http://www.hindawi.comThe latest articles from Hindawi Publishing Corporation© 2012, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/journals/ijqsr/2012/985152/We propose an asymptotic nonparametric confidence interval for quantile-based process capability indices (PCIs) based on the superstructure CNp(u,v) modified from Cp(u,v) which contains the four basic PCIs, Cp, Cpk, Cpm, and Cpmk, as special cases. Since the asymptotic variance of the estimator for quantile-based PCIs involves the density function of the underlying process, the existing asymptotic results cannot be used directly to construct confidence limits for PCIs. To obtain a consistent estimator for the asymptotic variance of the estimated quantile-based PCIs, in this paper, we propose to use the kernel density estimator for the underlying process. Consequently, the confidence limits for PCIs are established based on the consistent estimates. A real-life example from manufacturing engineering is used to illustrate the implementation of the proposed methods. Simulation studies are also presented in this paper to compare the two quantile estimators that are used in the definition of PCIs.Cheng Peng and Jiaqing XuCopyright © 2012 Cheng Peng and Jiaqing Xu. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/830764/Multivariate analysis is increasingly used to include all dimensions of quality concept, in light of rapid development of customer requirements. With the recent advances in information technology and in recording, large amounts of multivariate data are now needed to be analyzed. Many charting procedures are based on Mahalanobis distance, but their applicability relies heavily on the requirement of normality and their performance is related to the choice of a type I error rate. An alternative charting scheme based on data depth is pursued and its performance is assessed through a real example. This performance and that of a T2 chart for individual observations are discussed. Using the centre-outward ranking, this new method named DD-diagram is used to detect any multivariate quality datum that one of its components exceeds its limiting variation interval. For a given error-free sample, the DD-diagram can be used to signal out any point of another observed sample taken from a multivariate quality process. This new scheme based on data depth uses a properly chosen limiting variation line or Lvalue in order to evaluate the outlyingness of every point in the observed sample in all directions of the considered P-variates of quality process.Mekki HajlaouiCopyright © 2011 Mekki Hajlaoui. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/379129/Reliability studies for coding contributing factors of incident reports in high hazard industries are rarely conducted and reported. Although the Human Factors Analysis and Classification System (HFACS) appears to have a larger number of such studies completed than most other systems doubt exists as the accuracy and comparability of results between studies due to aspects of methodology and reporting. This paper reports on a trial conducted on HFACS to determine its reliability in the context of military air traffic control (ATC). Two groups participated in the trial: one group comprised of specialists in the field of human factors, and the other group comprised air traffic controllers. All participants were given standardized training via a self-paced workbook and then read 14 incident reports and coded the associated findings. The results show similarly low consensus for both groups of participants. Several reasons for the results are proposed associated with the HFACS model, the context within which incident reporting occurs in real organizations and the conduct of the studies.Liang Wang, Yaohua Wang, Xiaoqiang Yang, Kai Cheng, Haishan Yang, Baoguo Zhu, Chengfei Fan, and Xinwei JiCopyright © 2011 Liang Wang et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/820243/Microelectromechanical systems (MEMS) are a fast-growing field in microelectronics. MEMS are commonly used as actuators and sensors with a wide variety of applications in health care, automotives, and the military. The MEMS production cycle can be classified as three basic steps: (1) design process, (2) manufacturing process, and (3) operating cycle. Several studies have been conducted for steps (1) and (2); however, information regarding operational failure modes in MEMS is lacking. This paper discusses reliability in the context of MEMS functionality. It also presents a brief review of the most relevant failure mechanisms for MEMS.Daniel J. Fonseca and Miguel SequeraCopyright © 2011 Daniel J. Fonseca and Miguel Sequera. All rights reserved.http://www.hindawi.com/journals/ijqsr/2012/245910/This paper considers the estimation problem for the Frèchet distribution under progressive Type II censoring with random removals, where the number of units removed at each failure time has a binomial distribution. We use the maximum likelihood method to obtain the estimators of parameters and derive the sampling distributions of the estimators, and we also construct the confidence intervals for the parameters and percentile of the failure time distribution.Mohamed MubarakCopyright © 2012 Mohamed Mubarak. