Fault Diagnosis of Machine Based on Fuzzy Reliability Theory

1 IntroductionIncreasing attention is being paid to fault diagno:is of machines'in modern Produchon. PQoPlehope that through monitoring and analysis of some parameters of machines, faults can bediagnosed correchy in time and immediate measures can be taken to Prevent equipment frOmbeing damaged and accidents from sPreading, so as to edce econondc loss. Available faultdiagnosis includes vibration analysis, gray theOry diagnosis, neural network diagnosis, expertsystems and so onIZ3]. Referen…     

规则库自提取和简化的模糊系统

介绍了一种新的规则自提取和简化的模糊模型算法，该算法由3个步骤组成：首先，通过构造一个模糊软划分判别准则对输入输出空间进行模糊划分，得到最佳分类数，从而得到了最佳的规则数；其次，根据最佳规则数和神经网络来构造初始的模糊模型；第三，通过运用模糊相似分析法，可以得到计算2个模糊集相似度的方程，从而根据事先确定的阈值来合并相似的模糊集。这样就得到了一个既满足对精度的要求又简单且具有可解释性的模糊系统。最后，用该算法对我国全要素生产力进行了模糊规则的提取。     

用粗集-模糊神经网络评定空袭目标威胁程度

针对模糊神经网络运算过程中,当模糊规则较多时,网络学习速度慢,方法实时性差的缺点,本文提出采用粗糙集理论对该模型进行优化,该方法利用粗集数据分析方法,通过知识约简从数据中推理逻辑规则,并用约简后规则集作为模糊神经网络的规则将输入映射到输出的子空间上:在这个子空间上用改进的BP算法训练进行逼近.实验结果表明:通过粗集数据挖掘后提取的规则,不仅规则数目减少,且规则是不完全规则,减少了网络输入维数和各层神经元的个数,提高了网络运算速度,满足了系统实时性要求.     

Fuzzy-logic-based network for complex systems risk assessment: Application to ship performance analysis

In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster–Shafer theory of evidence is extended to monitor safety-critical systems’ performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi–Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input–output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex.     

Developing a fuzzy proportional–derivative controller optimization engine for engineering design optimization problems

In real world engineering design problems, decisions for design modifications are often based on engineering heuristics and knowledge. However, when solving an engineering design optimization problem using a numerical optimization algorithm, the engineering problem is basically viewed as purely mathematical. Design modifications in the iterative optimization process rely on numerical information. Engineering heuristics and knowledge are not utilized at all. In this article, the optimization process is analogous to a closed-loop control system, and a fuzzy proportional–derivative (PD) controller optimization engine is developed for engineering design optimization problems with monotonicity and implicit constraints. Monotonicity between design variables and the objective and constraint functions prevails in engineering design optimization problems. In this research, monotonicity of the design variables and activities of the constraints determined by the theory of monotonicity analysis are modelled in the fuzzy PD controller optimization engine using generic fuzzy rules. The designer only needs to define the initial values and move limits of the design variables to determine the parameters in the fuzzy PD controller optimization engine. In the optimization process using the fuzzy PD controller optimization engine, the function value of each constraint is evaluated once in each iteration. No sensitivity information is required. The fuzzy PD controller optimization engine appears to be robust in the various design examples tested.     

Engineering System Safety Analysis and Synthesis Using the Fuzzy Rule‐based Evidential Reasoning Approach

The main objective of this paper is to propose a framework for modelling, analysing and synthesizing system safety of engineering systems or projects on the basis of a generic rule‐based inference methodology using the evidential reasoning (RIMER) approach. The framework is divided into two parts. The first one is for fuzzy rule‐based safety estimation, referred to as a fuzzy rule‐based evidential reasoning (FURBER) approach. The second one is for safety synthesis using the evidential reasoning approach. In the FURBER framework, parameters used to define the safety level, including failure rate, failure consequence severity and failure consequence probability are described using fuzzy linguistic variables; a fuzzy rule base designed on the basis of a belief structure is used to capture uncertainty and nonlinear relationships between these three parameters and the safety level; and the inference of the rule‐based system is implemented using the evidential reasoning algorithm. Then the following steps involve synthesizing safety at higher levels of an engineering system to integrate all possible causes to a specific technical failure, or estimates made by a panel of experts. The synthesis is also based on the evidential reasoning approach. The final step describes the analysis and interpretation of the final synthesized safety of a system. The above framework has been applied to modelling system safety of an offshore and marine engineering system: the floating production storage offloading (FPSO) system. A series of case studies of collision risk between a FPSO and a shuttle tanker due to technical failure during a tandem offloading operation is used to illustrate the application of the proposed model. Copyright © 2005 John Wiley & Sons, Ltd.     

