Predicting uncertain behavior of press unit in a paper industry using artificial bee colony and fuzzy Lambda–Tau methodology

As the industrial systems are growing complex these-days and data related to the system performance are recorded/collected from various resources under various practical constraints. If the collected data are used as such in the analysis, then they have high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. Thus the main objective of the present work is to remove the uncertainties in the data up to a desired degree of accuracy by utilizing the uncertain, vague and limited data. For analysis of this, an artificial bee colony based Lambda–Tau (ABCBLT) methodology has been used in which expression of the reliability parameters are computed by using Lambda–Tau methodology and their membership functions are formulated by solving a nonlinear optimization problem with artificial bee colony (ABC) algorithm. A time varying failure rate has been used in the analysis instead of constant failure rate. A new RAM-Index has been proposed for ranking the systems’ components based on its performance. The technique has been demonstrated through a case study of press unit of a paper industry, situated in Northern part of India, producing 200 tons of paper per day. The results computed by the proposed approach are compared with the Lambda–Tau methodology and concluded that they have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties.     

Performing ergonomics analyses through virtual interactive design: Validity and reliability assessment

Virtual interactive design methodology was initially proposed as an integrated environment for digital human model–based ergonomics analysis of human–machine interactions, which uses a motion capture system and virtual environment to realize task simulation. The validity and reliability of this analysis methodology has not been systematically studied. In this article, potential errors are first investigated based on the technology used in the system structure and work process; then, three experimental integration levels are proposed to use different resources for creating manikin postures. Validity and reliability to use this integrated environment for static ergonomics analysis are then assessed by comparing the ergonomics analysis outputs achieved based on the three integration levels. The results indicate that the reliability of this methodology is good; however, the validity of the integrated system is affected by the limitation of the virtual environment used in the testing. © 2011 Wiley Periodicals, Inc.     

Sequential failure analysis using counters of Petri net models

Assessment of reliability and safety of a complex system with sequential failures is an important issue in industries, since the reliability and safety of a complex system depends, not only upon all failed states of the system components, but also upon the sequence of occurrence of those failures. In this paper, we present an alternative methodology for assessing sequential failures in a complex system. In previous research, the probability of sequential failures is computed based on tracking the markings of Petri net models and it is assumed that the time to failure of basic events follows an exponential distribution. To overcome the limitations of the current methods in applying Petri nets for assessment of sequential failures, the method presented in this paper employs the concept of counters used in Petri net simulation to perform sequential failure analysis and is developed based on evolution equations that describe the dynamics of the system. It is demonstrated with a sequential failure analysis example of an automated machining and assembly system.     

An integrated restoration methodology based on adaptive failure feature identification

Remanufacturing is an emerging eco-friendly industry because it consumes less energy, cost, and material to manufacture like-new parts with a warranty to match. However, restoration processes are ad-hoc and complex because the "raw" materials for remanufacturing are returned used parts, which exhibit significant uncertainties in failure features involving failure location, failure mode, failure volume, and failure degree. Thus, customized remanufacturing process planning (RPP) and restoration tool paths should be generated to restore the defects for each part. An integrated restoration methodology based on adaptive failure feature identification for remanufacturing is proposed to enable efficient and cost-effective remanufacturing. In this study, an adaptive failure feature identification algorithm is developed to identify the failure features on defective parts quickly. In this stage, the point clouds of the nominal model and defective model are used to extract defective regions through Boolean operations and then calculate the failure volume and degree. Based on the identified failure features, a knowledge reuse algorithm is proposed to retrieve the optimal RPP rapidly through mixed case-based reasoning (CBR) and rule-based reasoning (RBR). Finally, a tool path generation algorithm of hybrid Subtractive Manufacturing (SM) and Additive Manufacturing (AM) for the restoration of identified defects. The proposed methodology is verified by remanufacturing a defective blade with multi-defects and is approved to be flexible and effective.     

