Medical Health Care FMEA

Failure Mode and Effect Analysis and Risk Analysis for Medical Applications
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Performing FMEA Using Ontologies
The paper aims to introduce an approach that integrates a technique of knowledge engineering (Ontologies) and a technique of quality engineering (Failure Mode and Effects Analysis). An approach will be set up that shows the potentials of combining IT-based systems of knowledge and quality engineering. Particularly with regard to the quality engineering technique, the paper aims to demonstrate the advantages of this approach.
Expanded FMEA (EFMEA)
The main FMEA objective is the identification of ways in which a product, process or service fail to meet critical customer requirements, as well as the ranking and prioritization of the relative risks associated with specified failures. The effectiveness of prioritization can be significantly improved by using a simple graphical tool, as described by the authors. Evaluation of the adequacy of correction actions proposed to improve product/process/service, and the prioritization of these actions, can be supported by implementing the procedure proposed here, which is based on the evaluation of correction action feasibility. The procedure supports evaluation of both the feasibility of a corrective action implementation and impact of the action taken on failure mode.
Using FMEA for early robustness analysis of Web-based systems
Time pressure and quality issues are two main challenges facing today’s web development professionals. To achieve quick development of high quality systems, a lot of methods and techniques have been proposed. A widely recognized strategy in current practice is to emphasize early quality assurance techniques, as the late detection of defects are well known to be expensive and time-consuming. In this paper we take robustness as a critically important quality attribute, and propose a general framework for conducting early robustness analysis for web-based systems, based on Jacobson’s analysis method and FMEA (failure mode and effect analysis).
COST BASED FAILURE MODES AND EFFECTS ANALYSIS (FMEA) FOR SYSTEMS OF ACCELERATOR MAGNETS
The proposed Next Linear Collider (NLC) has a proposed 85% overall availability goal, the availability specifications for all its 7200 magnets and their 6167 power supplies are 97.5% each. Thus all of the electromagnets and their power supplies must be highly reliable or quickly repairable. Improved reliability or repairability comes at a higher cost. We have developed a set of analysis procedures for magnet designers to use as they decide how much effort to exert, i.e. how much money to spend, to improve the reliability of a particular style of magnet. We show these procedures being applied to a standard SLAC electromagnet design in order to make it reliable enough to meet the NLC availability specs. First, empirical data from SLAC’s accelerator failure database plus design experience are used to calculate MTBF for failure modes identified through a FMEA. Availability for one particular magnet can be calculated. Next, labor and material costs to repair magnet failures are used in a Monte Carlo simulation to calculate the total cost of all failures over a 30-year lifetime. Opportunity costs are included. Engineers choose from amongst various designs by comparing lifecycle costs.
Factors Affecting Error and Event Probabilities
The model proposes three components that make human task performers resilient to error: situational awareness, risk perception, and knowledge.
FAILURE MODE IDENTIFICATION THROUGH CLUSTERING ANALYSIS
Research has shown that nearly 80% of the costs and problems are created in product development and that cost and quality are essentially designed into products in the conceptual stage. Currently failure identification procedures (such as FMEA, FMECA and FTA) and design of experiments are being used for quality control and for the detection of potential failure modes during the detail design stage or postproduct launch. Though all of these methods have their own advantages, they do not give information as to what are the predominant failures that a designer should focus on while designing a product. This work uses a functional approach to identify failure modes, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component. In this paper, a statistical clustering procedure is proposed to retrieve information on the set of predominant failures that a function experiences. The various stages of the methodology are illustrated using a hypothetical design example.
Risk –Informed Regulation of Marine Systems Using FMEA
The marine industry is recognizing the powerful techniques that can be used to perform risk analysis of marine systems. One technique that has been applied in both national and international marine regulations is Failure Mode and Effects Analysis (FMEA). This risk analysis tool assumes a failure mode occurs in a system/component through some failure mechanism; the effect of this failure is then evaluated. A risk ranking can be developed in a more detailed variant of FMEA called Failure Mode and Effects Criticality Analysis (FMECA).
Using a Failure Modes, Effects and Diagnostic Analysis (FMEDA) to Measure Diagnostic Coverage in Programmable Electronic Systems.
