Reliability analysis of complex multi-robotic system using GA and fuzzy methodology

The objective of the study is to compute various reliability parameters for multi-robotic system, using Real Coded Genetic Algorithms (RCGAs) and Fuzzy Lambda-Tau Methodology (FLTM). The paper contains a new idea about the reliability analysis of robotic system. The optimal values of mean time between failures (MTBF) and mean time to repair (MTTR) are obtained using GAs. Petri Net (PN) tool is applied to represent the interactions among the working components of multi-robotic system. To enhance the relevance of the reliability study, triangular fuzzy numbers (TFNs) are developed from the computed data, using possibility theory. The use of fuzzy arithmetic in the PN model increases the flexibility for application to various systems and conditions. Various reliability parameters, namely failure rate, repair time, MTBF, expected number of failures (ENOF), reliability and availability, are computed using FLTM. Sensitivity analysis has also been performed and the effects on system MTBF are addressed. The adopted methodology improves the shortcomings/drawbacks of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The analysis presented, may be helpful for the system analyst to analyze and predict the system behavior and to reallocate the required resources.     

Fuzzy reliability analysis of repairable industrial systems using soft-computing based hybridized techniques

The purpose of the present study is to analyze the fuzzy reliability of a repairable industrial system utilizing historical vague, imprecise and uncertain data which reflects its components’ failure and repair pattern. Soft-computing based two different hybridized techniques named as Genetic Algorithms Based Lambda–Tau (GABLT) and Neural Network and Genetic Algorithms Based Lambda–Tau (NGABLT) along with a traditional Fuzzy Lambda–Tau (FLT) technique are used to evaluate some important reliability indices of the system in the form of fuzzy membership functions. As a case study, all the three techniques are applied to analyse the fuzzy reliability of the washing system in a paper mill and results are compared. Sensitivity analysis has also been performed to analyze the effect of variation of different reliability parameters on system performance. The analysis can help maintenance personnel to understand and plan suitable maintenance strategy to improve the overall performance of the system. Based on results some important suggestions are given for future course of action in maintenance planning.     

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.     

Predicting uncertain behavior of industrial system using FM—A practical case

As such the reliability of system is affected by many factors such as design, manufacturing, installation, commissioning, operation and maintenance. Consequently it may be extremely difficult if not impossible to model, analyze and predict the failure behavior of system.To this effect, the authors presented a structured framework which makes use of fuzzy methodology (FM), an approximate reasoning tool to deal with the imprecise, uncertain and subjective information related to system performance. The component related objective events are modeled with the help of the Petri net model of the system. Various parameters of managerial importance such as repair time, failure rate, mean time between failures, availability and expected number of failures are computed to quantify the uncertain behavior of system. Further, to improve upon the reliability characteristics of the system, in-depth qualitative analysis of unit is carried out using failure mode and effect analysis (FMEA) by listing all possible failure modes and their causes. A decision support system based on fuzzy set theory is developed to counter the limitations of traditional FMEA. The framework has been applied to model and analyze a real complex industrial system from paper mill.     

Fuzzy modeling of system behavior for risk and reliability analysis

The main objective of the article is to permit the reliability analyst's/engineers/managers/practitioners to analyze the failure behavior of a system in a more consistent and logical manner. To this effect, the authors propose a methodological and structured framework, which makes use of both qualitative and quantitative techniques for risk and reliability analysis of the system. The framework has been applied to model and analyze a complex industrial system from a paper mill. In the quantitative framework, after developing the Petrinet model of the system, the fuzzy synthesis of failure and repair data (using fuzzy arithmetic operations) has been done. Various system parameters of managerial importance such as repair time, failure rate, mean time between failures, availability, and expected number of failures are computed to quantify the behavior in terms of fuzzy, crisp and defuzzified values. Further, to improve upon the reliability and maintainability characteristics of the system, in depth qualitative analysis of systems is carried out using failure mode and effect analysis (FMEA) by listing out all possible failure modes, their causes and effect on system performance. To address the limitations of traditional FMEA method based on risky priority number score, a risk ranking approach based on fuzzy and Grey relational analysis is proposed to prioritize failure causes.     