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/537543/One of the key issues in robust parameter design is to configure the controllable factors to minimize the variance due to noise variables. However, it can sometimes happen that the number of control variables is greater than the number of noise variables. When this occurs, two important situations arise. One is that the variance due to noise variables can be brought down to zero The second is that multiple optimal control variable settings become available to the experimenter. A simultaneous confidence region for such a locus of points not only provides a region of uncertainty about such a solution, but also provides a statistical test of whether or not such points lie within the region of experimentation or a feasible region of operation. However, this situation requires a confidence region for the multiple-solution factor levels that provides proper simultaneous coverage. This requirement has not been previously recognized in the literature. In the case where the number of control variables is greater than the number of noise variables, we show how to construct critical values needed to maintain the simultaneous coverage rate. Two examples are provided as a demonstration of the practical need to adjust the critical values for simultaneous coverage.Aili Cheng, John Peterson, and Pallavi ChitturiCopyright © 2011 Aili Cheng et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/681210/This paper deals with the Bayes prediction of the future failures of a deteriorating repairable mechanical system subject to minimal repairs and periodic overhauls. To model the effect of overhauls on the reliability of the system a proportional age reduction model is assumed and the 2-parameter Engelhardt-Bain process (2-EBP) is used to model the failure process between two successive overhauls. 2-EBP has an advantage over Power Law Process (PLP) models. It is found that the failure intensity of deteriorating repairable systems attains a finite bound when repeated minimal repair actions are combined with some overhauls. If such a data is analyzed through models with unbounded increasing failure intensity, such as the PLP, then pessimistic estimates of the system reliability will arise and incorrect preventive maintenance policy may be defined. On the basis of the observed data and of a number of suitable prior densities reflecting varied degrees of belief on the failure/repair process and effectiveness of overhauls, the prediction of the future failure times and the number of failures in a future time interval is found. Finally, a numerical application is used to illustrate the advantages from overhauls and sensitivity analysis of the improvement parameter carried out.Preeti Wanti Srivastava and Nidhi JainCopyright © 2011 Preeti Wanti Srivastava and Nidhi Jain. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/721962/The reliability function for a parallel system of two identical components is derived from a stress-strength model, where failure of one component increases the stress on the surviving component of the system. The Maximum Likelihood Estimators of parameters and their asymptotic distribution are obtained. Further the Maximum Likelihood Estimator and Bayes Estimator of reliability function are obtained using the data from a life-testing experiment. Computation of estimators is illustrated through simulation study.S. B. Munoli and Rohit R. MutkekarCopyright © 2011 S. B. Munoli and Rohit R. Mutkekar. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/396297/We evaluate the ability of transient response analysis method (TRAM), a simple test strategy proposed for filters, to detect deviations in circuit specifications beyond established limits. Particularly, we focus our attention on deviations produced by displacement damage in integrated resistors. This damage is produced by the impact of high-energy particles like the encountered in space environments. For this purpose, we formulate a simple deviation-fault model that takes into consideration the degradation addressed. Additionally, more transient response parameters are taken into account in order to improve the fault coverage. We adopt for our evaluations two typical second-order filters as cases of study. For these filters, the simulation results show that TRAM reaches excellent fault coverage for both filters, suggesting that its use in space applications is encouraging.José Peralta, Gabriela Peretti, Eduardo Romero, Gustavo Demarco, and Carlos MarquésCopyright © 2011 José Peralta et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/395034/A sequence of independent lifetimes X1,…,Xm,Xm+1,…,Xn was observed from Maxwell distribution with reliability r1(t) at time t but later, it was found that there was a change in the system at some point of time m and it is reflected in the sequence after Xm by change in reliability r2(t) at time t. The Bayes estimators of m, θ1, θ2 are derived under different asymmetric loss functions. The effects of correct and wrong prior information on the Bayes estimates are studied.Mayuri Pandya and Hardik PandyaCopyright © 2011 Mayuri Pandya and Hardik Pandya. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/637079/This paper considers three competing maintenance actions: corrective after failure, condition-based preventive to avoid failure, and scheduled. A stochastic model is set for the time relationship between corrective and preventive maintenance. This model defines a version of the so-called random signs censoring, and it leads to natural goodness measures for condition control. The effect of condition control on important observable and unobservable indicators is studied. The latter half of the paper considers static Weibull models for the time to failure and the effect of condition control; the main contribution is a calculation method that derives the four model parameters from four figures of input data. Corresponding parameter experimentation is demonstrated on maintenance cost analysis and condition control design.Per-Erik HagmarkCopyright © 2011 Per-Erik Hagmark. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/357814/Let the regression model be Yi=β1Xi+εi, where εi are i. i. d. N (0,σ2) random errors with variance σ2>0 but later it was found that there was a change in the system at some point of time m and it is reflected in the sequence after Xm by change in slope, regression parameter β2. The problem of study is when and where this change has started occurring. This is called change point inference problem. The estimators of m, β1,β2 are derived under asymmetric loss functions, namely, Linex loss & General Entropy loss functions. The effects of correct and wrong prior information on the Bayes estimates are studied.Mayuri Pandya, Krishnam Bhatt, and Paresh AndhariaCopyright © 2011 Mayuri Pandya et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/718901/A methodology for probabilistic modeling of fatigue damage accumulation for single stress level and multistress level loading is proposed in this paper. The methodology uses linear damage accumulation model of Palmgren-Miner, a probabilistic S-N curve, and an approach for a one-to-one transformation of probability density functions to achieve the objective. The damage accumulation is modeled as a nonstationary process as both the expected damage accumulation and its variability change with time. The proposed methodology is then used for reliability prediction under single stress level and multistress level loading, utilizing dynamic statistical model of cumulative fatigue damage. The reliability prediction under both types of loading is demonstrated with examples.Vijay Rathod, Om Prakash Yadav, Ajay Rathore, and Rakesh JainCopyright © 2011 Vijay Rathod et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/618347/We derive Bayes estimators of reliability and the parameters of a two- parameter geometric distribution under the general entropy loss, minimum expected loss and linex loss, functions for a noninformative as well as beta prior from multiply Type II censored data. We have studied the robustness of the estimators using simulation and we observed that the Bayes estimators of reliability and the parameters of a two-parameter geometric distribution under all the above loss functions appear to be robust with respect to the correct choice of the hyperparameters a(b) and a wrong choice of the prior parameters b(a) of the beta prior.J. B. Shah and M. N. PatelCopyright © 2011 J. B. Shah and M. N. Patel. All rights reserved.http://www.hindawi.com/journals/ijqsr/2011/719534/Since Lindley and Smith introduced the idea of hierarchical prior distribution, some results have been obtained on hierarchical Bayesian method to deal with lifetime data. But all those results obtained by means of hierarchical Bayesian methods involve complicated integration compute. Though some computing methods such as Markov Chain Monte Carlo (MCMC) are available, doing integration is still very inconvenient for practical problems. This paper introduces a new method, named E-Bayesian estimation method, to estimate failure probability. In the case of one hyperparameter, the definition of E-Bayesian estimation of the failure probability is provided; moreover, the formulas of E-Bayesian estimation and hierarchical Bayesian estimation and the property of E-Bayesian estimation of the failure probability are also provided. Finally, calculation on practical problems shows that the provided method is feasible and easy to perform.Ming HanCopyright © 2011 Ming Han. All rights reserved.http://www.hindawi.com/journals/ijqsr/2010/816407/Steering module is a part of automotive suspension system which provides a means for an accurate vehicle placement and stability control. Components such as steering knuckle are subjected to fatigue failures due to cyclic loads arising from various driving conditions. This paper intends to give a description of a method used in the fatigue life reliability evaluation of the knuckle used in a passenger car steering system. An accurate representation of Belgian pave service loads in terms of response-time history signal was obtained from accredited test track using road load data acquisition. The acquired service load data was replicated on durability test rig and the SN method was used to estimate the fatigue life. A Pearson system was developed to evaluate the predicted fatigue life reliability by considering the variations in material properties. Considering random loads experiences by the steering knuckle, it is found that shortest life appears to be in the vertical load direction with the lowest fatigue life reliability between 14000–16000 cycles. Taking into account the inconsistency of the material properties, the proposed method is capable of providing the probability of failure of mass-produced parts.E. A. Azrulhisham, Y. M. Asri, A. W. Dzuraidah, N. M. Nik Abdullah, A. Shahrum, and C. H. Che HassanCopyright © 2010 E. A. Azrulhisham et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2010/180293/The objective of this paper is to quantify the effect of autocorrelation coefficients, shift magnitude, types of control charts, types of controllers, and types of monitored signals on a control system. Statistical process control (SPC) and automatic process control (APC) were studied under non-stationary stochastic disturbances characterized by the integrated moving average model, ARIMA (0,1,1). A process model was simulated to achieve two responses, mean squared error (MSE) and average run length (ARL). A factorial design experiment was conducted to analyze the simulated results. The results revealed that not only shift magnitude and the level of autocorrelation coefficients, but also the interaction between these two factors, affected the integrated system performance. It was also found that the most appropriate combination of SPC and APC is the utilization of the minimum mean squared error (MMSE) controller with the Shewhart moving range (MR) chart, while monitoring the control signal (X) from the controller. Therefore, integrating SPC and APC can improve process manufacturing, but the performance of the integrated system is significantly affected by