Top PDF A Brief Introduction to Fuzzy Logic Technique for Fault Diagnosis

A Brief Introduction to Fuzzy Logic Technique for Fault Diagnosis

A Brief Introduction to Fuzzy Logic Technique for Fault Diagnosis

Fuzzy logic (FL) is a multi valued logic, which allows interim values to be defined between linguistic expressions like yes/no, high/low, true/false. A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts. Superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth - the truth values between "completely true & completely false".

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Artificial Intelligence Tools Aided decision for Power Transformer Fault Diagnosis

Artificial Intelligence Tools Aided decision for Power Transformer Fault Diagnosis

In this paper, the artificial intelligence techniques are implemented for the faults classification using the dissolved gas analysis for power transformers. The DGA methods studied are key gas, graphical representation and ratios method. The fault diagnosis models performance was analyzed with fuzzy logic (using Gaussian, trapezoidal and triangular membership functions), neural networks (MLP and RBF) and Support Vector Machine (with polynomial and Gaussian kernel functions). The real data sets are used to investigate the performance of the DGA methods in power transformer oil.
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Performance Improvement in Fault Diagnosis using Fuzzy Logic Type-2 Classifier

Performance Improvement in Fault Diagnosis using Fuzzy Logic Type-2 Classifier

The results of qualitative technique based on trend granulation and quantitative technique based on logistic regression suggest that the fault datasets correspond to nonlinear dynamic time series. Since, the conventional techniques based on linear models are not suitable for this type of data, hence it is proposed to use Type-2 FLS based classifier to deal with the impact of uncertainty on classification framework. The rules obtained as a result of trend granulation in previous section have been used here. Type-2 FLS offers better capabilities to handle linguistic uncertainties by modeling the uncertainties using type-2 membership functions.
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An Interval Type-2 Fuzzy Logic Approach for Instrument Fault Detection and Diagnosis

An Interval Type-2 Fuzzy Logic Approach for Instrument Fault Detection and Diagnosis

about the process: model-based and process history-based. The former, also known as analytical redundancy, generates residuals between the process and its model in order to detect changes in parameters or variables of the process. In the latter a large amount of historic process data is required to extract features about the process that will be used in the diagnostic system. This feature extraction consists of a data transformation. Two transformation approaches can be adopted, namely, quantitative and qualitative [1], [3]. In the quantitative case, the feature extraction comprises the use of statistical methods such as Principal Component Analysis (PCA) or non-statistical methods based on Artificial Neural Networks (ANN) and Fuzzy Logic (FL). Most of the IFDD techniques use model-based techniques (analytical redundancy) due to the high costs required to implement hardware redundancy which needs at least three sensors to isolate a fault. In cases in which data measurement is noisy and incomplete or analytical model is not available, artificial intelligence techniques are good alternatives [2], [4], [5]. The recent popularity of process history-based methods is related to the complexity of industrial plants and the difficulty to model them, which also justifies the application of such a method to detect and diagnose faults in this work.
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“Motor Current Signature Analysis for Fault Diagnosis and Condition Monitoring of Induction Motors using Interval Type-2 Fuzzy logic”

“Motor Current Signature Analysis for Fault Diagnosis and Condition Monitoring of Induction Motors using Interval Type-2 Fuzzy logic”

In recent years, the problems of failure in large machines have become more significant and of concern in industrial applications. The desire to improve the reliability of industrial drive systems has led to concerted research and development activities in several countries to evaluate the causes and consequences of various fault conditions. In particular, ongoing research work is being focused on rotor bar faults and on the development of diagnostic techniques- Several diagnostic techniques has been proposed in the past to detect faults due to broken rotor bars. Most of them are based on the steady-state analysis of stator voltages and currents via fast Fourier transform (FFT). Induction motors are the workhorses of industry and are frequently used in many applications because of their simple structure, inexpensive cost, and stability. The early detection of anomalies in motor drive systems is very important for safe, economic, and uninterrupted operations. There are many faults that can occur in electrical machines. The induction motor faults can be clearly seen in Fig. 1. These have been categorized according to the main components of a machine i.e. stator related faults, rotor related faults, bearing related faults and other faults. Percentage of fault distribution is shown in fig. 2.
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Online Full Text

