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Fuzzy assessment of FMEA for engine systems
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When performing failure mode and effects analysis (FMEA) for quality assurance and reliability improvement, interdependencies among various failure modes with uncertain and imprecise information are very difficult to be incorporated for failure analysis. Consequently, the validity of the results may be questionable. This paper presents a fuzzy-logic-based method for FMEA to address this issue.
A platform for a fuzzy expert assessment is integrated with the proposed system to overcome the potential difficulty in sharing information among experts from various disciplines. The FMEA of diesel engine's turbocharger system is presented to illustrate the feasibility of such techniques.
Introduction
Reliability and quality assurance have been of increasing concern in the automotive industry in recent years [1], [2]. The latest evidence is the publication of the international standard SAE J1739_200006, jointly developed by experts from Daimler Chrysler, Ford and General Motors. This document describes the use of potential failure mode and effects analysis (FMEA) and gives general guidance in the application of different types of FMEA, in particular the design FMEA and the process FMEA. This is an important technique that is used to identify and eliminate known or potential failures to enhance reliability and safety of a simple product or even complex systems [3]. A well-established methodology intended to identify and evaluate the potential failures of a product/process and their effects and to initiate actions that could eliminate or reduce the chance of the potential failures occurring. However, conventional FMEA techniques still impose some limitations on problem solving such as the following:•
It may be difficult or even impossible to precisely determine the probability of failure events in FMEA. Much information in FMEA is expressed in the linguistic way such as ‘likely’, ‘important’ or ‘very high’ etc. In addition, most components or systems degrade over time and have multiple states. An assessment on these states is also often subjective and qualitatively described in natural language such as ‘degradation of performances’, ‘reliability’, and ‘safety’. It is difficult for conventional FMEA to evaluate these linguistic variables.
•
Interdependencies among various failure modes and effects on the same level and different levels of hierarchical structure of an engineering system are not taken into account. It is not likely to combine multiple qualitative assessments and is even more difficult to obtain the probability distributions that several failure modes occur simultaneously using traditional approach.
•
In conventional FMEA, the diversity and ability of the team are the most important considerations, followed by training for the team member. This leads to a high cost. Furthermore, other industrial practitioners usually find it hard to share their experience. This indeed prohibits application of FMEA in a broader scope. Hence, developing a system in which expert's experience can be incorporated for effective automation of the FMEA assessment on computer will be very beneficial.
A lot of research has been carried out to enhance the performance of FMEA in the past decade. Keller and Kara-Zaitri [4] proposed using rules to express the interdependencies among various causes and effects. Bell et al. [5] developed a method of causal reasoning in FMEA. The major advantages of the method are that reasoning is performed in terms of FMEA language (e.g. ‘too small’, ‘high’). However, the method can be used only in cases where the inputs and outputs of a component are known. Wang et al. [6] proposed an approach combining FMEA and the Boolean Representation Method (BRM). It can be used in the case where multiple state variables and feedback loops are involved. In addition, combinations of occurrence of failure modes that affect safety can be studied by inductive BRM. However, it might be difficult to construct the Boolean representation tables for some components of a system especially during early conception and design phases when the relationships between components are far from being clear or hard to be precisely represented. Bowles and Peláez [7] proposed a technique that uses membership function, max–min inference and defuzzification in criticality analysis (CA). On the other hand, the method in Ref. [8] is based on the theories of possibility distribution and probability of fuzzy events to treat uncertainties of the data and multiple failure modes. Nevertheless, the probability of fuzzy events must be known when using the method.
In this paper, an approach of fuzzy-logic-based FMEA is presented. It broadens method in Ref. [7] from only CA to FMEA and constructs a fuzzy assessment system to perform it. It is useful for conducting the FMEA using the information and expert's expertise that is often uncertain or vague in the design phase; in particular, for a mechanical system which usually has no crisp inputs and outputs and the relationships among the failure modes and effects are very complex, subjective and qualitative.
The reasons for employing fuzzy logic [9] are as follows. First, all FMEA-related information is recorded in natural language. It is easy and plausible for fuzzy logic to deal with them because the basis for fuzzy logic is the basis for human communication and fuzzy logic can be built on top of the experience of experts. Second, fuzzy logic allows imprecise data to be used. Therefore, it can easily treat many states of components and system and other fuzzy information included in FMEA. Finally, there is no broad assumption of complete independence of the evidence or ideas to be combined. It is also important for FMEA because experts' qualitative assessment about the relationships of failure modes and effects can be naturally expressed and combined.