基于混合智能的电火花加工电参数学习模型的研究

提出了一个基于混合智能的电火花加工电参数学习模型,它模仿熟练操作者的决策过程,由于工艺数据库、加工规则库、学习模块和推理模块组成。在学习模块中利用遗传算法从工艺数据库中抽取出反映电参数和加工结果之间关系的模糊产生式规则,存储在规则库中。推理模块基于这些规则利用模糊拄是对新加的要求提供合适的电参数。     

Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface

The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is well-performed better than traditional classifiers but it suffers from the exponential growth of the rules produced. In the past, we have proposed an information gain based fuzzy association rule mining algorithm for extracting both association rules and member-ship functions of medical data to reduce the rules. It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. In this paper, an enhanced approach, called gain ratio based fuzzy weighted association rule mining, is thus proposed for distinct diseases and also increase the learning time of the previous one. Experimental results show that there is a marginal improvement in the attribute selection process and also improvement in the classifier accuracy. The system has been implemented in Java platform and verified by using benchmark data from the UCI machine learning repository.     

空间网壳结构的自调整模糊振动控制

基于模糊控制原理,构建运用于大跨空间网格结构的经控制规则修改的自调整模糊控制器,提出含有一个和多个调整因子的控制规则的解析形式及其相应控制规则表,且对控制规则进行调整因子自寻优.运用该模糊控制器,实施对网壳结构的振动控制,数值对比分析在自调整模糊控制器与未经过规则修改的模糊控制器下,结构动力响应及调整因子在自寻优前后的结构动力响应.研究表明,该自调整模糊控制器设计简便,振动控制效果明显.     

Input control and dispatching rules in a dynamic CONWIP flow-shop

The purpose of this paper is to study the input control and dispatching rules that might be used in a flow shop controlled by a constant work-in-process system (CONWIP) within a make-to-stock environment. The CONWIP system was originally conceived for a single card-count control and a FCFS dispatching rule, although in this paper it is shown that its performance can be increased by using other input controls as well as different dispatching rules.     

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of input MFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lower MF (LMF) of the MF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2 MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method. It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches. The work implies a wider range of IT2 MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions.     

Position precision compensation method of a micro-manipulation stage based on fuzzy control

The hysteresis nonlinearity of piezoelectric micro-manipulation stage leads to the decrease of its position accuracy and dynamic performance, and it is hard to establish the accurate hysteresis model. A position accuracy compensation method was adopted based on the fuzzy control strategy to get rid of the dependence on hysteretic model. As for a one-dimensional micro-manipulation stage, the position deviation and deviation variation rate of the stage was used as fuzzy input, and the input voltage change of piezoelectric actuator was used as fuzzy output. A method of developing fuzzy rules was presented based on experiment data of PID control to acquire experience. The fuzzy relationship between the stage input and output by fuzzy reasoning and de-fuzzy process was established, and the adaptive compensation of hysteresis could be realized. In order to illustrate the feasibility of the proposed method, the experimental comparative analysis with PID control was carried out. The position errors were compared when the stage was tracking the sine signals with different frequencies. The experimental results show that the proposed fuzzy control method can make the stage have higher position tracking accuracy and faster tracking speed, and has better adaptability.     

基于模糊控制的微操作平台位置精度补偿方法

由于压电式微操作平台的迟滞非线性会导致其位置精度和动态性能下降,且难以建立精确的迟滞非线性模型,采用一种基于模糊控制策略的位置精度补偿方法,以摆脱对迟滞模型的依赖。以一种一维压电式微操作平台为对象,以平台的位置偏差与偏差变化率为模糊输入,压电驱动器输入电压变化量为模糊输出,提出采用基于PID控制的实验数据获取经验来制定模糊规则的方法。通过模糊推理和解模糊过程,建立平台输入量与输出量之间的模糊关系,实现了可消除迟滞现象的自适应补偿。为了说明所提出的位置精度补偿方法的可行性,通过实验与PID控制进行比较,分析平台跟踪不同频率正弦信号的位置误差。实验结果表明,所提出的模糊控制方法能使平台具有更高的位置跟踪精度和更快的跟踪速度,并具有较好的自适应性。     

Prediction of posttranslational modifications using intact-protein mass spectrometric data

We present a Web-based application that uses whole-protein masses determined by mass spectrometry to identify putative co- and posttranslational proteolytic cleavages and chemical modifications. The protein cleavage and modification engine (PROCLAME) requires as input an intact mass measurement and a precursor identification based on peptide mass fingerprinting or tandem mass spectrometry. This approach predicts mass-modifying events using a depth-first tree search, bounded by a set of rules controlled by a custom-built fuzzy logic engine, to explore a large number of possible combinations of modifications accounting for the experimental mass. Candidates are saved during a search if they are within a user-specified instrument mass accuracy; the total number of possible candidates searched is based on a specified fuzzy cutoff score. Candidates are scored and ranked using a simple probabilistic model. There is generally not enough information in an intact mass measurement to determine a single unique protein characterization; however, the program provides utility by expediting the identification of sets of putative events consistent with the mass data and ranking them for further investigation. This approach uses a simple, intuitive rule base and lends itself to discovery of unannotated posttranslational events. We have assessed the program with both in silico-generated test data and with published data from an analysis of large ribosomal subunit proteins, both from the yeast S. cerevisiae. Results indicate a high degree of sensitivity and specificity in characterizing proteins whose masses resulted from reasonable proteolysis and covalent modification scenarios. The application is available on the web at http://proclame.unc.edu.     