process autocorrelation. Therefore, if the performance of the integrated system under non-stationary disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when integrating SPC and APC.Karin KandananondCopyright © 2010 Karin Kandananond. All rights reserved.http://www.hindawi.com/journals/ijqsr/2010/849043/Gathered data is frequently not in a numerical form allowing immediate appliance of the quantitative mathematical-statistical methods. In this paper are some basic aspects examining how quantitative-based statistical methodology can be utilized in the analysis of qualitative data sets. The transformation of qualitative data into numeric values is considered as the entrance point to quantitative analysis. Concurrently related publications and impacts of scale transformations are discussed. Subsequently, it is shown how correlation coefficients are usable in conjunction with data aggregation constrains to construct relationship modelling matrices. For illustration, a case study is referenced at which ordinal type ordered qualitative survey answers are allocated to process defining procedures as aggregation levels. Finally options about measuring the adherence of the gathered empirical data to such kind of derived aggregation models are introduced and a statistically based reliability check approach to evaluate the reliability of the chosen model specification is outlined.Stefan LoehnertCopyright © 2010 Stefan Loehnert. All rights reserved.http://www.hindawi.com/journals/ijqsr/2010/213690/Although failure reporting, analysis, and corrective action system (FRACAS) has two management perspectives, its tasks and related information, the previous researches and applications mainly have focused on the data management. This study is to develop a process-oriented FRACAS which supports the operation of the failure-related activities. The development procedures are (1) to define the reporting and analysis tasks, (2) to define the information to be used at each task, and (3) to design a computerized business process model and set the attributes such as durations, rules, and document types. This computerized FRACAS process can be activated in a business process management system (BPMS) which employs the enactment functions, deliver tasks to the proper workers, provide the necessary information, and alarm the abnormal status of the tasks (delay, incorrect delivery, cancellation). Through implementing the prototype system, improvements are found for automation of the tasks, prevention of disoperation, and real-time activity monitoring.Jae Hoon Lee, SungIl Chan, and Joong Soon JangCopyright © 2010 Jae Hoon Lee et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2010/942184/We study the loss of power of the stratified log-rank test (SLRT) compared to the unstratified log-rank test (ULRT) in the case of a large number of strata with relatively a small number of stratum sizes in terms of the asymptotic distributions of test statistics under local alternatives. The SLRT tends to lose information due to overstratification. It is better to test the homogeneity among strata before using the stratified log-rank test.Changyong Feng, Hongyue Wang, and Xin M. TuCopyright © 2010 Changyong Feng et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2009/126283/A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.Janet Myhre, Daniel R. Jeske, Michael Rennie, and Yingtao BiCopyright © 2009 Janet Myhre et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2009/754896/Good estimates of the reliability of a system make use of test data and expert knowledge at all available levels. Furthermore, by integrating all these information sources, one can determine how best to allocate scarce testing resources to reduce uncertainty. Both of these goals are facilitated by modern Bayesian computational methods. We demonstrate these tools using examples that were previously solvable only through the use of ingenious approximations, and employ genetic algorithms to guide resource allocation.Todd L. Graves and Michael S. HamadaCopyright © 2009 Todd L. Graves and Michael S. Hamada. All rights reserved.http://www.hindawi.com/journals/ijqsr/2009/670340/Hydrodynamic parameters play a major role in the dynamics and control of Autonomous Underwater Vehicles (AUVs). The performance of an AUV is dependent on the parameter variations and a proper understanding of these parametric influences is essential for the design, modeling, and control of high-performance AUVs. In this paper, the sensitivity of hydrodynamic parameters on the control of a flatfish type AUV is analyzed using robust design techniques such as Taguchi's design method and statistical analysis tools such as Pareto-ANOVA. Since the pitch angle of an AUV is one of the crucial variables in the control applications, the sensitivity analysis of pitch angle variation is studied here. Eight prominent hydrodynamic coefficients are considered in the analysis. The results show that there are two critical hydrodynamic parameters, that is, hydrodynamic force and hydrodynamic pitching moment in the heave direction that influence the performance of a flatfish type AUV. A near-optimal combination of the parameters was identified and the simulation results have shown the effectiveness of the method in reducing the pitch error. These findings are significant for the design modifications as well as controller design of AUVs.M. Santhakumar, T. Asokan, and T. R. SreeramCopyright © 2009 M. Santhakumar et al. All rights reserved.http://www.hindawi.com/journals/ijqsr/2009/707583/We assume that the operator is interested in monitoring a multinomial process. In this case the items are classified into (k+1) ordered distinct and mutually exclusive defect categories. The first category is used to classify the conforming defect-free items, while the