The role of quality engineering in the 1990s

The business environment of the 1990s will be affected in a major way by two major catalysts for change: one driven by technology and one driven by organizational change. This paper reviews the impact of major catalysts for change expected in the 1990s. Organizational change strategies will enable a corporation to be able accurately to assess the performance and success of each engineering group in the design, manufacturing, and customer installation and life cycle support. The strategies for the development of the methods and tools and long-term career development strategies to support the ability of the organization to catalyse change are also discussed, with the conclusion that the quality engineering profession, correctly structured and empowered, will be the driver of the successful implementation of organizational change strategies in the 1990s.     

Combined hardware and software aspects of reliability

This paper presents the similarities and differences between hardware, software and system reliability. Relative contributions to system failures are shown for software and hardware and failure and recovery propensities are also discussed. Reliability, availability and maintainability (RAM) concepts have been broadly developed for software reliability than hardware reliability. Extending these software concepts to hardware and system reliability helps in examining the reliability of complex systems. The paper concludes with assurance techniques for defending against faults. Most of the techniques discussed originate in software reliability but apply to all aspects of a system. Also, the effects of redundancy on overall system availability are shown.     

Quality and reliability research into the next century

The paper examines the research needed to advance the science of Q&R and in particular looks at that required to overcome the present crisis in reliability prediction and assessment. Areas requiring special attention are methods for the objective grading of product and organizational quality, research into human error causes and prevention, better modelling of reliability for prediction purposes, the discovery and use of unreliability diagnostic parameters, and better understanding and application of accelerated testing. Accelerated testing, screening and reliability prediction should all be more failure-mode-specific than they are currently. Reliability physics should pay increased attention to the precise effect material and geometric defects have on device life.     

Comparative study of quantitative models for hardware,software and human reliability assessment

Various models which may be used for quantitative assessment of hardware, software and human reliability are compared in this paper. Important comparison criteria are the system life cycle phase in which the model is intended to be used, the failure category and reliability means considered in the model, model purpose, and model characteristic such as model construction approach, model output and model input. The main objective is to present limitations in the use of current models for reliability assessment of computer-based safety shutdown systems in the process industry and to provide recommendations on further model development. Main attention is given to presenting the overall concept of various models from a user's point of view rather than technical details of specific models. A new failure classification scheme is proposed which shows how hardware and software failures may be modelled in a common framework.     

A management view of software reliability

Projects with a high software content are frequently completed late, with overrun costs and inadequate performance. Much of this is due to inadequate management, mainly caused by a lack of availability or knowledge of the criteria, methods and tools on which effective management should be based. Examples are given of the most frequent problems met by project managers. A plea is made for a vigorous effort to be made in developing management strategies which will allow the tools and techniques now being researched and developed to be used effectively for producing the high quality software needed for systems.     

A fuzzy logic based approach to reliability improvement estimation during product development

During early stages of product development process, a vast amount of knowledge and information is generated. However, most of it is subjective (imprecise) in nature and remains unutilized. This paper presents a formal structure for capturing this information and knowledge and utilizing it in reliability improvement estimation. The information is extracted as improvement indices from various design tools, experiments, and design review records and treated as fuzzy numbers or linguistic variables. Fuzzy reasoning method is used to combine and quantify the subjective information to map their impact on product reliability. The crisp output of the fuzzy reasoning process is treated as new evidence and incorporated into a Bayesian framework to update the reliability estimates. A case example is presented to demonstrate the proposed approach.     

Improvements in reliability and serviceability of cemented carbides with wear-resistant coatings

Typical defects in microstructure at a surface-coating interface in chemical vapor deposition coated cemented carbides are described. Results on applying new technologies developed to improve reliability and serviceability of coated cemented carbides are presented. Using WC-based cemented carbide substrates with a uniform, fine microstructure etched before coating eliminates various defects at the surface-coating interface. This results in improvement in serviceability and reliability of the coated cemented carbides in metal-cutting.     

A study of software reliability growth from the perspective of learning effects

For the last three decades, reliability growth has been studied to predict software reliability in the testing/debugging phase. Most of the models developed were based on the non-homogeneous Poisson process (NHPP), and S-shaped type or exponential-shaped type of behavior is usually assumed. Unfortunately, such models may be suitable only for particular software failure data, thus narrowing the scope of applications. Therefore, from the perspective of learning effects that can influence the process of software reliability growth, we considered that efficiency in testing/debugging concerned not only the ability of the testing staff but also the learning effect that comes from inspecting the testing/debugging codes. The proposed approach can reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and the results in the experiment show good fit. A comparative analysis to evaluate the effectiveness for the proposed model and other software failure models was also performed. Finally, an optimal software release policy is suggested.     

