A Step Toward Risk Mitigation During Conceptual Product Design: Component Selection for Risk Reduction

The objective of this article is to introduce a method that will mitigate product risks during the conceptual design phase by identifying design variables that affect product failures. By using this comprehensive, step-by-step process that combines existing techniques in a new way, designers can begin with a simple functional model and emerge from the conceptual design phase with specific components selected with many risks already mitigated. The risk in early design (RED) method plays a significant role in identifying failure modes by functions, and these modes are then analyzed through modeling equations or lifespan analyses, in such a manner that emphasizes variables under the designers’ control. With the valuable insight this method provides, informed decisions can be made early in the process, thereby eliminating costly changes later on.     

Conceptual design of sacrificial sub-systems: failure flow decision functions

This paper presents a method to conceptually model sacrificing non-critical sub-systems, or components, in a failure scenario to protect critical system functionality through a functional failure modeling technique. Understanding the potential benefits and drawbacks of choosing how a failure is directed in a system away from critical sub-systems and toward sub-systems that can be sacrificed to maintain core functionality can help system designers to design systems that are more likely to complete primary mission objectives despite failure events. Functional modeling techniques are often used during the early stage of conceptual design for complex systems to provide a better understanding of system architecture. A family of methods exists that focuses on the modeling of failure initiation and propagation within a functional model of a system. Modeling failure flow provides an opportunity to understand system failure propagation and inform system design iteration for improved survivability and robustness. Currently, the ability to model failure flow decision-making is missing from the family of function failure and flow methodologies. The failure flow decision function (FFDF) methodology presented in this paper enables system designers to model failure flow decision-making problems where functions and flows that are critical to system operation are protected through the sacrifice of less critical functions and flow exports. The sacrifice of less critical system functions and flows allows for mission critical functionality to be preserved, leading to a higher rate of mission objective completion. An example of FFDF application in a physical design is a non-critical peripheral piece of electrical hardware being sacrificed during an electrical surge condition to protect critical electronics necessary for the core functionality of the system. In this paper, a case study of the FFDF method is presented based on a Sojourner class Mars Exploration Rover (MER) platform.     

Common cause failure analysis of cyber–physical systems situated in constructed environments

While cyber–physical system sciences are developing methods for studying reliability that span domains such as mechanics, electronics and control, there remains a lack of methods for investigating the impact of the environment on the system. External conditions such as flooding, fire or toxic gas may damage equipment and failing to foresee such possibilities will result in invalid worst-case estimates of the safety and reliability of the system. Even if single component failures are anticipated, abnormal environmental conditions may result in common cause failures that cripple the system. This paper proposes a framework for modeling interactions between a cyber–physical system and its environment. The framework is limited to environments consisting of spaces with clear physical boundaries, such as power plants, buildings, mines and urban underground infrastructures. The purpose of the framework is to support simulation-based risk analysis of an initiating event such as an equipment failure or flooding. The functional failure identification and propagation (FFIP) framework is extended for this purpose, so that the simulation is able to detect component failures arising from abnormal environmental conditions and vice versa: Flooding could be caused by a failure in a pipe or valve component. As abnormal flow states propagate through the system and its environment, the goal of the simulation is to identify the system-wide cumulative effect of the initiating event and any related common cause failure scenario. FFIP determines this effect in terms of degradation or loss of the functionality of the system. The method is demonstrated with a nuclear reactor’s redundant coolant supply system.     

A failure analysis method for designing highly reliable product-service systems

Recently, product-service systems (PSSs), which create value by integrating a physical product and a service, have been attracting attention. In PSSs, it is critical for a provider to offer highly reliable products and services. To do so, the provider needs to effectively and efficiently detect possible failures, and then, take adequate measures against them in the conceptual design stage. However, in current studies on product failure analysis, service aspects are not covered in analyzing failure causes and developing measures. On the other hand, product aspects are hardly considered in existing methods of service failure analysis. To fill the gap, this paper proposes a method for failure analysis in PSS design called PSS failure mode and effect analysis (PSS FMEA). Especially, this paper extends the framework of FMEA, and then, a procedure for PSS FMEA is introduced so that designers can analyze failures and develop measures in consideration of both product and service aspects. Furthermore, the proposed method supports designers in finding new business opportunities. The proposed method was applied to a real offering of products and services by a cleaning machine provider and found effective.     

