure modes and effects analysis (FMEA) has been used to identify the critical risk events and predict a system failure to avoid or reduce the potential failure modes and their effect on operations. The risk priority number (RPN) is the classical method to evaluate the risk of failure in conventional FMEA. RPN, which ranges from 1 to 1000, is a mathematical product of three parameters—severity (