An approach for analyzing the reliability of industrial systems using soft-computing based technique

The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system by utilizing vague, imprecise, and uncertain data. In this, two important tools namely traditional Lambda–Tau and artificial bee colony algorithm have been used to build a technique named as an artificial bee colony (ABC) algorithm based Lambda–Tau (ABCBLT). In real-life situation, data collected from various resources contains a large amount of uncertainties due to human errors and hence it is not easy to analyze the behavior of such system up to a desired accuracy. If somehow behavior of these systems has been calculated, then they have a high range of uncertainty. For handling this situation, a fuzzy set theory has been used in the analysis and an artificial bee colony has been used for determining their corresponding membership functions. To strengthen the analysis, various reliability parameters, which affects the system performance directly, have been computed in the form of fuzzy membership functions. Sensitivity as well as performance analysis has also been analyzed and their computed results are compared with the existing techniques result. The butter–oil processing plant, a complex repairable industrial system has been taken to demonstrate the approach.     

Weighted fuzzy reasoning using weighted fuzzy Petri nets

This paper presents a Weighted Fuzzy Petri Net model (WFPN) and proposes a weighted fuzzy reasoning algorithm for rule-based systems based on Weighted Fuzzy Petri Nets. The fuzzy production rules in the knowledge base of a rule-based system are modeled by Weighted Fuzzy Petri Nets, where the truth values of the propositions appearing in the fuzzy production rules and the certainty factors of the rules are represented by fuzzy numbers. Furthermore, the weights of the propositions appearing in the rules are also represented by fuzzy numbers. The proposed weighted fuzzy reasoning algorithm can allow the rule-based systems to perform fuzzy reasoning in a more flexible and more intelligent manner     

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.     

基于模糊微分Petri网的混合系统表示

为了描述连续部分为模糊的混合系统,提出了基于模糊微分Petri网的混合系统表示方法。该方法的切换规则由时间来控制并由离散变迁来监督管理,而连续部分则由模糊微分方程描述,通过模糊微分库所和模糊微分变迁来建模。最后通过实例来展示我们的Petri网对混合系统的表示能力。     

RAM analysis of repairable industrial systems utilizing uncertain data

Industrial systems are mostly complex and considered as repairable. Also data, either collected or available (historical), reflecting their failure and repair patterns are limited, vague and imprecise due to various practical constraints. In such circumstances, their reliability, availability and maintainability (RAM) analysis may play an important role in any design modifications, if required, for achieving its optimum performance. However, it is difficult to estimate the RAM parameters of these systems up to a desired degree of accuracy by utilizing available information and uncertain data. This paper provides an idea, how can we estimate the RAM parameters of these systems by utilizing available information and uncertain data. For this purpose, Genetic Algorithms based Lambda–Tau (GABLT) technique is used. In this technique, expressions for the RAM parameters of the system are obtained by using traditional Lambda–Tau methodology and genetic algorithm is used to compute these parameters in the form of fuzzy membership functions utilizing quantified data in the form of triangular fuzzy numbers. A general RAM-Index is used for post RAM analysis to rank the subunits of the system on the basis of their performance. The approach has been applied to the press (series system) and washing (series–parallel system) units of a typical paper mill. The results may be helpful for the plant personnel for analyzing the systems’ behavior and to improve their performance by adopting suitable maintenance strategies.     

模糊时间Petri网及扩展的模糊时间Petri网

网络实时系统对时间和QoS有严格要求,因此对设计、实现和测试的技术和工具都有很高的要求。模糊时间Petri网是将模糊集合理论用于不确定的或主观的时间信息的表示,在每个变迁上添加时间间隔约束对模糊时间Petri网进行扩展得到扩展的模糊时间Petri网,它是一种对网络实时系统进行建模和分析的形式化模型,Petri网数学理论基础能保证并发系统的可靠性和正确性。本文详细地介绍了模糊时间Petri网及可扩展的时间Petri网,可扩展的时间Petri网的网精简方法,使用design／CPN仿真,及使用EFTN对多媒体同步建模,并探讨了进一步研究的方向。     

模糊Petri网在8K电力机车LCU故障诊断中的应用

首先引入模糊Petri网的结构和定义,分析了8K电力机车逻辑控制单元(LCU)发生故障的主要原因,总结了3种常见故障;针对LCU故障并发的特点,建立了模糊产生式规则与Petri网推理技术相结合的常开触点和常闭触点两个故障诊断模型,利用其具有较好的并行处理能力及容错性,实现了对8K电力机车LCU的模糊故障的推理;结果表明该模型具有快捷、准确的特点,展现出较强的实用性;8K电力机车LCU的故障综合测试系统已在湖东电力机务段投入使用,提高了工作效率,保证了8K电力机车的安全运行。     

