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

Failure mode and effect analysis generation for conceptual design

Failure Mode and Effect Analysis (FMEA) is a widely used concurrent engineering tool for quality improvement and risk assessment. However, many shortcomings have hindered its effectiveness. The research described here aims to contribute to the implementation of FMEA in conceptual design by eliminating some of these shortcomings. The focus of the work is on the information modelling of FMEA knowledge, and the emphasis is on the avoidance of additional workload for the designer and the encouragement of knowledge reuse. A relational data model has been created to support the automatic generation of the FMEA. This automatic generation replaces the traditional brainstorming process for FMEA report creation. Inputs of failure reports from the factory floor are used for FMEA generation. As an alternative approach, designers can provide the characteristics of the components of their design to generate the FMEA. The user has the final decision on whether the FMEA generated are to be recorded as the final FMEA report. Prototype software has been created to demonstrate the above capabilities. The data model is also intended to support the viewpoints of multiple users, namely, the product designer, the field engineer, the process engineer and the maintenance engineer. Further research is in progress

Failure modes and effects analysis through knowledge modelling

Failure Mode and Effect Analysis (FMEA) is a widely used quality improvement and risk assessment tool in manufacturing. Design and process failures recorded through FMEA provides valuable knowledge for future product and process design. However, the way the knowledge is captured poses considerable difficulties for reuse. This research aims to contribute to the reuse of FMEA knowledge through a knowledge modelling approach. FMEA activities are shifted to the conceptual design stage to avoid costly and difficult design changes at later stages of the design process. An object-oriented approach has been used to create an FMEA model. Functional diagrams have been used for the conceptual model. The FMEA model uses functional reasoning techniques to enable automatic FMEA generation from historical data. The reasoning technique also provides a means for the creation of new knowledge. The automatic generation replaces the traditional brainstorming process for FMEA report creation. The sources of the historical data can be from the previous FMEA, failure reports or from the individual designers

Integrating model checking with HiP-HOPS in model-based safety analysis

The ability to perform an effective and robust safety analysis on the design of modern safety–critical systems is crucial. Model-based safety analysis (MBSA) has been introduced in recent years to support the assessment of complex system design by focusing on the system model as the central artefact, and by automating the synthesis and analysis of failure-extended models. Model checking and failure logic synthesis and analysis (FLSA) are two prominent MBSA paradigms. Extensive research has placed emphasis on the development of these techniques, but discussion on their integration remains limited. In this paper, we propose a technique in which model checking and Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS) – an advanced FLSA technique – can be applied synergistically with benefit for the MBSA process. The application of the technique is illustrated through an example of a brake-by-wire system

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 ful®ls the functions, and used to analyse the safety of the design. FLAME, an automated failure mode and e€ects 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 di€erent 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.

TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system

Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system

Model-Based Safety Analysis

System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical

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

Engineering failure analysis and design optimisation with HiP-HOPS

The scale and complexity of computer-based safety critical systems, like those used in the transport and manufacturing industries, pose significant challenges for failure analysis. Over the last decade, research has focused on automating this task. In one approach, predictive models of system failure are constructed from the topology of the system and local component failure models using a process of composition. An alternative approach employs model-checking of state automata to study the effects of failure and verify system safety properties. In this paper, we discuss these two approaches to failure analysis. We then focus on Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS) - one of the more advanced compositional approaches - and discuss its capabilities for automatic synthesis of fault trees, combinatorial Failure Modes and Effects Analyses, and reliability versus cost optimisation of systems via application of automatic model transformations. We summarise these contributions and demonstrate the application of HiP-HOPS on a simplified fuel oil system for a ship engine. In light of this example, we discuss strengths and limitations of the method in relation to other state-of-the-art techniques. In particular, because HiP-HOPS is deductive in nature, relating system failures back to their causes, it is less prone to combinatorial explosion and can more readily be iterated. For this reason, it enables exhaustive assessment of combinations of failures and design optimisation using computationally expensive meta-heuristics. (C) 2010 Elsevier Ltd. All rights reserved