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FMEA - papers : page 1
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An Alternative Software
Reliability Assessment
UML software development tools facilitate computer aided reliability
assessment based on severity of potential failure effects and
effectiveness of protection provisions. This assessment is more widely
applicable than one based on failure rate. |
SYSTEM BEHAVIOR MODELING AS A
BASIS FOR ADVANCED FAILURE MODES AND EFFECTS ANALYSIS
This paper presents a method for developing a device behavior model to
enhance reliability at the early stages of conceptual design. The model
facilitates a semi-automated advanced failure modes and effects analysis
(FMEA). The model performs analyses and simulations of device behavior,
reasons about conditions that depart from desired behaviors, and
analyzes the results of those departures. The proposed method rigorously
specifies pre- and post-conditions, yet is flexible in the syntax of
device operation. The paper shows how the method can capture failures
normally missed by existing FMEA methods. An automatic ice maker serves
as an example application. |
ADVANCED FAILURE MODES AND
EFFECTS ANALYSIS USING BEHAVIOR MODELING
This paper presents a systematic method applicable at the early stages
of design to enhance life-cycle quality of ownership: Advanced Failure
Modes and Effect Analysis (AFMEA). The proposed method uses behavior
modeling to simulate device operations and helps identify failure and
customer dissatisfaction modes beyond component failures. The behavior
model reasons about conditions that cause departures from normal
operation and provides a framework for analyzing the consequences of
failures. The paper shows how Advanced FMEA applies readily to the early
stages of design and captures failure modes normally missed by
conventional FMEA. The result is a systematic method capable of
capturing a wider range of failure modes and effects early in the design
cycle. An automatic ice maker from a domestic refrigerator serves as an
illustrative example. KEYWORDS: behavior modeling, FMEA, reliability |
ADVANCED FMEA USING META
BEHAVIOR MODELING FOR CONCURRENT DESIGN OF PRODUCTS AND CONTROLS
This paper presents the use of Advanced Failure Modes and Effects
Analysis (AFMEA) as a methodology for the concurrent design of
electro-mechanical products and their control systems. The past two
years have seen the extension of AFMEA to simulate dynamic changes of
device operations using meta-behavior modeling. This approach can help
engineers identify failure modes associated with controls and their
interaction with physical systems and drive system design toward more
reliable solutions. The proposed method uses behavior modeling to map
control functions to physical entities and identifies failure modes as
the departure from intended control functions. AFMEA provides a
framework for controls and hardware developers to discuss and understand
the relationship between sub-systems, controls, and overall system
performance. An example of a power generation system illustrates how
AFMEA applies to the early stages of layout and controls design.
KEYWORDS: behavior modeling, FMEA, reliability, concurrent engineering,
systems engineering |
ADVANCED FAILURE MODES AND
EFFECTS ANALYSIS OF COMPLEX PROCESSES
This paper presents the use of Advanced Failure Modes and Effects
Analysis (AFMEA) as a methodology to analyze manufacturing process
reliability. The proposed method applies to early process design and
seeks to improve product quality, process efficiency, and time to
market. The method uses behavior modeling to relate process functions,
performance state variables, and physical entities. The model can be
used to define process failures explicitly and provides a framework for
assessing causes and effects. An example of a precision turning
operation illustrates how AFMEA applies to the analysis of manufacturing
processes. A pilot analysis of an ultrasonic inspection process revealed
that AFMEA is comprehensive and adaptable to other processes. Ongoing
work for AFMEA is developing deployment strategies for minimal time
burden and links to embedded error proofing. KEYWORDS: behavior
modeling, process FMEA, reliability |
Automating the Failure Modes and Effects
Analysis of Safety Critical Systems
Proceedings of the Eighth IEEE International Symposium on High Assurance
Systems Engineering (HASE’04) |
Using FMEA for early robustness analysis
of Web-based systems
Proceedings of the 28th Annual International Computer Software and
Applications Conference (COMPSAC’04) |
Use of FMEA on moisture problems in
buildings
Building Physics 2002 |
Function-directed Electrical Design
Analysis
Functional labels provide a simple but very reusable way for defining
the functionality of a system and for making use of that knowledge.