Describing an IMS by a FNRTPN definition: a VHDL approach

A new Petri net extension and a novel method describing the structure and behaviour of an intelligent manufacturing system (IMS), using a VHDL tool, is proposed. The PN extension is defined as Fuzzy Neural Real Time Petri Net (FNRTPN), where the fuzzy part let the intelligent scheduling of the tasks for the IMS, and the neural part calculates the estimation of the parameters of the set point for each resource in the system. At last, VHDL is proposed as an Unified Modelling Language (UML) to express the model of the manufacturing process. The extension and VHDL are used to apply a fuzzy neural control scheme to a FMS.     

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.     

直觉模糊Petri网的空战战术决策

空战对抗时间短、任务重、态势变化迅速、不确定性因素多。直觉模糊集是处理不确定性问题的有效方法,结合Petri网强大的知识表示能力,给出了直觉模糊Petri网（IFRN）的模糊推理算法和推理规则。针对空战战机稍纵即逝的特点,适当选取模糊控制变量和隶属度函数,构建了直觉模糊Petri网决策模型。选取某空战实例进行仿真实验,决策结果与专家的预测结果吻合,验证了模型的正确性和可用性。分析了模型在实际应用中存在的缺陷,提出适当的改进意见,使模型更加完善。     

A process-control and diagnostic tool based on continuous fuzzy Petri nets

The use of Petri nets and fuzzy logic in intelligent process control has caught the attention of many researchers. In this paper, a Continuous Fuzzy Petri Net (CFPN) tool which integrates the three technologies of fuzzy control, Petri nets and real-time expert systems is presented. The CFPN approach can deal with real-time continuous inferencing, for the purpose of process monitoring and diagnostics, at a high level in the presence of uncertainty. This tool has been implemented in the G2 real-time expert-system environment and is currently being used by ESSO Canada.     

A new fuzzy methodology-based structured framework for RAM and risk analysis

The aim of this paper is to propose a new hybridized framework for analyzing the performance issues of a chemical process plant by utilizing uncertain, imprecise and vague information. In the proposed framework, Fuzzy Lambda–Tau (FLT) approach has been used for computing reliability, availability and maintainability (RAM) parameters of the considered system. Further, for enhancing the RAM characteristics of the system, improved Fuzzy Failure Mode Effect Analysis (FMEA) approach is adopted. Under improved Fuzzy FMEA approach, defined Fuzzy linguistic rating values in the form of triangular and trapezoidal Fuzzy numbers have been assigned by the experts to each risk factor of the listed failure causes. The proposed framework is demonstrated with an industrial application in a chlorine production plant of a chemical process industry. The results show decreasing trend for system availability and deposition of solid Nacl, mechanical failure, corrosion due to wet chlorine, scanty lubrication, improper adsorption and valve malfunctioning are identified as the most critical failure causes for the considered system. A comparative performance analysis between the proposed framework, Fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS), Fuzzy evaluation based on distance from average solution (Fuzzy EDAS) and Fuzzy Vlse Kriterijumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR) are then carried out to show the competence of the proposed framework. It is expected that the analytical results would be highly useful in formulating an optimal maintenance policy for such complex systems and may also be used for improving performance of similar plants.     

模糊Petri网及知识表示

在建造专家系统中虽然有很多种知识表示方法，但都有不尽人意的地方，本文试图用一种新的方法－Ｐｅｔｒｉ网模型来表示知识。本文给出了Ｆｕｚｚｚｙ Ｐｅｔｒｉ网和广义Ｆｕｚｚｙ Ｐｅｔｒｉ网两种模型，并给出了相应的推理算法，一旦专家知识用Ｐｅｔｒｉ网表示后，根据Ｐｅｔｒｉ网固有的特性，我们就能处理专家系统中并行推理、无回溯推理，反向推理等问题。     

具有死锁和陷阱结构的模糊Petri网性态分析

Petri网是一种具有动态特性的建模工具,模糊Petri网是Petri网的一个重要分支,可以有效处理具有不精确的、不确定的复杂因素的系统模型问题。本文在模糊动态Petri网的定义和运行规则基础上,研究了具有死锁和陷阱结构的模糊Petri网系统,通过配置不同的初始标识,分析其动态运行特性,并给出算例分析。