Improving the Reliability in the Next Generation of US Army Platforms through Physics of Failure Analysis

Published studies and audits have documented that a significant number of U.S. Army systems are failing to demonstrate established reliability requirements. In order to address this issue, the Army developed a new reliability policy in December 2007 which encourages use of cost-effective reliability best practices. The intent of this policy is to improve reliability of Army systems and material, which in turn will have a significant positive impact on mission effectiveness, logistics effectiveness and life-cycle costs. Under this policy, the Army strongly encourages the use of Physics of Failure (PoF) analysis on mechanical and electronics systems. At the US Army Materiel Systems Analysis Activity, PoF analyses are conducted to support contractors, program managers and engineers on systems in all stages of acquisition from design, to test and evaluation (T&E) and fielded systems. This article discusses using the PoF approach to improve reliability of military products. PoF is a science-based approach to reliability that uses modeling and simulation to eliminate failures early in the design process by addressing root-cause failure mechanisms in a computer-aided engineering environment. The PoF approach involves modeling the root causes of failure such as fatigue, fracture, wear, and corrosion. Computer-aided design tools have been developed to address various loads, stresses, failure mechanisms, and failure sites. This paper focuses on understanding the cause and effect of physical processes and mechanisms that cause degradation and failure of materials and components. A reliability assessment case study of circuit cards consisting of dense circuitry is discussed. System level dynamics models, component finite element models and fatigue-life models were used to reveal the underlying physics of the hardware in its mission environment. Outputs of these analyses included forces acting on the system, displacements of components, accelerations, stress levels, weak points in the design and probable component life. This information may be used during the design process to make design changes early in the acquisition process when changes are easier to make and are much more cost effective. Design decisions and corrective actions made early in the acquisition phase leads to improved efficiency and effectiveness of the T&E process. The intent is to make fixes prior to T&E which will reduce test time and cost, allow more information to be obtained from test and improve test focus. PoF analyses may be conducted for failures occurring during test to better understand the underlying physics of the problem and identify the root cause of failures which may lead to better fixes for problems discovered, reduced test-fix-test iterations and reduced decision risk. The same analyses and benefits mentioned above may be applied to systems which are exhibiting failures in the field.     

Cognitive map-based system modeling for identifying interaction failure modes

Past few decades have seen an upsurge in failure analysis techniques capable of dealing with reliability issues up front in the early stages of the product development process. Most of these approaches are centered on component-specific failures. However, with the advent of highly complex systems that derive functionalities from multiple physical phenomena domains, more emphasis is required on identifying failures arising due to various system interactions, which is largely absent in existing failure analysis approaches. Owing to the causal nature of system interaction failures, the use of cognitive maps in system modeling and simulation for failure analysis is highly suitable. This paper proposes a structured framework for the development and use of cognitive map-based system models capable of capturing all types of failure modes, including interaction failures. The applicability of the proposed framework is demonstrated with the example of an electric water heater.     

Utility based maintenance analysis using a Random Sign censoring model

Industrial systems subject to failures are usually inspected when there are evident signs of an imminent failure. Maintenance is therefore performed at a random time, somehow dependent on the failure mechanism. A competing risk model, namely a Random Sign model, is considered to relate failure and maintenance times. We propose a novel Bayesian analysis of the model and apply it to actual data from a water pump in an oil refinery. The design of an optimal maintenance policy is then discussed under a formal decision theoretic approach, analyzing the goodness of the current maintenance policy and making decisions about the optimal maintenance time.     