Online Full Text

Associated with an increasing demand for high performance as well as for more safety and reliability of dynamic systems, and a natural trend toward system automation, fault detection and diagnosis has received more and more attention. The existing techniques for fault detection and diagnosis can be broadly divided into process history based and process model-based methods. Each of these can further be classified into qualitative and quantitative approaches. The qualitative approaches involve fault trees [1], signed directed graph [2], fuzzy logic [3], neural networks [4], and expert systems [5], The quantitative approaches are basically modeling, filtering
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Genetically Tuned Interval Type-2 Fuzzy Logic for Fault Diagnosis of Induction Motor

Genetically Tuned Interval Type-2 Fuzzy Logic for Fault Diagnosis of Induction Motor

Sahraoui (2009) presented work on Fuzzy Logic Approach for the Diagnosis of Rotor Faults in Squirrel Cage Induction Motors. Motor Current Signature Analysis (MCSA) was used. The strategy rests on the follow-up (in amplitude and frequency) of the harmonics representing the defects of the broken bars, preparing and thus generating the adequate inputs for the treatment where the decision is made by fuzzy logic. Z. Ye, A. Sadeghian, B. Wu (2006) presented Mechanical fault diagnostics for induction motor with variable speed drives using adaptative neuro-fuzzy inference system. Takagi & Sugeno (1985) studied on the fuzzy identification of systems and its application to modeling and control of engineering systems. Jamshidi (1997) contributed on his work for fuzzy control of complex systems in soft computing methods. Mamdani E.H. (1974, 76) contributed his work on application of fuzzy algorithm for simple dynamic plant and advances in linguistic synthesis of fuzzy controllers. Tong R.M. (1978) reported on synthesis of fuzzy models for industrial process. R. Alcal´a, J. Alcal´a-Fdez, and F. Herrera (2007) introduced a proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection. An innovative study on fault diagnosis of Induction Motor using hybrid FFT and soft computing techniques is proposed in this research work. In the proposed methodology a genetically tuned type-2 FFT fuzzy system and also ANFIS is to be developed. Before using the raw data available from the industrial and commercial sources, it will be normalized for mapping in 0,1 range. This section presented the literature review on different fuzzy techniques, various fault diagnosis of induction motor, fast Fourier transform implementation and fuzzy logic. Review covered variety of topic, methods, techniques and approaches
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Fault Diagnosis of an Induction Motor based on Fuzzy Logic, Artificial Neural Network and Hybrid System

Fault Diagnosis of an Induction Motor based on Fuzzy Logic, Artificial Neural Network and Hybrid System

Diagnostic of industrial processes is a scientific discipline aimed at the detection of faults in industrial plants, their isolation, and their identification [9, 16]. Many scientific researches deal with the problem of induction motors faults detection and diagnosis and the major difficulty is the lack of accurate model that describes a fault motor [7, 9]. In fact, a fuzzy logic approach, neural networks and hybrid system may help to diagnose an induction motor faults. It is very important in this project to do analysis, comparison and data collection to acknowledge the behavior of the induction motor conditions and determine the causes of the faults occurrence using fuzzy logic, neural network and hybrid system. Moreover, this will be done throughout modeling of induction motor, estimation of the state of the faults that can occur on a such a motor, the use of fault diagnosis method by fuzzy logic, neural and the combination of both paradigm (hybrid system). This paper is organized as follow: in section 1, introduction is presented. Section 2 presents the modeling of an induction motor. In section 3, 4 and 5, fault detection of this machine is studied using the stated methods. And finally, results, discussions and conclusion are presented in section 6 and 7.
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Fuzzy Logic Approach for Fault Diagnosis of three Phase Transmission Line

Fuzzy Logic Approach for Fault Diagnosis of three Phase Transmission Line

Transmission line among the other electrical power system component suffers from unexpected failure due to various random causes. Because transmission line is quite large as it is open in environment. A fault occurs on transmission line when two or more conductors come in contact with each other or ground. This paper presents a proposed model based on MATLAB software to detect the fault on transmission line. Fault detection has been achieved by using Fuzzy Logic based intelligent control technique. The proposed method aims in presenting a fast and accurate fault diagnosis method to classify and identify the type of fault which occurs on a power transmission system. In this paper, some of the unconventional approaches for condition monitoring of power systems comprising of relay Breaker, along with the application of soft computing technique like fuzzy logic. Results show that the proposed methodology is efficient in identifying fault in transmission system.
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Artificial Intelligence Based Fault Diagnosis of Power Transformer-A Probabilistic Neural Network and Interval Type-2 Support Vector Machine Approach