The paper will concentrate on design FMEA of mechanical systems and it is organized as follows. In Section 2, the general fuzzy assessment architecture of FMEA is described. A fuzzy-logic-based FMEA modeling is developed in Section 3. A turbocharger system example and assessment system is presented in Section 4. This is followed by some concluding remarks in Section 5.
Introduction
Reliability and quality assurance have been of increasing concern in the automotive industry in recent years [1], [2]. The latest evidence is the publication of the international standard SAE J1739_200006, jointly developed by experts from Daimler Chrysler, Ford and General Motors. This document describes the use of potential failure mode and effects analysis (FMEA) and gives general guidance in the application of different types of FMEA, in particular the design FMEA and the process FMEA. This is an important technique that is used to identify and eliminate known or potential failures to enhance reliability and safety of a simple product or even complex systems [3]. A well-established methodology intended to identify and evaluate the potential failures of a product/process and their effects and to initiate actions that could eliminate or reduce the chance of the potential failures occurring. However, conventional FMEA techniques still impose some limitations on problem solving such as the following:•
It may be difficult or even impossible to precisely determine the probability of failure events in FMEA. Much information in FMEA is expressed in the linguistic way such as ‘likely’, ‘important’ or ‘very high’ etc. In addition, most components or systems degrade over time and have multiple states. An assessment on these states is also often subjective and qualitatively described in natural language such as ‘degradation of performances’, ‘reliability’, and ‘safety’. It is difficult for conventional FMEA to evaluate these linguistic variables.
•
Interdependencies among various failure modes and effects on the same level and different levels of hierarchical structure of an engineering system are not taken into account. It is not likely to combine multiple qualitative assessments and is even more difficult to obtain the probability distributions that several failure modes occur simultaneously using traditional approach.
•
In conventional FMEA, the diversity and ability of the team are the most important considerations, followed by training for the team member. This leads to a high cost. Furthermore, other industrial practitioners usually find it hard to share their experience. This indeed prohibits application of FMEA in a broader scope. Hence, developing a system in which expert's experience can be incorporated for effective automation of the FMEA assessment on computer will be very beneficial.
A lot of research has been carried out to enhance the performance of FMEA in the past decade. Keller and Kara-Zaitri [4] proposed using rules to express the interdependencies among various causes and effects. Bell et al. [5] developed a method of causal reasoning in FMEA. The major advantages of the method are that reasoning is performed in terms of FMEA language (e.g. ‘too small’, ‘high’). However, the method can be used only in cases where the inputs and outputs of a component are known. Wang et al. [6] proposed an approach combining FMEA and the Boolean Representation Method (BRM). It can be used in the case where multiple state variables and feedback loops are involved. In addition, combinations of occurrence of failure modes that affect safety can be studied by inductive BRM. However, it might be difficult to construct the Boolean representation tables for some components of a system especially during early conception and design phases when the relationships between components are far from being clear or hard to be precisely represented. Bowles and Peláez [7] proposed a technique that uses membership function, max–min inference and defuzzification in criticality analysis (CA). On the other hand, the method in Ref. [8] is based on the theories of possibility distribution and probability of fuzzy events to treat uncertainties of the data and multiple failure modes. Nevertheless, the probability of fuzzy events must be known when using the method.
In this paper, an approach of fuzzy-logic-based FMEA is presented. It broadens method in Ref. [7] from only CA to FMEA and constructs a fuzzy assessment system to perform it. It is useful for conducting the FMEA using the information and expert's expertise that is often uncertain or vague in the design phase; in particular, for a mechanical system which usually has no crisp inputs and outputs and the relationships among the failure modes and effects are very complex, subjective and qualitative.
The reasons for employing fuzzy logic [9] are as follows. First, all FMEA-related information is recorded in natural language. It is easy and plausible for fuzzy logic to deal with them because the basis for fuzzy logic is the basis for human communication and fuzzy logic can be built on top of the experience of experts. Second, fuzzy logic allows imprecise data to be used. Therefore, it can easily treat many states of components and system and other fuzzy information included in FMEA. Finally, there is no broad assumption of complete independence of the evidence or ideas to be combined. It is also important for FMEA because experts' qualitative assessment about the relationships of failure modes and effects can be naturally expressed and combined.
The paper will concentrate on design FMEA of mechanical systems and it is organized as follows. In Section 2, the general fuzzy assessment architecture of FMEA is described. A fuzzy-logic-based FMEA modeling is developed in Section 3. A turbocharger system example and assessment system is presented in Section 4. This is followed by some concluding remarks in Section 5.