Unlike more complex functional representation schemes, these labels can
be efficiently linked to a behavioral simulator to interpret the
simulation in a way that is meaningful to the user. They are also simple
to specify, and highly reusable with different behavioral
implementations of the system's functions. This claim has been
substantiated by the development of the FLAME application, a practical
automated design analysis tool in regular use at several automotive
manufacturers. The combination of functional labels and behavioral
simulator can be employed for a variety of tasks – simulation, failure
mode and effects analysis (FMEA), sneak circuit analysis, design
verification, diagnostic candidate generation – producing results that
are very valuable to engineers and presented in terms that are easily
understood by them. The utility of functional labels is illustrated in
this paper for the domain of car electrical systems, with links to a
qualitative circuit simulator. In this domain, functional labels provide
a powerful way of interpreting the behavior of the circuit simulator in
terms an engineer can understand. Keywords: functional reasoning;
qualitative reasoning; automotive applications; FMEA; sneak circuit
analysis; design verification; |
PATIENT SAFETY Optimizing FMEA
and RCA efforts in health care
ASHRM JOURNAL 2004 VOL . 24 NO. 3 |
Reliability-Centered Maintenance
Planning based on Computer-Aided FMEA
For proper management of life cycle of machines and manufacturing
facilities, it is important to perform appropriate maintenance
operations, and to keep machine status for better reuse and recycling
opportunity. For this purpose, a virtual maintenance system is very
effective, where facility life cycle model is constructed in computer,
and reliability and availability of machines are predicted based on
usage deterioration modelling. FMEA(Failure Mode and Effect Analysis) is
a powerful method to extensively investigate possible machine failure
and functional deterioration, and to predict reliability. However it is
very time-consuming and tedious to perform FMEA by conventional manual
method. In this paper, computer aided FMEA is proposed, and its
theoretical basis is discussed. An extended product model is introduced,
where possible machine failure information is added to describe used
machine status. By applying generic behaviour simulation to extended
product models, it is possible to detect abnormal or mal-behaviour of
machines under used conditions. Based on this behaviour analysis and
extended product models, FMEA process can be performed by computer-aided
manner, and can be very efficient to avoid laborious work and possible
errors. Based on FMEA results, maintenance planning can be evaluated by
simulating life cycle operations of machines and by predicting
reliability during operation. For validating the proposed computer-aided
FMEA method, several experiments are performed for mechatronics
products.
Keywords: Maintenance, Reliability, FMEA |
A DEVELOPMENT OF
HAZARD ANALYSIS TO AID SOFTWARE DESIGN
This paper describes a technique for software safety analysis which has
been developed with the specific aim of feeding into and guiding design
development. The method draws on techniques from the chemical
industries’ Hazard and Operability (HAZOP) analysis, combining this
withwork on software failure classification to provide a structured
approach to identifying the hazardous failure modes of new software. |
Life Cost-Based FMEA Using Empirical
Data
Failure Mode and Effect Analysis (FMEA) is a design tool that helps
designers identify risks. The traditional FMEA involves ambiguity with
the definition of risk priority number: the product of occurrence,
detection difficulty, and severity subjectively measured in a 1 to 10
range. Life-cost Based FMEA alleviates this ambiguity by using the
estimated cost of failures. Yet, the methods still relies on judgment of
experts in determining variables such as frequency, detection time,
fixing time, delay time, and parts cost. To resolve this subjectivity,
this paper proposes a systematic use of empirical data for applying
life-cost-based FMEA. A case study of a large scale particle accelerator
shows the advantages of the proposed approach in predicting life cycle
failure cost, measuring risk and planning preventive, scheduled
maintenance and ultimately improving up-time. Keywords: FMEA, Life
Cost-Based FMEA, Empirical Data, Failure Cost |
Life Cost-Based FMEA Incorporating Data
Uncertainty
Failure Modes and Effects Analysis (FMEA) is a design tool that
mitigates risks during the design phase before they occur. Although many
industries use the current FMEA technique, it has many limitations and
problems. Risk is measured in terms of Risk Priority Number (RPN) that
is a product of occurrence, severity, and detection difficulty.
Measuring severity and detection difficulty is very subjective and with
no universal scale. RPN is also a product of ordinal variables, which is
not meaningful as a proper measure. This paper addresses these
shortcomings and introduces a new methodology, Life Cost-Based FMEA,
which measures risk in terms of cost. The ambiguity of detection
difficulty and severity is resolved by measuring these in terms of time
loss. Life Cost-Based FMEA is useful for comparing and selecting design
alternatives that can reduce the overall life cycle cost of a particular
system. Next, a Monte Carlo simulation is applied to the Cost-Based FMEA
to account for the uncertainties in: detection time, fixing time,
occurrence, delay time, down time, and model complex scenarios. This
paper compares and contrasts these three different FMEAs: RPN, Life
Cost-based point estimation, and Life Cost-Based using Monte Carlo
simulation for data uncertainty.
Keywords: FMEA, Life Cost-Based FMEA, Monte Carlo Simulation, Failure
Cost |
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