One of the key issues in the quantitative evaluation of programmable electronic systems is the diagnostic capability of the equipment. This is measured by a parameter called the Coverage Factor, C. This factor can vary widely. The range of possible values is often the subject of great debate. Within limits, the diagnostic coverage factor can be calculated by knowing which component failure modes are detected by diagnostics. An extension of the Failure Modes and Effects Analysis (FMEA) can be used to show this information. This extension, called a Failure Modes, Effects and Diagnostic Analysis can serve as a useful design verification tool as well as a means to provide more precise input to reliability and safety modeling.
Create a Simple Framework To Validate FMEA Performance
Any Green or Black Belt should be able to use the information in this article to explain to management why an FMEA validation process is a valuable tool that will produce both quality improvement and real profit enhancing results.
Using Health Care Failure Mode and Effect Analysis™: The VA National Center for Patient Safety’s Prospective Risk Analysis System
The authors describe the prospective risk assessment method currently being rolled out throughout the Veterans Affairs health care system.
Idea Paper: A Failure Analysis Matrix
The goal of this dissertation is the creation of a usable and useful model for prioritizing solutions to potential failures in information systems. Sincell et al. (1998) pointed out that many benefits of FMEA can be obtained by alternative methods. Hindson, Cook, and Kochhar (1997) stated that many benefits of FMEA can be obtained without formally using the tool. The dissertation's new model will be called a failure analysis matrix (FAM).
New Techniques for Failure Analysis and Test Program Design
This paper discusses a currently proposed technique (sensitivity analysis) for analog fault analysis and describes several new software techniques already in use to perform analysis, diagnosis, and isolation of failures in analog and mixed-signal circuits and systems. Unique methods and algorithms for schematic entry, setting of failure characteristics, definition of test strategies, recording of simulation-based measurements, reduction of time-constrained simulation problems, creation of fault trees, and test sequencing are all discussed.
Prediction and Diagnosis of Propagated Errors in Assembly Systems Using Virtual Factories
Large-scale automated assembly systems are widely used in automotive, aerospace and consumer electronics industries to obtain high quality products in less time. However, one disadvantage of these automated systems is that they are composed of too many working parameters. Since it is not possible to monitor all these parameters during the assembly process, an undetected error may propagate and result in a more critical detected error. In this paper, a unique way of detecting and diagnosing these types of failures by using Virtual Factories is discussed. A Virtual Factory was developed by building and linking several software modules to predict and diagnose propagated errors. A multi-station assembly system was modeled and a previously discussed ‘‘off-line prediction and recovery’’ method was applied. The obtained results showed that this method is capable of predicting propagated errors, which are too complex to solve for a human expert.
Combining Functional and Structural Reasoning for Safety Analysis of Electrical Designs
Increasing complexity of design in automotive electrical systems has been paralleled by increased demands for analysis of the safety and reliability aspects of those designs. Such demands can place a great burden on the engineers charged with carrying out the analysis. This paper describes how the intended functions of a circuit design can be combined with a qualitative model of the electrical circuit that fulfils the functions, and used to analyse the safety of the design. FLAME, an automated failure mode effects analysis system based on these techniques, is described in detail. FLAME has been developed over several years, and is capable of composing an FMEA report for many different electrical subsystems. The paper also addresses the issue of how the use of functional and structural reasoning can be extended to sneak circuit analysis and fault tree analysis.
SCENARIO-BASED FMEA: A LIFE CYCLE COST PERSPECTIVE
Failure Modes and Effects Analysis (FMEA) is a method to identify and prioritize potential failures of a product or process. The traditional FMEA uses three factors, Occurrence, Severity, and Detection, to determine the Risk Priority Number (RPN). This paper addresses two major problems with the conventional FMEA approach: 1) The Detection index does not accurately measure contribution to risk, and 2) The RPN is an inconsistent risk-prioritization technique. The authors recommend two deployment strategies to address these shortcomings: 1) Organize the FMEA around failure scenarios rather than failure modes, and 2) Evaluate risk using probability and cost. The proposed approach uses consistent and meaningful risk evaluation criteria to facilitate life cost-based decisions.
KEYWORDS: FMEA, FMECA, Risk Priority Number (RPN), reliability, risk management
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Medical Health Care FMEA

Failure Mode and Effect Analysis and Risk Analysis for Medical Applications
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