FPGA-Based Multiple-Channel Vibration Analyzer for Industrial Applications in Induction Motor Failure Detection

Early detection of failures in equipment is one of the most important concerns to industry. Many techniques have been developed for early failure detection in induction motors. There is the necessity of low-cost instrumentation for online multichannel measurement and analysis of vibration in the frequency domain, and this could be fixed to the machine for continuous monitoring to provide a reliable continuous diagnosis without needing trained staff. Field-programmable gate arrays (FPGAs) are distinguished by being very fast and highly reconfigurable devices, allowing the development of scalable parallel architectures for multichannel analysis without changing the internal hardware. The novelty of this work is the development of a low-cost FPGA based on a multichannel vibration analyzer; this is capable of providing an automatic diagnosis of the motor state carrying out online continuous monitoring. To test the functionality of the proposed vibration analyzer, three experiments on 746-W (1-hp) induction motors were carried out. Such experiments are intended to detect motor failures such as broken bars, unbalance, and looseness. The obtained results show the overall system performance.      

Mapping function to failure mode during component development

When designing aerospace systems, it is essential to provide crucial failure information for failure prevention. Failure modes and effects types of analyses and prior engineering knowledge and experience are commonly used to determine the potential modes of failures a product might encounter during its lifetime. When new products are being considered and designed, this knowledge and information is expanded upon to help designers extrapolate based on their similarity with existing products and the potential design tradeoffs. In this work, we aim to enhance this process by providing design-aid tools which derive similarities between functionality and failure modes. Specifically, this paper presents the theoretical foundations of a matrix-based approach to derive similarities that exist between different failure modes, by mapping observed failure modes to the functionality of each component, and applies it to a simple design example. The function–failure mode method is proposed to design new products or redesign existing ones with solutions for functions that eliminate or reduce the potential of a failure mode. Electronic Publication     

FMEA—A diagraph and matrix approach

This paper presents a method for failure mode and effects analysis (FMEA) of mechanical and hydraulic systems based on a diagraph and matrix approach. The method takes into account structural as well as functional interaction of the system. This is desirable as failures in these systems are not independent. A failure mode and effects diagraph, derived from the structure of the system, models the effects of failure modes of the system and consists of nodes, subnodes and edges. For efficient computer processing, matrices are defined to represent the diagraph. A function (VCM-F_me or VPF-F_me) characteristic of the system failure mode and effects is obtained from the matrix and this aids in the detailed analysis leading to the identification of various structural components of failure mode and effects. In addition, the number of tests for failure mode and effects are derived. An index I_fme, a measure of failure mode and effects of the system, is obtained using VPF-F_me. The methodology is applicable not only at the design stage during the operation stage also.     

Multidisciplinary perspective on accident investigation

The increasing complexity of many computer-controlled application processes is placing increasing demands on the investigation of adverse events. At the same time, there is a growing realisation that accident investigators must consider a wider range of contributory and contextual factors that help to shape human behaviour in the causes of safety-related incidents. A range of techniques have been developed to address these issues. For example (as we show in this paper), task modelling techniques have been extended from human computer interaction and systems design to analyse the causes and consequences of operator ‘error’. Similarly, barrier analysis has been widely used to identify the way in which defences either protected or failed to protect a target system from potential hazards. Many barriers fail from common causes, including misconceptions that can be traced back to early stages in the development of a safety-critical system. For instance, unwarranted assumptions can be made about the impact of training on operator behaviour in emergency situations. Similarly, barrier analysis can also be used before a system has been designed to inform the system model and make it more tolerant to errors by incorporating human and technical barriers into the design. Task models often uncover deep-rooted problems, for instance, in workload allocation across many different aspects of an interactive control system. It can be difficult to use barrier and task analysis to trace these common causes that lie behind the failure of many different defences. In order to deal with this complex combination of contributory factors and systems, we promote the use of abstraction (via models) as a way of representing these components and their interrelations whether it is design, construction or investigation. We use, to formally model an abstraction of the system. Additionally, the system model (described using a dialect of high-level Petri-nets) allows to reason about the system and to check conformance with the other models (task model, safety case and barriers). This paper, therefore, shows how an analysis of safety case arguments can be used to support the application of barrier, task, error and system analysis during the investigation of a command and control failure. The intention, in this paper, is to show that if an accident involved the failure of multiple barriers, it is also possible to trace the common causes of those failures back to the assumptions and arguments that are embodied within a safety case. Many countries require that safety cases demonstrate a system is ‘acceptably safe’ before they grant regulatory approval. These documents and the associated analytical techniques, therefore, provide a rich source of information about why command and control failures occurred. We demonstrate our approach on a fatal mining accident case study.     