When performing failure mode and effects analysis (FMEA) for quality assurance and reliability improvement, interdependencies among various failure modes with uncertain and imprecise information are very difficult to be incorporated for failure analysis. Consequently, the validity of the results may be questionable. This paper presents a fuzzy-logic-based method for FMEA to address this issue.
A platform for a fuzzy expert assessment is integrated with the proposed system to overcome the potential difficulty in sharing information among experts from various disciplines. The FMEA of diesel engine's turbocharger system is presented to illustrate the feasibility of such techniques.
Introduction
Reliability and quality assurance have been of increasing concern in the automotive industry in recent years [1], [2]. The latest evidence is the publication of the international standard SAE J1739_200006, jointly developed by experts from Daimler Chrysler, Ford and General Motors. This document describes the use of potential failure mode and effects analysis (FMEA) and gives general guidance in the application of different types of FMEA, in particular the design FMEA and the process FMEA. This is an important technique that is used to identify and eliminate known or potential failures to enhance reliability and safety of a simple product or even complex systems [3]. A well-established methodology intended to identify and evaluate the potential failures of a product/process and their effects and to initiate actions that could eliminate or reduce the chance of the potential failures occurring. However, conventional FMEA techniques still impose some limitations on problem solving such as the following:•
It may be difficult or even impossible to precisely determine the probability of failure events in FMEA. Much information in FMEA is expressed in the linguistic way such as ‘likely’, ‘important’ or ‘very high’ etc. In addition, most components or systems degrade over time and have multiple states. An assessment on these states is also often subjective and qualitatively described in natural language such as ‘degradation of performances’, ‘reliability’, and ‘safety’. It is difficult for conventional FMEA to evaluate these linguistic variables.
•
Interdependencies among various failure modes and effects on the same level and different levels of hierarchical structure of an engineering system are not taken into account. It is not likely to combine multiple qualitative assessments and is even more difficult to obtain the probability distributions that several failure modes occur simultaneously using traditional approach.
•
In conventional FMEA, the diversity and ability of the team are the most important considerations, followed by training for the team member. This leads to a high cost. Furthermore, other industrial practitioners usually find it hard to share their experience. This indeed prohibits application of FMEA in a broader scope. Hence, developing a system in which expert's experience can be incorporated for effective automation of the FMEA assessment on computer will be very beneficial.
A lot of research has been carried out to enhance the performance of FMEA in the past decade. Keller and Kara-Zaitri [4] proposed using rules to express the interdependencies among various causes and effects. Bell et al. [5] developed a method of causal reasoning in FMEA. The major advantages of the method are that reasoning is performed in terms of FMEA language (e.g. ‘too small’, ‘high’). However, the method can be used only in cases where the inputs and outputs of a component are known. Wang et al. [6] proposed an approach combining FMEA and the Boolean Representation Method (BRM). It can be used in the case where multiple state variables and feedback loops are involved. In addition, combinations of occurrence of failure modes that affect safety can be studied by inductive BRM. However, it might be difficult to construct the Boolean representation tables for some components of a system especially during early conception and design phases when the relationships between components are far from being clear or hard to be precisely represented. Bowles and Peláez [7] proposed a technique that uses membership function, max–min inference and defuzzification in criticality analysis (CA). On the other hand, the method in Ref. [8] is based on the theories of possibility distribution and probability of fuzzy events to treat uncertainties of the data and multiple failure modes. Nevertheless, the probability of fuzzy events must be known when using the method.
In this paper, an approach of fuzzy-logic-based FMEA is presented. It broadens method in Ref. [7] from only CA to FMEA and constructs a fuzzy assessment system to perform it. It is useful for conducting the FMEA using the information and expert's expertise that is often uncertain or vague in the design phase; in particular, for a mechanical system which usually has no crisp inputs and outputs and the relationships among the failure modes and effects are very complex, subjective and qualitative.
The reasons for employing fuzzy logic [9] are as follows. First, all FMEA-related information is recorded in natural language. It is easy and plausible for fuzzy logic to deal with them because the basis for fuzzy logic is the basis for human communication and fuzzy logic can be built on top of the experience of experts. Second, fuzzy logic allows imprecise data to be used. Therefore, it can easily treat many states of components and system and other fuzzy information included in FMEA. Finally, there is no broad assumption of complete independence of the evidence or ideas to be combined. It is also important for FMEA because experts' qualitative assessment about the relationships of failure modes and effects can be naturally expressed and combined.
