An FMEA analysis using grey theory and grey rough sets

This paper presents a hybrid method for detecting the most important failure items as well as the most effective alternative strategy to cope with possible events. The proposed model of this paper uses grey technique to rank various alternatives and FMEA technique to find important faults. The implementation of the proposed method has been illustrated for an existing example on the literature. The results of this method show that the proposed model has been capable of detecting the most trouble making problems with fuzzy logic and finds the most important solution strategy using FMEA technique

Semi-automatic FMEA supporting complex systems with combinations and sequences of failures

Failure Modes and Effects Analysis (FMEA) is a well established safety analysis technique used for the assessment of safety critical engineering systems in the automotive industry. Although FMEA has been shown to be useful, the analysis is typically restricted to the effects of single component failures; even partial analysis of combinations or sequences of multiple failures is in practice considered too complex, laborious and costly to perform. In this paper, we describe a new technique in which FMEAs are semi-automatically built from the topology of a system and component-level specifications of failure data. The proposed technique allows an extended form of combinatorial & sequential FMEA in which assessment of the effects of combinations and sequences of failures becomes feasible and cost effective. We show how this technique can address difficulties encountered in classical FMEA and, drawing from a simplified brake-by-wire example, we show how it can improve the assessment of safety critical automotive systems

An evaluation of failure modes and effects analysis generation method for conceptual design

Failure modes and effects analysis (FMEA) is used in the manufacturing industry to improve product quality and productivity. However, the traditional approach has many shortcomings that affect its effectiveness and limit its usefulness, especially in the early stages of design. Automating the FMEA report generation process seems to answer some of these problems, and there has been much past and on-going research in this area. However, most of the work is limited to specific applications. This paper proposes a method for FMEA generation for a generic application using minimum information during the conceptual design stage. Prototype software has been created for the proposed method. It has been evaluated using case studies from the design and manufacture of two-way radios. The evaluation revealed the feasibility of the proposal, as well as some weaknesses that need further improvement. Generally, the capability of the method to generate FMEA report with minimum information is demonstrated

Towards a Model-Based Systems Engineering Approach for Robotic Manufacturing Process Modelling with Automatic FMEA Generation

YesThe process of generating FMEA following document-centric approach is tedious and susceptible to human error. This paper presents preliminary methodology for robotic manufacturing process modelling in MBSE environment with a scope of automating multiple steps of the modelling process using ontology. This is followed by the reasoning towards automatic generation of process FMEA from the MBSE model. The proposed methodology allows to establish robust and self-synchronising links between process-relevant information, reduce the likelihood of human error, and scale down time expenses

Modeling and analysis of process failures using probabilistic functional model

Failure analysis is an important tool for effective safety management in the chemical process industry. This thesis applies a probabilistic approach to study two failure analysis techniques. The first technique focuses on fault detection and diagnosis (FDD), while the second is on vulnerability analysis of plant components. In formulating the FDD strategy, a class of functional model called multilevel flow modeling (MFM) was used. Since this model is not commonly used for chemical processes, it was tested on a crude distillation unit and validated using a simulation flowsheet implemented in Aspen HYSYS (Version 8.4) to demonstrate its suitability. Within the proposed FDD framework, probabilistic information was added by transforming the MFM model into its equivalent fault tree model to provide the ability to predict the likelihood of component’s failure. This model was then converted into its equivalent Bayesian network model using HUGIN 8.1 software to facilitate computations. Evaluations of the system on a heat exchanger pilot plant highlight the capability of the model in detecting process faults and identifying the associated root causes. The proposed technique also incorporated options for multi – state functional outcomes, in addition to the typical binary states offered by typical MFM model. The second tool proposed was a new methodology called basic event ranking approach (BERA), which measures the relative vulnerabilities of plant components and can be used to assist plant maintenance and upgrade planning. The framework was applied to a case study involving toxic prevention barriers in a typical process plant. The method was compared to some common importance index methodologies, and the results obtained ascertained the suitability of BERA to be used as a tool to facilitate risk based decisions in planning maintenance schedules in a process plant