The early implementation of failure modes into existing component model libraries

This research addresses a need in systems engineering to verify that a system can meet performance requirements; this is done by integrating failure behavior into the system’s nominal model during the initial stages of design. In general, failure behavior is not used in early assessments, lending toward increased uncertainty in the model’s validity. Current libraries do not model failures and thus cannot confidently address how a design will function in the intended operational environments. Since failures occur from effects on the environment, they should be included during verification and validation efforts. Current approaches capture off-nominal behavior using parameter variation where flow variables and parameters are varied to measure the system-level effect. This approach is ad hoc and does not accurately capture failure mode behavior. To address this limitation, an approach is developed to understand and implement failure mode behavior into nominal models. The Modelica Standard Library (MSL) is used as an example for the component library of nominal models. MSL has a significant amount of basic nominal component behavior and therefore is desirable for this research. Two approaches are developed to implement failure mode behavior; the first uses transfer function and use case graphs, and the second uses existing literature. In addition, complex systems often have a large number of components and an even larger number of failure modes. Since the goal is to limit the development time, we generate an approach to identify high-risk failure modes. This captures an early system-level effect of each failure mode and uses an occurrence to calculate risk. To show the usefulness of each method, two examples are provided including a vehicle drivetrain subsystem with a variety of failures and a diesel engine with fuel injector and valve failures.     

A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data

Many times, reliability studies rely on false premises such as independent and identically distributed time between failures assumption (renewal process). This can lead to erroneous model selection for the time to failure of a particular component or system, which can in turn lead to wrong conclusions and decisions. A strong statistical focus, a lack of a systematic approach and sometimes inadequate theoretical background seem to have made it difficult for maintenance analysts to adopt the necessary stage of data testing before the selection of a suitable model. In this paper, a framework for model selection to represent the failure process for a component or system is presented, based on a review of available trend tests. The paper focuses only on single-time-variable models and is primarily directed to analysts responsible for reliability analyses in an industrial maintenance environment. The model selection framework is directed towards the discrimination between the use of statistical distributions to represent the time to failure (“renewal approach”); and the use of stochastic point processes (“repairable systems approach”), when there may be the presence of system ageing or reliability growth. An illustrative example based on failure data from a fleet of backhoes is included.     

A method for designing power supply chain networks accounting for failure scenarios and preventive maintenance

A power grid is vulnerable and failures are inevitable. Failures decrease the power supply with an adverse effect on meeting the demand for electricity. Therefore, there is a need for a method to design power grid networks that result in the least possible disruption to the power supply when a failure occurs. In the literature, the focus has been on the design of the generation system without considering the transmission system or failures in the transmission system. Since power grids are integrated generation and transmission systems, each system will affect the other, so both generation and transmission systems need to be considered, as they are in this article. Methods developed for the structural modelling and analysis of supply chains are shown to be useful. The focus in this article is on describing a method using the supply chain construct for designing power grids that are relatively insensitive to failure in the integrated generation and transmission system. The efficacy of the method is illustrated using data from the Tehran Regional Electric Company. One of the findings is that targeted failures have a higher impact on decreasing the performance of the power grid than random failures. However, the focus is on the method rather than the results per se.     