The paper will concentrate on design FMEA of mechanical systems and it is organized as follows. In Section 2, the general fuzzy assessment architecture of FMEA is described. A fuzzy-logic-based FMEA modeling is developed in Section 3. A turbocharger system example and assessment system is presented in Section 4. This is followed by some concluding remarks in Section 5.
Introduction
Reliability and quality assurance have been of increasing concern in the automotive industry in recent years [1], [2]. The latest evidence is the publication of the international standard SAE J1739_200006, jointly developed by experts from Daimler Chrysler, Ford and General Motors. This document describes the use of potential failure mode and effects analysis (FMEA) and gives general guidance in the application of different types of FMEA, in particular the design FMEA and the process FMEA. This is an important technique that is used to identify and eliminate known or potential failures to enhance reliability and safety of a simple product or even complex systems [3]. A well-established methodology intended to identify and evaluate the potential failures of a product/process and their effects and to initiate actions that could eliminate or reduce the chance of the potential failures occurring. However, conventional FMEA techniques still impose some limitations on problem solving such as the following:•
It may be difficult or even impossible to precisely determine the probability of failure events in FMEA. Much information in FMEA is expressed in the linguistic way such as ‘likely’, ‘important’ or ‘very high’ etc. In addition, most components or systems degrade over time and have multiple states. An assessment on these states is also often subjective and qualitatively described in natural language such as ‘degradation of performances’, ‘reliability’, and ‘safety’. It is difficult for conventional FMEA to evaluate these linguistic variables.
•
Interdependencies among various failure modes and effects on the same level and different levels of hierarchical structure of an engineering system are not taken into account. It is not likely to combine multiple qualitative assessments and is even more difficult to obtain the probability distributions that several failure modes occur simultaneously using traditional approach.
•
In conventional FMEA, the diversity and ability of the team are the most important considerations, followed by training for the team member. This leads to a high cost. Furthermore, other industrial practitioners usually find it hard to share their experience. This indeed prohibits application of FMEA in a broader scope. Hence, developing a system in which expert's experience can be incorporated for effective automation of the FMEA assessment on computer will be very beneficial.
A lot of research has been carried out to enhance the performance of FMEA in the past decade. Keller and Kara-Zaitri [4] proposed using rules to express the interdependencies among various causes and effects. Bell et al. [5] developed a method of causal reasoning in FMEA. The major advantages of the method are that reasoning is performed in terms of FMEA language (e.g. ‘too small’, ‘high’). However, the method can be used only in cases where the inputs and outputs of a component are known. Wang et al. [6] proposed an approach combining FMEA and the Boolean Representation Method (BRM). It can be used in the case where multiple state variables and feedback loops are involved. In addition, combinations of occurrence of failure modes that affect safety can be studied by inductive BRM. However, it might be difficult to construct the Boolean representation tables for some components of a system especially during early conception and design phases when the relationships between components are far from being clear or hard to be precisely represented. Bowles and Peláez [7] proposed a technique that uses membership function, max–min inference and defuzzification in criticality analysis (CA). On the other hand, the method in Ref. [8] is based on the theories of possibility distribution and probability of fuzzy events to treat uncertainties of the data and multiple failure modes. Nevertheless, the probability of fuzzy events must be known when using the method.
In this paper, an approach of fuzzy-logic-based FMEA is presented. It broadens method in Ref. [7] from only CA to FMEA and constructs a fuzzy assessment system to perform it. It is useful for conducting the FMEA using the information and expert's expertise that is often uncertain or vague in the design phase; in particular, for a mechanical system which usually has no crisp inputs and outputs and the relationships among the failure modes and effects are very complex, subjective and qualitative.
The reasons for employing fuzzy logic [9] are as follows. First, all FMEA-related information is recorded in natural language. It is easy and plausible for fuzzy logic to deal with them because the basis for fuzzy logic is the basis for human communication and fuzzy logic can be built on top of the experience of experts. Second, fuzzy logic allows imprecise data to be used. Therefore, it can easily treat many states of components and system and other fuzzy information included in FMEA. Finally, there is no broad assumption of complete independence of the evidence or ideas to be combined. It is also important for FMEA because experts' qualitative assessment about the relationships of failure modes and effects can be naturally expressed and combined.
The paper will concentrate on design FMEA of mechanical systems and it is organized as follows. In Section 2, the general fuzzy assessment architecture of FMEA is described. A fuzzy-logic-based FMEA modeling is developed in Section 3. A turbocharger system example and assessment system is presented in Section 4. This is followed by some concluding remarks in Section 5.
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