A direct decoupling approach for efficient reliability-based design optimization

This paper develops an efficient methodology to perform reliability-based design optimization (RBDO) by decoupling the optimization and reliability analysis iterations that are nested in traditional formulations. This is achieved by approximating the reliability constraints based on the reliability analysis results. The proposed approach does not use inverse first-order reliability analysis as other existing decoupled approaches, but uses direct reliability analysis. This strategy allows a modular approach and the use of more accurate methods, including Monte-Carlo-simulation (MCS)-based methods for highly nonlinear reliability constraints where first-order reliability approximation may not be accurate. The use of simulation-based methods also enables system-level reliability estimates to be included in the RBDO formulation. The efficiency of the proposed RBDO approach is further improved by identifying the potentially active reliability constraints at the beginning of each reliability analysis. A vehicle side impact problem is used to examine the proposed method, and the results show the usefulness of the proposed method.     

Coordinative optimization method of composite laminated structures based on system reliability

This paper develops the coordinative optimization method based on system reliability for laminated structures. The proposed method improves the rough RBO based on first layer failure (FLF) criterion for composite laminates, and the coupling optimization method of thickness and sequence in traditional RBO strategy based on last layer failure criterion (LLF) is improved. In this paper, the finite element analysis is used to obtain the response for the failure based on two-dimension Hashin failure criterion (the limit function). Obviously, the stiffness of composite materials will decline due to destruction of elements. Therefore, stiffness degradation is considered to describe the process of damage evolution. Subsequently, combining with the branch-bound method (B&B), we can complete the search of main failure sequences and calculate the system reliability with the help of the second-order upper bound theory. In order to guarantee the efficiency and accuracy of optimization, the adaptive GA algorithm is introduced in the whole optimization procedure. After the proposed optimization policy is given in detail, two laminated structures are presented and the results are compared with the traditional optimal method based on safety factor, which demonstrates the validity and reasonability of the developed methodology.

     

Simulation-based time-dependent reliability analysis for composite hydrokinetic turbine blades

The reliability of blades is vital to the system reliability of a hydrokinetic turbine. A time-dependent reliability analysis methodology is developed for river-based composite hydrokinetic turbine blades. Coupled with the blade element momentum theory, finite element analysis is used to establish the responses (limit-state functions) for the failure indicator of the Tsai–Hill failure criterion and blade deflections. The stochastic polynomial chaos expansion method is adopted to approximate the limit-state functions. The uncertainties considered include those in river flow velocity and composite material properties. The probabilities of failure for the two failure modes are calculated by means of time-dependent reliability analysis with joint upcrossing rates. A design example for the Missouri river is studied, and the probabilities of failure are obtained for a given period of operation time.     

Reliability estimation of complex numerical problems using modified conditional expectation method

A simulation-based structural reliability analysis method is presented. It is intended as an alternate approach to estimate reliability for problems for which most-probable point of failure methods fail and when computational resources are limited. The proposed method combines conditional expectation and estimating the PDF or CDF of a selected portion of the limit state. In the proposed approach, complex limit state functions are simplified to two random variable problems. The success of the simplification depends on the quality of the CDF estimate. Results indicate that the method may provide accurate and efficient solutions for some difficult reliability problems.     

A cellular manufacturing system based on new similarity coefficient which considers alternative routes during machine failure

A two-phase procedure for configuring a cellular manufacturing system is proposed. In Phase I, a new similarity coefficient which considers the number of alternative routes when available during machine failure is proposed. The objective of Phase I is to identify part families based on the proposed new similarity coefficient. In Phase II, a new methodology which simultaneously considers scheduling and operational aspects in the cell design during machine failure for a manufacturing environment is proposed. Phase II shows how the scheduling and operational aspects influence the resource utilization during machine failure. The objective of the proposed methodology is to minimize the total sum of inventory holding cost, early/late finish penalty cost for each part in a given period, operating cost and machine investment cost by grouping machines into cells.     

Investigation on Ag‐containing compound generated in manufacturing of low‐temperature polycrystalline Si active matrix organic light‐emitting diode

In this work, we report a systematic investigation on Ag‐containing compound widely generated in anode wet etching process of low‐temperature polycrystalline Si active matrix organic light‐emitting diode (LTPS‐AMOLED). The formation mechanism of the aforementioned compound was proposed and confirmed by sufficient evidence. The relevant test results show that, unlike traditional metal compounds, this compound cannot be removed by aqueous oxalic acid solution. Furthermore, the reported Ag‐containing compounds grow and migrate in response to electric fields in high‐temperature and high‐humidity environment (85°C, 85% humidity) to short neighboring integrated circuit pads, causing severe product reliability failure. Spectra test results indicate that products with reliability failure are characterized by reduced display brightness and shifted color coordinates. To improve the reliability failure caused by Ag‐containing compounds, five potential schemes are presented, and most of them are conducted in this study.     

