A failure modes and mechanisms naming taxonomy

O’Halloran, Bryan M.; Stone, Robert B.; Tumer, Irem Y.

doi:10.1109/rams.2012.6175455

Cited by 19 publications

(8 citation statements)

References 13 publications

(16 reference statements)

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“…For example, Shappel's Human Factors Analysis and Classification System (HFACS) [5] provides a detailed review of issues related to human such as complacency, distraction and confusion; but does not focus on technology issues that can also cause uncertainty. His work can be complemented by O'Halloran's taxonomy of Failure Mode/Mechanisms Distribution (FMD) [6] that lists the possible safety-critical issues resulted from technical properties such as kinetic, chemical and electrical. In a different study, Endsley's taxonomy of situation awareness error [7] focuses on information and decision making, which provides another dimension of causal factors.…”

Section: Reference Of Causal Paths To Recognise Unknown Uncertaintymentioning

confidence: 99%

Incorporating Epistemic Uncertainty into the Safety Assurance of Socio-Technical Systems

Leong¹,

Kelly²,

Alexander³

2017

Electron. Proc. Theor. Comput. Sci.

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In system development, epistemic uncertainty is an ever-present possibility when reasoning about the causal factors during hazard analysis. Such uncertainty is common when complicated systems interact with one another, and it is dangerous because it impairs hazard analysis and thus increases the chance of overlooking unsafe situations. Uncertainty around causation thus needs to be managed well. Unfortunately, existing hazard analysis techniques tend to ignore unknown uncertainties, and system stakeholders rarely track known uncertainties well through the system lifecycle. In this paper, we outline an approach to managing epistemic uncertainty in existing hazard analysis techniques by focusing on known and unknown uncertainty. We have created a reference populated with a wide range of safety-critical causal relationships to recognise unknown uncertainty, and we have developed a model to systematically capture and track known uncertainty around such factors. We have also defined a process for using the reference and model to assess possible causal factors that are suspected during hazard analysis. To assess the applicability of our approach, we have analysed the widely-used MoDAF architectural model and determined that there is potential for our approach to identify additional causal factors that are not apparent from individual MoDAF views. We have also reviewed an existing safety assessment example (the ARP4761 Aircraft System analysis) and determined that our approach could indeed be incorporated into that process. We have also integrated our approach into the STPA hazard analysis technique to demonstrate its feasibility to incorporate into existing techniques. It is therefore plausible that our approach can increase safety assurance provided by hazard analysis in the face of epistemic uncertainty.

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Section: Reference Of Causal Paths To Recognise Unknown Uncertaintymentioning

confidence: 99%

Incorporating Epistemic Uncertainty into the Safety Assurance of Socio-Technical Systems

Leong¹,

Kelly²,

Alexander³

2017

Electron. Proc. Theor. Comput. Sci.

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“…McNelles et al [10] classified system failures by following the structure hierarchy of machines, and assigned the failure modes which own the similar effect and detection to the same group. Based on the function levels of equipment, O'Halloran et al [11] classified the failure mechanisms into three levels and described failures by using the original environment, failure mechanism, etc. Saxena et al [12] proposed an encoding method for failure modes by the components and functions of equipment, which saves plenty of effort for the determination of failure modes.…”

Section: Introductionmentioning

confidence: 99%

“…For the FMEA work of mechanical systems, it is needed to find out all the potential failure modes, causes and effects of all levels top-down according to the construction of machines [25]- [27]. Since a current failure mode can be not only the failure cause of the upper level but also the failure effect of the lower level, there is much repetitive work in the reliability analysis [11]. Because there is no strict demarcation between failure modes and failure causes, they can always convert to each other.…”

Section: Introductionmentioning

confidence: 99%

A More Reasonable Definition of Failure Mode for Mechanical Systems Using Meta-Action

Zhang

Ran

2019

IEEE Access

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Identification of failure modes is a vital work in the process of failure mode and effect analysis (FMEA). Since the traditional failure mode is defined as a specific function failure or a part failure, there are uncountable failure modes for mechanical products. Then, it is too hard to list or predict all of the potential failure modes when we do FMEA on a machine. Besides, the same failure can be defined as a failure mode or a failure cause casually, which makes the analysis work stuck. To solve these problems, we propose a meta-action failure mode in this paper. A machine is decomposed into meta-action units based on the meta-action concept, and the abnormal motion performance of the unit is defined as the meta-action failure mode. All the potential failure modes of meta-actions and the expressions are given in theory. To verify the practicality and advantages of the proposed method, an illustrative example is provided. The results show that the meta-action failure mode can overcome these problems in traditional failure modes and simplify the FMEA process. INDEX TERMS Failure mode and effect analysis (FMEA), reliability analysis, motion failure, meta-action, mechanical product.