Mixing Bayes and empirical Bayes inference to anticipate the realization of engineering concerns about variant system designs

Mixing Bayes and Empirical Bayes inference provides reliability estimates for variant system designs by using relevant failure data - observed and anticipated - about engineering changes arising due to modification and innovation. A coherent inference framework is proposed to predict the realization of engineering concerns during product development so that informed decisions can be made about the system design and the analysis conducted to prove reliability. The proposed method involves combining subjective prior distributions for the number of engineering concerns with empirical priors for the non-parametric distribution of time to realize these concerns in such a way that we can cross-tabulate classes of concerns to failure events within time partitions at an appropriate level of granularity. To support efficient implementation, a computationally convenient hypergeometric approximation is developed for the counting distributions appropriate to our underlying stochastic model. The accuracy of our approximation over first-order alternatives is examined, and demonstrated, through an evaluation experiment. An industrial application illustrates model implementation and shows how estimates can be updated using information arising during development test and analysis.     

Evaluating the sustainability impacts of packaging: the plastic carry bag dilemma

Life cycle assessment (LCA) is used by practitioners and policy‐makers to help them understand the sustainability impacts of packaging. LCA is useful because it quantifies the impact of a product throughout its life cycle, from raw materials extraction through to disposal or recovery. However, it can only ever be one input to decisions about the design or procurement of packaging. LCA has limitations as a tool to measure environmental impact and it does not currently evaluate social or financial impact. This paper provides a critical review of the role of LCA in evaluating packaging sustainability. It does this by evaluating the results of LCA studies that compare different types of carry bags and their implications for policy and practice. The benefits and limitations of this type of analysis are discussed. The case study of plastic carry bags demonstrates that while a scientific understanding of life cycle impacts is essential to support informed decision‐making, a broader sustainability analysis is required to ensure that all relevant issues are considered. These include the functionality of alternative bags, their relative cost, convenience for consumers and retailers, and the availability of reuse and recovery systems. An alternative approach, which evaluates packaging design within a broader sustainability framework, is presented and discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

A framework for capturing and analyzing the failures due to system/component interactions

To keep up with the speed of globalization and growing customer demands for more technology‐oriented products, modern systems are becoming increasingly more complex. This complexity gives rise to unpredictable failure patterns. While there are a number of well‐established failure analysis (physics‐of‐failure) models for individual components, these models do not hold good for complex systems as their failure behaviors may be totally different. Failure analysis of individual components does consider the environmental interactions but is unable to capture the system interaction effects on failure behavior. These models are based on the assumption of independent failure mechanisms. Dependency relationships and interactions of components in a complex system might give rise to some new types of failures that are not considered during the individual failure analysis of that component. This paper presents a general framework for failure modes and effects analysis (FMEA) to capture and analyze component interaction failures. The advantage of the proposed methodology is that it identifies and analyzes the system failure modes due to the interaction between the components. An example is presented to demonstrate the application of the proposed framework for a specific product architecture (PA) that captures interaction failures between different modules. However, the proposed framework is generic and can also be used in other types of PA. Copyright © 2007 John Wiley & Sons, Ltd.     

A generic tool for cost estimating in aircraft design

A methodology to estimate the cost implications of design decisions by integrating cost as a design parameter at an early design stage is presented. The model is developed on a hierarchical basis, the manufacturing cost of aircraft fuselage panels being analysed in this paper. The manufacturing cost modelling is original and relies on a genetic-causal method where the drivers of each element of cost are identified relative to the process capability. The cost model is then extended to life cycle costing by computing the Direct Operating Cost as a function of acquisition cost and fuel burn, and coupled with a semi-empirical numerical analysis using Engineering Sciences Data Unit reference data to model the structural integrity of the fuselage shell with regard to material failure and various modes of buckling. The main finding of the paper is that the traditional minimum weight condition is a dated and sub-optimal approach to airframe structural design.