Developing Methods of Obtaining Quality Failure Information from Complex Systems

The complexity in most engineering systems is constantly growing due to ever-increasing technological advancements. This result in a corresponding need for methods that adequately account for the reliability of such systems based on failure information from components that make up these systems. This dissertation presents an approach to validating qualitative function failure results from model abstraction details. The impact of the level of detail available to a system designer during conceptual stages of design is considered for failure space exploration in a complex system. Specifically, the study develops an efficient approach towards detailed function and behavior modeling required for complex system analyses. In addition, a comprehensive research and documentation of existing function failure analysis methodologies is also synthesized into identified structural groupings. Using simulations, known governing equations are evaluated for components and system models to study responses to faults by accounting for detailed failure scenarios, component behaviors, fault propagation paths, and overall system performance. The components were simulated at nominal states and varying degrees of fault representing actual modes of operation. Information on product design and provisions on expected working conditions of components were used in the simulations to address normally overlooked areas during installation. The results of system model simulations were investigated using clustering analysis to develop an efficient grouping method and measure of confidence for the obtained results. The intellectual merit of this work is the use of a simulation based approach in studying how generated failure scenarios reveal component fault interactions leading to a better understanding of fault propagation within design models. The information from using varying fidelity models for system analysis help in identifying models that are sufficient enough at the conceptual design stages to highlight potential faults. This will reduce resources such as cost, manpower and time spent during system design. A broader impact of the project is to help design engineers identifying critical components, quantifying risks associated with using particular components in their prototypes early in the design process and help improving fault tolerant system designs. This research looks to eventually establishing a baseline for validating and comparing theories of complex systems analysis

Failure Analysis in Conceptual Phase toward a Robust Design: Case Study in Monopropellant Propulsion System

As a system becomes more complex, the uncertainty in the operating conditions increases. In such a system, implementing a precise failure analysis in early design stage is vital. However, there is a lack of applicable methodology that shows how to implement failure analysis in the early design phase to achieve a robust design. The main purpose of this paper is to present a framework to design a complex engineered system resistant against various factors that may cause failures, when design process is in the conceptual phase and information about detailed system and component is unavailable. Within this framework, we generate a population of feasible designs from a seed functional model, and simulate and classified failure scenarios. We also develop a design selection function to compare robust score for candidate designs, and produce a preference ranking. We implement the proposed method on the design of an aerospace monopropellant propulsion system

An Experimental Study on the Influence that Failure Number, Specialization, and Domain have on Confidence in Predicting System Failures

Design reviews are typically used for three types of design activities: 1) identifying errors, 2) assessing the impact of the errors, and 3) suggesting solutions for the errors. This experimental study focuses on understanding the second issue as it relates to the number of errors considered, the existence of controls, and the level of domain familiarity of the assessor. A set of design failures and associated controls developed for a completed industry sponsored project is used as the experimental design problem. Non-domain individuals (psychology class students), domain generalists (first year engineering students), and domain specialists (graduate mechanical students) are provided a set of failure modes and asked to estimate the likelihood that the system would still successfully achieve the stated objectives. Primary results from the study include the following: the confidence level for all domain population decreased significantly as the number of design errors increased (largest p-value=0.0793) and this decrease in confidence is more significant as the design errors increase. The impact on confidence is less when solutions (controls) are provided to prevent the errors (largest p-value=0.0334), the confidence decreased faster for domain general engineers as compared to domain specialists (p= The research presents a study on how estimations are made in design reviews. It answers the question on how individuals assess the performance of systems which is necessary to be addressed in order to evaluate the importance of methods such as design reviews and design review tools (FMEA, DFMEA, FTA) used in design engineering. It addresses the challenges faced by the impact of design errors in the design process and how they affect assessment by different types of designers in predicting successful system performance