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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.

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.
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
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
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
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
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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