高层结构方案实例库系统及其在结构智能选型中应用

从结构方案设计的角度,建立了工程实例信息的统一表征模型,收集获取了国内外1008个高层结构工程实例信息。在此基础上,利用关系数据库技术建造了高层结构实例库及其管理系统,实现了实例数据录入、修改、删除、查询、统计和知识发现等功能。最后结合高层结构智能选型的实际,给出了基于实例库的定量型与模糊型结构选型规则的挖掘过程与方法,同时还构建了定量属性的模糊区间划分及其模糊化方法,分析探讨了基于Apriori算法的关联分析方法在工程智能设计等方面的可能应用。上述工作为挖掘与充分利用已有工程实例中的知识信息,提高结构设计效率、质量与智能化水平等提供了新的途径、方法与手段等的支持。     

Dispatching rules in scheduling Dispatching rules in scheduling: a fuzzy approach

Job-shop scheduling through simulation uses various kinds of dispatching rules such as SPT or the slack time rule. Each of these rules aims at satisfying a single criterion although workshop management is a multi-criteria problem. This paper proposes a way to use fuzzy logic in order to build aggregated rules allowing to obtain a compromise between the satisfaction of several criteria. When the criteria of performance change with the evolution of the production environment, these aggregated rules can be parametrized in order to modify the respective influence of the elementary rules they are composed of.     

A multi-attribute dispatching rule for automated guided vehicle systems

This paper considers the dispatching problem associated with operations of automated guided vehicles (AGVs). A multi-attribute dispatching rule for dispatching of an AGV is developed and evaluated. The multi-attribute rule, using the additive weighting method, considers three system attributes concurrently: the remaining space in the outgoing buffer of a workstation, the distance between an idle AGV and a workstation with a job waiting for the vehicle to be serviced, and the remaining space in the input buffer of the destination workstation of a job. A neural network approach is used to obtain dynamically adjusting attribute weights based on the current status of the manufacturing system. Simulation analysis of a job shop is used to compare the multi-attribute dispatching rule with dynamically adjusting attribute weights to the same dispatching rule with fixed attribute weights and to several single attribute rules. Results show that the multi-attribute dispatching rule with the ability to adapt attribute weights to job shop operational conditions provides a better balance among the performance measures used in the study.     

Optical generation of fuzzy-based rules

In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.     

A performance evaluation of three inference engines as expert systems for failure mode identification in shafts

This paper aims to present performance evaluation of three different inference engines (rule based reasoning, fuzzy based reasoning and Bayesian based reasoning) for failure mode identification in shafts. This research was done with a focus on the validation cases and results after their use in failure cases from several industries where the three systems were tested under the same conditions.Each system was implemented using the same user interface and knowledge base, with different frameworks and techniques as follows: rule based inference reasoning (prolog, C#), Mamdani-fuzzy based reasoning (C, MATLAB®) and Bayesian based reasoning with a variable elimination algorithm (C, MATLAB®).The best performance was obtained using the Bayesian inference engine. The conditional probabilities give flexibility when evidence is not listed, while the fuzzy and classical IF-THEN systems depend on the rules in the inference engine.The process presented in this paper could be used for validation of any expert system or for comparison with other expert systems (inference engines) when the knowledge base is the same.     

Fuzzy rules extraction using a hybrid of base‐n‐number‐coded GA and SVD‐QR

Abstract

A hybrid of a base‐n‐number‐coded genetic algorithm (base‐n‐number‐coded GA) and an SVD‐QR is proposed to construct a fuzzy system directly from some gathered input‐output data of the identified system. Each individual in the base‐n‐number‐coded GA is applied to determine the fuzzy sets in each input variable. However, the grid‐type fuzzy partition by the fuzzy sets associated with each input variable may generate some redundant fuzzy subspaces. Therefore, an SVD‐QR method is applied to remove the redundant fuzzy subspaces to efficiently describe the behavior of the identified system so that the premise part of the fuzzy system is determined. Then, the recursive least‐squares method is used to determine the consequent part of the fuzzy system. Subsequently, a fitness function is defined such that it can guide the search procedure to select an appropriate fuzzy system that not only maintains a good performance but also has relevant fuzzy rules. Finally, two nonlinear system identification problems are used to illustrate the efficiency of the proposed method.