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“…This means that the failure modes are generally related to the performance requirements of the system, as Tinga [3] states. Performance can have the following classes [4]:  Intermittent no/full operation means that the failure only causes random occasional periods where functionality is completely affected.  Intermittent degraded/full operation means that the failure only causes random occasional periods where functionality is partially affected.…”

Section: Defining Failurementioning

confidence: 99%

“…Analysis information systems provide access to a series of databases and small models. 4. Accounting models calculate the consequences of planned actions on the basis of accounting definitions.…”

Section: Dss Typesmentioning

confidence: 99%

FAME-X : failure mechanism identification expert system

Karampelas¹

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Dedicated to my parents, who taught me what personal sacrifice is, to my grandfather who taught me what pride is and to my sister who taught me what strength is.Αφιερώνεται στους γονείς μου που μου δίδαξαν τι σημαίνει αυτοθυσία, στον παππού μου που μου δίδαξε τι σημαίνει περηφάνια και στην αδερφή μου που μου δίδαξε τι σημαίνει δύναμη i SUMMARYAlthough maintenance tries to prevent them, failures regularly occur in practice. Prevention prerequisites insight on "how" a failure occurs. Failure mechanisms (FM) describe how components fail. However, operators or technicians as the first people to deal with a failure, normally lack the knowledge to assess FM that act on a material level. Current failure reporting is typically done by entering a failure type/failure mode/cause manually or selecting from a drop-down menu. However, this process has been reported as problematic by several industries. The current thesis attempts to propose a treatment for the above problematic area which is addressed in the first chapter. Many companies face the same failure reporting problem. Several have been approached to discuss their needs and the means to satisfy them. Chapter 2 presents the stakeholders of this project and identifies their needs in different levels. The treatment proposed here is the FAME-X Expert System (ES) which is designed to assist operators in assessing the appropriate FM. Operators can access FAME-X from their mobile devices via the Web and perform an on-site failure analysis. Chapter 3 introduces the idea behind the ES which is simply that failure analysis expertise is transferred from a human to a computer in the form of heuristics. This knowledge is then stored in the computer and users call upon the computer for specific advice as needed. The computer asks questions regarding failure diagnostic characteristics, makes inferences and arrives at a specific conclusion. Then, like a human consultant, it gives advices on the most dominant FM given the provided evidence and explains, if necessary, the logic behind the advice. Chapter 4 defines the design cycle of FAME-X which is composed by the following tasks: problem investigation, treatment design and treatment validation. The structure of the thesis corresponds to these tasks. The content of Chapters 1 until 3 is dedicated to problem investigation. The rest of the chapters are addressing the two remaining tasks. The actual design of FAME-X (i.e. treatment design) starts in Chapter 5 where the requirements for the design are set. FAME-X system is based on an extensive study of failure diagnostic characteristics and their contribution to FM. In contrast to failure modes, the proposed FM approach enhances the decision making upon proper remedial actions. Chapter 6 formulates the failure analysis knowledge which is then transformed into heuristics in order to be able to be inserted into the computer. The detailed design of the computer is presented in Chapter 8 where the various modules comprising an ES are developed an intergraded. This chapter concludes the tre...

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A failure modes and mechanisms naming taxonomy

Cited by 19 publications

References 13 publications

Incorporating Epistemic Uncertainty into the Safety Assurance of Socio-Technical Systems

Incorporating Epistemic Uncertainty into the Safety Assurance of Socio-Technical Systems

A More Reasonable Definition of Failure Mode for Mechanical Systems Using Meta-Action

FAME-X : failure mechanism identification expert system

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