remaining k categories are used to classify the nonconforming items in k defect grades, with increasing degrees of nonconformity. Usually the process is said to be capable if the overall proportion of nonconforming items is very small and remains low, or declines over time. Nevertheless, since we classify the nonconforming items into k distinct defect grades, the operator can also evaluate the overall level of defectiveness. This quality parameter depends on the k defect categories. Furthermore, we are interested in evaluating, over time, the proportion of nonconforming items in each category as well as the overall level of defectiveness. To achieve this goal, we propose (i) a normalized index that can be used to evaluate the capability of the process in terms of the overall level of defectiveness, and (ii) a two-sided Shewhart-type multivariate control chart to monitor the overall proportion of nonconforming items and the corresponding defectiveness level.Paolo C. CozzucoliCopyright © 2009 Paolo C. Cozzucoli. All rights reserved.http://www.hindawi.com/journals/ijqsr/2008/594753/It is shown that the exact solution for the capability index (CPI) for Gaussian-distributed process with target bias can be expressed in terms of an unbiased CPI and a normalized target bias. The principal advantage of this specific formulation is that it facilitates evaluation of the degradation of the capability of the process due to bias between process mean and the process target. It is shown how this formalism, initially developed for the short-term process, is readily extended to long-term process for which the distribution is Gaussian. Readily isolated in the latter case are the two long-term CPI degrading effects, namely, process instability and target bias. Sufficient conditions to guarantee that long-term processes are distributed as Gaussian are discussed. Within the context of these assumed conditions, a new paradigm for a long-term locator ‘‘k’’ is proposed. For a three sigma process the results indicate that the exact CPI model is a less pessimistic predictor than both of the industry CPI models tested.Nikhil T. Satyala and R. J. PieperCopyright © 2008 Nikhil T. Satyala and R. J. Pieper. All rights reserved.http://www.hindawi.com/journals/ijqsr/2008/471607/The traditional variable selection methods for survival data depend on iteration procedures, and control of this process assumes tuning parameters that are problematic and time consuming, especially if the models are complex and have a large number of risk factors. In this paper, we propose a new method based on the global sensitivity analysis (GSA) to select the most influential risk factors. This contributes to simplification of the logistic regression model by excluding the irrelevant risk factors, thus eliminating the need to fit and evaluate a large number of models. Data from medical trials are suggested as a way to test the efficiency and capability of this method and as a way to simplify the model. This leads to construction of an appropriate model. The proposed method ranks the risk factors according to their importance.Jassim N. HussainCopyright © 2008 Jassim N. Hussain. All rights reserved.http://www.hindawi.com/journals/ijqsr/2008/156851/Modern production methods demand the synchronous multicharacteristic optimization of goods. There is a need to diversify a basic product to the importance placed on its individual quality components by a wide spectrum of concerned customers. This work shows how the super-ranking concept may be utilized taking into account relative weights among the implicated responses. The theoretical development is focused on the difficult situation where the optimization is attempted through unreplicated and saturated fractional factorial designs. A nested super-ranking scheme is devised to accommodate a dual weight assignment, first by setting up a single consolidated response per implicated customer and then, in a subsequent step, by incorporating a customer importance rating thus rendering an overall single master response. A demonstration of the proposed method on a pragmatic problem arising in aluminum milling involves optimization due to seven controlling factors concurrently influencing nine product responses modulated by six preference ratings set by a given customer base, respectively. Key benefits of this method are the offered ease of intermixing numerical and categorical data in mainstream multiresponse optimization problems, and keeping customer preferences in perspective through economical, short-cycle screening while relaxing stringent data normality and possible multidistributional effects among the implicated quality characteristics.George J. BesserisCopyright © 2008 George J. Besseris. All rights reserved.http://www.hindawi.com/journals/ijqsr/2008/263895/The risk graph is one of the most popular methods used to determine the safety integrity level for safety instrumented functions. However, conventional risk graph as described in the IEC 61508 standard is subjective and suffers from an interpretation problem of risk parameters. Thus, it can lead to inconsistent outcomes that may result in conservative SIL's. To overcome this difficulty, a modified risk graph using fuzzy rule-based system is proposed. This novel version of risk graph uses fuzzy scales to assess risk parameters, and calibration may be made by varying risk parameter values. Furthermore, the outcomes which are numerical values of risk reduction factor (the inverse of the probability of failure on demand) can be compared directly with those given by quantitative and semiquantitative methods such as fault tree analysis (FTA), quantitative risk assessment (QRA), and layers of protection analysis (LOPA).R. Nait-Said, F. Zidani, and N. OuzraouiCopyright © 2008 R. Nait-Said et al. All rights reserved.