Strategic evolution of eco-products: a product life cycle planning methodology

This paper presents a methodology and a software tool to establish an eco-design concept of a product and its life cycle by assigning appropriate life cycle options to the components of the product. The product life cycle planning (LCP) methodology provides the following systematic procedures. First, the medium- or long-term production and collection plan for the product family is clarified. Next, target values for the product and its life cycle are set in the process of determination of customer-oriented specification and eco-specification. Then, eco-solution ideas to realize reasonable resource circulation are generated by using various life cycle option analysis charts. Finally, an eco-design concept which involves eco-solution ideas is evaluated for decision-making at early stages of product development. A design support tool was made for efficiently planning product life cycles by using quality function deployment and life cycle assessment data. Based on case studies, it was verified that the proposed methodology and tool are useful for developing multi-generational eco-products.     

Application of the fault tree analysis for assessment of power system reliability

A new method for power system reliability analysis using the fault tree analysis approach is developed. The method is based on fault trees generated for each load point of the power system. The fault trees are related to disruption of energy delivery from generators to the specific load points. Quantitative evaluation of the fault trees, which represents a standpoint for assessment of reliability of power delivery, enables identification of the most important elements in the power system. The algorithm of the computer code, which facilitates the application of the method, has been applied to the IEEE test system. The power system reliability was assessed and the main contributors to power system reliability have been identified, both qualitatively and quantitatively.     

Component reliability assessment and assurance using a new step-stress test

Several features of accelerated reliability testing are surveyed in this paper. A new technique for the practical evaluation of reliability under thermally induced acceleration is introduced. This procedure is termed the reverse-step-stress test, and involves a decrease in applied stress after some pre-specified failure criterion has been met within a fixed sample of components. An analysis is presented which confirms that the reverse-step-stress test can reveal failures having low activation energies that would not be detectable in conventional constant-stress, high temperature testing.     

全生命周期产品和过程模型集成技术研究

制造业的产品和过程集成技术体现在各个不同的层面,为此,研究了全生命周期产品和过程模型的集成技术。采用面向对象方法,构建全生命周期虚拟产品模型,对全生命周期的产品信息进行表达,并对全生命周期的涵义进行阐述。虚拟产品模型由实体对象和联系对象组成,它是一个动态模型,在产品全生命周期中不断进化。通过面向对象Petri网模型(OPN),描述产品全生命周期的各种过程。在虚拟产品模型和OPN模型的基础上重点讨论产品模型和过程模型的集成技术,并结合实例进行研究。产品和过程模型集成为产品信息的重用和追溯提供支持。     

Key attributes of the SAPHIRE risk and reliability analysis software for risk-informed probabilistic applications

The Idaho National Laboratory is a primary developer of probabilistic risk and reliability analysis (PRRA) tools, dating back over 35 years. Evolving from mainframe-based software, the current state-of-the-practice has led to the creation of the SAPHIRE software. Currently, agencies such as the Nuclear Regulatory Commission, the National Aeronautics and Aerospace Agency, the Department of Energy, and the Department of Defense use version 7 of the SAPHIRE software for many of their risk-informed activities. In order to better understand and appreciate the power of software as part of risk-informed applications, we need to recall that our current analysis methods and solution methods have built upon pioneering work done 30–40 years ago. We contrast this work with the current capabilities in the SAPHIRE analysis package. As part of this discussion, we provide information for both the typical features and special analysis capabilities, which are available. We also present the application and results typically found with state-of-the-practice PRRA models. By providing both a high-level and detailed look at the SAPHIRE software, we give a snapshot in time for the current use of software tools in a risk-informed decision arena.     

Application of inverse first-order reliability method for probabilistic fatigue life prediction

A general probabilistic life prediction methodology for accurate and efficient fatigue prognosis is proposed in this paper. The proposed methodology is based-on an inverse first-order reliability method (IFORM) to evaluate the fatigue life at an arbitrary reliability level. This formulation is different from the forward reliability problem, which aims to calculate the failure probability at a fixed time instant. The variables in the fatigue prognosis problem are separated into two categories, i.e., random variables and index variables. An efficient searching algorithm for fatigue life prediction is developed to find the corresponding index variable at a certain confidence level. Numerical examples using direct Monte Carlo simulation and the proposed IFORM method are compared for algorithm verification. Following this, various experimental data for metallic materials are used for model prediction validation.     

Analysis of sequential failures for assessment of reliability and safety of manufacturing systems

Assessment of reliability and safety of a manufacturing system with sequential failures is an important issue in industry, since the reliability and safety of the system depend not only on all failed states of system components, but also on the sequence of occurrences of those failures. Methods that are currently available in sequential failure analysis always start with given sequences of the failures in the system, which is not the case in real life situations; therefore, the sequences of the failures should be identified and the probability of their occurrence should be determined. In this paper, we represent a methodology that can be used for identifying the failure sequences and assessing the probability of their occurrence in a manufacturing system. The method employs Petri net modeling and reachability trees constructed based on the Petri nets. The methodology is demonstrated on an example of an automated machining and assembly system.     

