FMEA - papers : page 1
Other FMEA Sources
|
|
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 |
| Continue on next page |
|
|