Artificial Intelligence Based Fault Diagnosis of Power Transformer-A Probabilistic Neural Network and Interval Type-2 Support Vector Machine Approach

ABSTRACT: Power transformers has an important role in electrical power transmission and its interruption has financial losses, thus its condition monitoring is essential and performance of this equipment is effective for power system reliability. In this paper, proposed method has advantages of both probabilistic neural network (PNN) and Interval Type-2 Fuzzy Support Vector Machine (IT2FSVM). Firstly, main feature is extracted from primary and secondary three phase currents and search coils differential voltage by wavelet transform and this information is used as probabilistic neural network inputs. AI techniques are applied to establish classification features for faults in the transformers based on the collected gas data. The features are applied as input data to PNN and IT2FSVM combination of classifiers for faults classification. The experimental data from NTPC Korba-India is used to evaluate the performance of proposed method. The results of the various DGA methods are classified using AI techniques. In comparison to the results obtained from the AI techniques, the PNN plus IT2SVM has been shown to possess the most excellent performance in identifying the transformer fault type. The test results indicate that the PNN plus IT2SVM approach can significantly improve the diagnosis accuracies for power transformer fault classification. In addition, the study aims to study the joint effect of PNN and IT2SVM on the classification performance when used together. KEYWORDS: Probabilistic Neural Network (PNN), Interval Type-2 Fuzzy Logic, Support Vector Machines, Dissolved gas analysis, Transformer Fault Diagnosis.
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Fuzzy Logic Based Fault Classification Scheme for Digital Distance Protection

Fuzzy Logic Based Fault Classification Scheme for Digital Distance Protection

Along with other electrical components, the transmission line suffers from the unexpected failures due to various faults. Protecting of transmission lines is most important task to safeguard electric power systems. For safe operation of transmission line systems, the protection systems should be able to detect, classify, locate accurately and clear the fault as fast as possible to maintain stability in the network. The protective systems are required to prevent the propagation of these faults in the system. The occurrence of any transmission line faults gives rise to the transient condition which may lead to the instability of the system. The purpose of a protective relaying system is to detect all theabnormal signals indicating faults on a transmission system. After detection of the fault, the faulted part should be isolated from the rest of the system to prevent the fault propagation into healthy parts.Transmission line relaying involves three major tasks: fault detection, fault classification and fault location. Fast detection of a transmission line fault enables quick isolation of the faulty line from service and protects it from the transient effects of the fault.
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Soft-Computing Based Data Mining: A Review

Soft-Computing Based Data Mining: A Review

Abstract:Soft computing is a consortium of methodologies that works synergistically and provides, in one form or another, flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. The guiding principle is to devise methods of computation that lead to an acceptable solution at low cost by seeking for an approximate solution to an imprecisely/precisely formulated problem. Soft computing methodologies (involving fuzzy sets, neural networks, genetic algorithms, and rough sets) are most widely applied in the data mining step of the overall KDD process. Fuzzy sets provide a natural framework for the process in dealing with uncertainty. Neural networks and rough sets are widely used for classification and rule generation. Genetic algorithms (GAs) are involved in various optimization and search processes, like query optimization and template selection. Other approaches like case based reasoning and decision trees are also widely used to solve data mining problems. The present article provides an over view of the available literature on data mining that is scarce, in the soft computing framework.
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An Introduction to Fuzzy Logic Controller and its Applications

An Introduction to Fuzzy Logic Controller and its Applications

This paper presents the nature of fuzziness and how the fuzzy operations are performed and how fuzzy rules can incorporate the underlying knowledge to develop a fuzzy logic controller or simply a fuzzy controller. Fuzzy logic is a way to make machines more intelligent to deal with uncertain, imprecise or qualitative decision making problems like humans. This paper also provides some applications of fuzzy controller in a simple and easy to understand manner.