Building confidence in the reliability assessment of thermal-hydraulic passive systems

Thermal-hydraulic (T-H) passive systems play a crucial role in the development of future solutions for nuclear power plant technologies. A fundamental issue still to be resolved is the quantification of the reliability of such systems.The difficulty comes from the uncertainties in the evaluation of their performance, because of the lack of experimental and operational data and of validated models of the phenomena involved. The uncertainties concern the deviations of the underlying physical principles from the expected T-H behaviour, due to the onset of physical phenomena infringing the system performance or to changes in the initial/boundary conditions of system operation.In this work, some insights resulting from a survey on the technical issues associated with estimating the reliability of T-H passive systems in the context of nuclear safety are first provided. It is concluded that the most realistic assessment of the passive system response to the uncertain accident conditions can be achieved by Monte-Carlo (MC) sampling of the system uncertain parameters followed by the simulation of the accident evolution by a detailed mechanistic T-H code. This procedure, however, requires considerable and often prohibitive computational efforts for achieving acceptable accuracies, so that a limitation on the MC sample size, i.e. on the number of code runs, is necessarily forced onto the analysis. As a consequence, it becomes mandatory to provide quantitative measures of the uncertainty of the computed estimates.To this aim, two classes of statistical methods are proposed in the paper to quantify, in terms of confidence intervals, the uncertainties associated with the reliability estimates. The first method is based on the probability laws of the binomial distribution governing the stochastic process of system success or failure. The second method is founded on the concept of bootstrapping, suitable to assess the accuracy of estimators when no prior information on their distributions is available. To the authors’ knowledge, it is the first time that these methods are applied to quantitatively bracket the confidence on the estimates of the reliability of passive systems by MC simulation.The two methods are demonstrated by an application to a real passive system of literature.     

A Bayesian model averaging method for software reliability modeling and assessment

The software reliability modeling is of great significance in improving software quality and managing the software development process. However, the existing methods are not able to accurately model software reliability improvement behavior because existing single model methods rely on restrictive assumptions and combination models cannot well deal with model uncertainties. In this article, we propose a Bayesian model averaging (BMA) method to model software reliability. First, the existing reliability modeling methods are selected as the candidate models, and the Bayesian theory is used to obtain the posterior probabilities of each reliability model. Then, the posterior probabilities are used as weights to average the candidate models. Both Markov Chain Monte Carlo (MCMC) algorithm and the Expectation–Maximization (EM) algorithm are used to evaluate a candidate model's posterior probability and for comparison purpose. The results show that the BMA method has superior performance in software reliability modeling, and the MCMC algorithm performs better than EM algorithm when they are used to estimate the parameters of BMA method.     

Probabilistic reliability assessment of a heritage structure under horizontal loads

This paper proposes a methodology for the probabilistic reliability assessment of heritage buildings. The procedure addresses investigation and tests on the structure and it considers the implementation of Bayesian updating techniques for a rational use of the collected information. After having described the peculiarities of ancient buildings, it is shown how probabilistic methods can be adapted to evaluate their safety. A practical application of the methodology to a relevant case study is presented, namely a historic aqueduct in Italy. The main goal is to demonstrate the effectiveness of a probabilistic approach to the reliability assessment of heritage structures.     

Quality of life development during initial hemodialysis therapy and association with loss of residual renal function

Introduction: Health related quality of life (HRQOL) is markedly reduced in hemodialysis patients compared to the general population. We investigated the course of self‐reported HRQOL over time and the association with selected factors, focusing on changes in glomerular filtration rate (GFR). Methods: Eighty‐two newly started hemodialysis patients from the SAFIR cohort filled out the Kidney Disease Quality of Life Short Form Version 1.3 (KDQOL‐SF^TM) questionnaire at baseline, 6 and 12 months. The SAFIR study was a randomized, placebo‐controlled, double‐blind intervention study, examining the effects of the angiotensin II receptor blocker irbesartan. HRQOL was a secondary outcome measure. Main inclusion criteria: Dialysis vintage <1 year, left ventricular ejection fraction >30% and urinary output >300 mL/day. GFR was measured with mean creatinine and urea clearance from 24‐hour urine collections at baseline, 6 and 12 months. Findings: Irbesartan treatment did not affect HRQOL. Patients were pooled into one group for further analyses. Decline in GFR correlated significantly with decreasing HRQOL over time. HRQOL was stable over time, with a slight nonsignificant tendency toward improved HRQOL. The largest HRQOL‐differences (positive values equal improved HRQOL) observed during the 12 month study period were (mean[95% confidence interval]): Burden of kidney disease:6.4[?2.2;15.0], Role limitations‐physical:12.7[?2.1;27.5], and Role limitations‐emotional:9.7[?5.2;24.6]. Comorbidity, especially diabetes, hospital admissions, female gender, and age were strongly associated with lower HRQOL in cross sectional analysis. Discussion: Preservation of residual renal function seems to be important for HRQOL. In newly started HD patients, HRQOL showed little change after 12 months. HRQOL was negatively affected by comorbidity, especially diabetes, hospital admissions, female gender, and age.