The Poisson-exponential lifetime distribution

In this paper we proposed a new two-parameters lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk problem base. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulae for its reliability and failure rate functions, quantiles and moments, including the mean and variance. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived analytically in order to obtaining the asymptotic covariance matrix. The methodology is illustrated on a real data set.     

A sequential approximate programming strategy for reliability-based structural optimization

Although reliability-based structural optimization (RBSO) is recognized as a rational structural design philosophy that is more advantageous to deterministic optimization, most common RBSO is based on straightforward two-level approach connecting algorithms of reliability calculation and that of design optimization. This is achieved usually with an outer loop for optimization of design variables and an inner loop for reliability analysis. A number of algorithms have been proposed to reduce the computational cost of such optimizations, such as performance measure approach, semi-infinite programming, and mono-level approach. Herein the sequential approximate programming approach, which is well known in structural optimization, is extended as an efficient methodology to solve RBSO problems. In this approach, the optimum design is obtained by solving a sequence of sub-programming problems that usually consist of an approximate objective function subjected to a set of approximate constraint functions. In each sub-programming, rather than direct Taylor expansion of reliability constraints, a new formulation is introduced for approximate reliability constraints at the current design point and its linearization. The approximate reliability index and its sensitivity are obtained from a recurrence formula based on the optimality conditions for the most probable failure point (MPP). It is shown that the approximate MPP, a key component of RBSO problems, is concurrently improved during each sub-programming solution step. Through analytical models and comparative studies over complex examples, it is illustrated that our approach is efficient and that a linearized reliability index is a good approximation of the accurate reliability index. These unique features and the concurrent convergence of design optimization and reliability calculation are demonstrated with several numerical examples.     

故障树分析的新方法

求故障函数不相交积之和的表达式是定量分析大型复杂系统可靠性的关键步骤.对此本 文提出三种新算法:原始Sharp法、递归Sharp法和直接Sharp法,后两种更适合大型复杂 系统的近似计算.     

Performance and behavior analysis of repairable industrial systems using Vague Lambda–Tau methodology

To remain competitive and to provide timely and accurate services, the decision maker or system analyst view the reliability and maintainability issues as a part of corporate quest to improve quality of the products/processes and services delivered. But the collected or available data available from the historical records are mostly uncertain, limited and imprecise in nature. Thus in such situation(s), it is difficult, if not impossible, to analyze the behavior and performance of the system up to desired degree of accuracy. Thus attempt has been made by the author for analyzing the behavior and performance of the system using Vague Lambda–Tau methodology. Intuitionistic fuzzy set theory has been used for representing the classical(crisp) data into triangular fuzzy numbers because intuitionistic sets are characterized by a truth membership function and false membership functions (non-membership functions) so that sum of both values is less than 1. Sensitivity analysis has also been performed and the effects on system mean time between failures are addressed. The performance analysis of the system has been investigated through a composite measure of reliability, availability and maintainability (RAM) called RAM-Index which explore the effect of failure/repair rates on system performance. Based on their performance, the most critical component of the system has been investigated. The methodology improves the shortcomings of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The pulping unit of a paper mill situated in a northern part of India, producing approximately 200 ton of paper per day, has been considered to demonstrate the proposed approach. The results obtained by proposed approach are compared with the existing fuzzy Lambda–Tau and the crisp methodologies.     

基于递推分解的Torus网络可靠性研究

为解决大规模Torus网络可靠度计算中遇到的NP难问题,引入递推分解和组合模型的思想对Torus网络的可靠性进行分析研究.递推分解的算法降低了计算网络可靠度的复杂性,组合模型的方法则降低了网络的结构复杂度.对于大规模的Torus网络,通过采用可靠度上下界逐步逼近的方法,可以得到较高精度的可靠度近似值.实验结果表明,在结点失效概率均小于0.10%时,对多达上千个结点的Torus网络仍超过90%的可靠度,而且提出的方法也适合其它并行体系结构网络的可靠度计算.     