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A Concise Introduction to Logic

A Concise Introduction to Logic

We might decide also that each thing that our language talks about has only one name. Some philosophers have thought that such a rule would be very helpful. However, it turns out it is often very hard to know if two apparent things are the same thing, and so in a natural language we often have several names for the same thing. A favorite example of philosophers, taken from the philosopher and mathematician Gottlob Frege (1848-1925), is “Hesperus” and “Phosphorus.” These are both names for Venus, although some who used these names did not know that. Thus, for a while, some people did not know that Hesperus was Phosphorus. And, of course, we would not have been able to use just one name for both, if we did not know that these names pointed at the same one thing. Thus, if we want to model scientific problems, or other real world problems, using our logic, then a rule that each thing have one and only one name would demand too much: it would require us to solve all our mysteries before we got started. In any case, there is no ambiguity in a thing having several names.
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Diagnosis of failures in a spark ignition engine using fuzzy logic

Diagnosis of failures in a spark ignition engine using fuzzy logic

system in the area of artificial intelligence through the study and research of the fuzzy logic theory and using the functionality of the FUZZY DESIGNER software; therefore, it could be performed a corrective maintenance in an internal combustion engine, rapidly and safely. Each response surface of the input variables enables to identify the behavior of the output variables, verifying the condition they are for performing the corrective maintenance, if necessary. The maximum percentage error of 6% ensures that the designed fuzzy control system works with high reliability.
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Type 2 fuzzy elliptic membership functions for modeling uncertainty

Type 2 fuzzy elliptic membership functions for modeling uncertainty

A gigantic amount of publications in fuzzy logic theory, no doubt almost all of them report the superiority of fuzzy logic systems over the conventional methods, may cause the readers get lost in this big ocean; and frankly speaking, only a few of those concentrate on the meaning of uncertainty while their basic claim is to be able to deal with it. In this section, we make an attempt to consider all these sets to form the perspective of uncertainty modeling. This is, by its very nature, somewhat subjective but we hope is interesting for the reader.

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Fault Diagnosis Technology Based on Model Driven

Fault Diagnosis Technology Based on Model Driven

From the practical operation, the fault reasoning process can be completed in the fault period (time from the beginning to the end of the fault, usually 7 - 8 s) and send the fault report to the dispatching center. Compared with the traditional analysis method, the fault diagnosis by model driven is quicker, more accurate and more re- liable. It can not only deal with the typical fault, but also meet the requirements of complicated fault type. Moreover, the fault time, fault device, fault phase and the corresponding bay contained in the report is helpful for the operator to judge quickly and avoid the continuous expansion of influence.
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Compositional Rule of Inference and Adaptive Fuzzy Rule Based Scheme with Applications

Compositional Rule of Inference and Adaptive Fuzzy Rule Based Scheme with Applications

Abstract. Incorporate imprecise, uncertain and linguistic information into logical analysis fuzzy inference scheme dominates over classical two-valued logic. Compositional algebra with relations is used in inference scheme for approximate reasoning. In a fuzzy inference scheme, linguistic variables are used in observation and effect parts to get a conclusion. The conclusion has been used to other system-generated result to get the final result that we have addressed as Adaptive fuzzy rule based scheme. In this paper, stability and continuity of compositional rules are studied for a generalized fuzzy inference scheme. Finally, adaptive fuzzy inference scheme has been applied to stock data.
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Fuzzy Fault Tree Analysis for Fault Diagnosis of Cannula Fault in Power Transformer

Fuzzy Fault Tree Analysis for Fault Diagnosis of Cannula Fault in Power Transformer

In present paper, we introduce a novel approach to ap- proximate the failure possibility of basic events, if more than one fuzzy number is assigned to a particular basic event by different experts. The possibilities of basic events are considered to be triangular fuzzy numbers. Three fuzzy numbers are assigned to each basic event by the Experts A, B and C. These experts collect data of failure for each component in three different operating conditions “Worst Case Condition”, “Conducive Envi- ronment” and “Highly Conducive Environment”. Unlike previous techniques, we investigate the operating condi- tions rigorously and assess the weightage of each of them. Taking view of this, we use the parameter estimation method used in PERT method and generalize it by re- placing crisp numbers with fuzzy numbers, to obtain most likely fuzzy number to represent the failure possi- bility of basic events. The proposed method seems to be very pragmatic and preclude underestimation/overesti- mation of failure possibility for basic events.
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Analog Circuit Diagnosis Based on SubKPCA SVM

Analog Circuit Diagnosis Based on SubKPCA SVM

Fault diagnosis for analog circuit is still a challenging subject in the circuit test research field. Due to the inherent characteristics of analog circuit [1-2], such as its nonlinearity, continuous response and tolerance on component parameters, etc., inducing the diversity and complexity of fault types of the circuit, it is difficult for the conventional fault diagnosis theories and methods to achieve the expected results in practical engineering. Hence, it is very important to explore some efficient fault diagnosis theories and methods to meet the development of analog circuit.
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