An inverse-measure-based unilevel architecture for reliability-based design optimization

Reliability-based design optimization (RBDO) is a methodology for finding optimized designs that are characterized with a low probability of failure. Primarily, RBDO consists of optimizing a merit function while satisfying reliability constraints. The reliability constraints are constraints on the probability of failure corresponding to each of the failure modes of the system or a single constraint on the system probability of failure. The probability of failure is usually estimated by performing a reliability analysis. During the last few years, a variety of different formulations have been developed for RBDO. Traditionally, these have been formulated as a double-loop (nested) optimization problem. The upper level optimization loop generally involves optimizing a merit function subject to reliability constraints, and the lower level optimization loop(s) compute(s) the probabilities of failure corresponding to the failure mode(s) that govern(s) the system failure. This formulation is, by nature, computationally intensive. Researchers have provided sequential strategies to address this issue, where the deterministic optimization and reliability analysis are decoupled, and the process is performed iteratively until convergence is achieved. These methods, though attractive in terms of obtaining a workable reliable design at considerably reduced computational costs, often lead to premature convergence and therefore yield spurious optimal designs. In this paper, a novel unilevel formulation for RBDO is developed. In the proposed formulation, the lower level optimization (evaluation of reliability constraints in the double-loop formulation) is replaced by its corresponding first-order Karush–Kuhn–Tucker (KKT) necessary optimality conditions at the upper level optimization. Such a replacement is computationally equivalent to solving the original nested optimization if the lower level optimization problem is solved by numerically satisfying the KKT conditions (which is typically the case). It is shown through the use of test problems that the proposed formulation is numerically robust (stable) and computationally efficient compared to the existing approaches for RBDO.     

The stochastic finite-element method

A generic stochastic finite-element method for modeling structures is proposed as a means to analyze and design structures in a probabilistic framework. Stochastic differential and difference equation theory is applied in structures discretized with the finite-element methodology.Transient structural loads, idealized as stochastic processes, are incorporated into finite-element dynamic models with uncertain parameters. An estimate of the probability of failure based on known and established procedures in second-moment reliability analysis can be made with the aid of a transformation to gaussian space of the random variables that define structural reliability.The stochastic finite-element method will facilitate the use of probabilistic mathematical structural models for structural code development or design of important structures. It will also permit better estimation of structural reliability, which, when combined with risk analysis, could lead to improved decision-making processes.     

An improved Kriging-based approach for system reliability analysis with multiple failure modes

Reliability analysis with multiple failure modes is needed because more than one failure mode exists in many engineering applications. Kriging-based surrogate model is widely adopted for component reliability analysis because of its high computational efficiency. Compared with Kriging-based component reliability analysis, selecting the sample points that affect the system performance is more difficult than that of a single failure mode in system reliability analysis. Therefore, how to select suitable sample points is a key problem in system reliability analysis. Meanwhile, reducing the number of calls to the performance functions is challenging, especially for systems with time-consuming performance functions. In this paper, an improved Kriging-based system reliability analysis approach is proposed based on the two strategies. In strategy 1, the initial sample points are determined by considering only two different cases: (a) the candidate samples are selected from the safe regions only for series systems; (b) the candidate samples are selected from the failure regions only for parallel systems. Therefore, samples having little contributions to the composite performance function are avoided. In strategy 2, the sample points determined in strategy 1 will be further optimized by interpolating. From comparisons with three reported methods in numerical examples, the efficiency and accuracy of the proposed method are illustrated.

     

基于聚类的软件失效数据预处理

应用失效数据进行软件可靠性参数的评估与预测是软件可靠性工程的主要内容之一。利用分层聚类算法对失效数据中的扰动和异常数据点进行隔离,并将以故障密度为相似性度量进行聚类处理后的失效数据集进行SRGM建模与软件可靠性参数估计,以较好的曲线拟合度提高SRGM参数估计与可靠性预测的精确性和抗干扰性,从而得到良好的软件可靠性分析与预测结果。