Power transformer fault diagnosis based on variable precision rough set

In order to effectively handle the real-valued data sets in practice, it is valuable from theoretical and practical aspects to combine fuzzy rough set and variable precision rough set so that a powerful tool can be developed. That is, the model of fuzzy variable precision rough set, which not only can handle numerical data but also is less sensitive to misclassification and perturbation,In this paper, we propose a new variable precision rough fuzzy set by introducing the variable precision parameter to generalized rough fuzzy set, i.e., the variable precision rough fuzzy set based on general relation. We, respectively, define the variable precision rough lower and upper approximations of any fuzzy set and it level set with variable precision parameter by constructive approach. Also, we present the properties of the proposed model in detail. Meanwhile, we establish the relationship between the variable precision rough approximation of a fuzzy set and the rough approximation of the level set for a fuzzy set. Furthermore, we give a new approach to uncertainty measure for variable precision rough fuzzy set established in this paper in order to overcome the limitations of the traditional methods. Finally, some numerical example are used to illuminate the validity of the conclusions given in this paper.

Transformer fault diagnosis is a complex task that includes many possible types of faults and demands special trained personnel. This paper presents an intelligent fault diagnosis method of power transformer based on fuzzy logic and rough set theory. By using a fuzzy logic technique, the continuous attribute values are transformed into the fuzzy values by automatically deriving membership functions from a set of data with similarity clustering. With the concepts of fuzzy similarity relation and fuzzy similarity classes, β-positive region, β-negative region andβ-boundary region of rough-fuzzy approximation space are given. Also, a fuzzy rough set learning algorithm is given for inducing rules from quantitative data. The application to fault diagnosis of transformer shows the proposed algorithm can find more objective and effective diagnostic rules from the quantitative data and has yielded promising results.

Grade and precision are two important confidence levels. In this paper, the connection and combination between them are studied. The connection between graded approximations and variable precision approximations is studied, and the important formula of conversion is achieved. Approximation of intersection of grade and precision is defined based on logical ”and” operation of grade and precision, and its construction is achieved, which becomes important theory base for decreasing computing complexity and computing quantity of application algorithm of graded rough sets and variable precision rough sets. Graded rough sets and variable precision rough sets are united and included by the approximation of intersection of grade and precision. The study on combination and unity of graded rough sets and variable precision rough sets in this paper, has important value on both theory development and practice application.KeywordsArtificial IntelligenceRough SetsGraded Rough SetsVariable Precision Rough SetsConfidence LevelsGradePrecision

An integration method of variable precision rough set and neural network for fault diagnosis is presented and used in rotary machinery fault diagnosis. The method integrates the ability of variable precision rough set on reduction of diagnosis information system and that of neural network for fault classification. Typical faults of rotating machinery were simulated in our rotor test-bed. The power spectrum data are used as rotating machinery fault diagnosis signal. For inconsistent data and noise data in power spectrum, variable precision rough set model allows a flexible region of lower approximations by precision variables. By attribute reduction based on variable precision rough set, redundant attributes are identified and removed. The reduction results are used as the input of neural network. The diagnosis results show that the proposed approach for input dimension reduction in neural network is very effective and has better learning efficiency and diagnosis accuracy.

The variable precision rough set (VPRS) model generalizes the Pawlak rough set model with variable parameters. The Bayesian rough set (BRS) model improves the VPRS model with non-parametric modification by using the prior probability as a reference. This paper presents two research results related to rough set model and rough vague sets. One is a modification to the Bayesian rough set model and the other is a modification to rough vague sets. First, the Bayesian rough set model is analyzed and discussed. Second, a modification to this model is proposed and verified. Finally, a modification to rough vague sets is presented and its related properties are discussed..

Traditional rough set of noise data, the lack of adaptability, lack of flexibility or robustness, for the engineering data can not distinguish between equivalence classes of edge region of overlap with the collection, resulting in loss of many valuable engineering information. Strong noise in the actual engineering data over-fitting due to reduced ability to distinguish the object, its limitations limits its further application. In this paper, variable precision rough set model (VPRS, the variable precision Rough set Model), allows a certain threshold level of classification rate exists, in order to better solve the engineering data, no functional relationship between attributes of interference data classification problem, it Overcome the standard rough set model on the shortcomings of the data is too sensitive to noise, thereby enhancing the data analysis and processing robustness. Finally, simulation results show that using variable precision rough set theory to solve the data uncertainty of similar projects under the influence of the extraction is feasible.

Variable precision rough set model is the extension of rough set model, which introduce theβparameter based on the basic rough set theory, that is, allows a certain degree of misclassification rate existing. This paper presents the web mining algorithm based on variable precision rough set in e-commerce, analyzes the limitation of the reduction methods based on variable precision rough set theory. The experiment shows the CPU Time in the attribute numbers, indicating that VPRS is superior to normal algorithm in web mining in e-commerce. The experiments show the algorithm could flexibly and dynamically web mining and therefore provide a valuable addition to the field.

Variable precision rough sets (VPRS), as a extension of rough sets (RS) is adopted to reduce the redundant features for its ability of more useful information adopted compared with RS. The reduced features after VPRS are fed into the improved BP network proposed to inspect the defects of surface quality, which results in short training time and a high classification accuracy with a typical application in defect inspection of wood veneer.

The fault diagnosis based on the vibration analysis becomes a powerful supplement for the conventional fault diagnosis method and plays an increasingly important role in large power transformer's faults diagnosis because of its unique advantages. The vibration source, vibration characteristics, transmission route and the principles of fault diagnosis based on vibration method are analyzed. The paper discusses the structure of vibration measuring system, the measuring point selection and the analysis method of the vibration measuring signal, forecasting the development trend and research direction of transformer's faults diagnosis based on vibration method.

A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.

Mobile ad hoc networks are formed dynamically without any infrastructure and each node in the network is responsible for routing information. Rough set theory is a mathematical tool to deal with vagueness and uncertainty. Variable precision rough sets (VPRS) is a generalization of rough sets that allows for a controlled degree of misclassification. This paper proposes extensions in mobile ad hoc routing using VPRS. The performance of the proposed mobile ad hoc routing protocol is compared with that of an existing routing protocol.

In order to remove the drawbacks of the classical attribute reduction algorithm based on attribute importance which can't ensure to gain the simplest decision table and the best noise resisting ability, this paper presents a improved attribute reduction algorithm based on β - variable precision rough sets. A parameter β represents the error resolution and makes the decision table more simple and reliable. Compared with the classical attribution reduction algorithm, β - variable precision rough sets attribute reduction algorithm has better generalization and ability of resisting noise. These two algorithms are used on the simulation of Car Test Results and the results verify the superiority of the improved algorithm.

An illustration of variable precision rough sets model: an analysis of the findings of the UK Monopolies and Mergers Commission

Transformers play a crucial role in ensuring the safety of power grids. It is of great value to diagnose faults using the large amount of power data generated by the grid. It is possible to detect internal latent faults in transformers in advance of their occurrence if the normal operating condition of the transformer is detected in a timely manner. To perform online fault diagnosis of grid current transformers, we combine the Transformer and BiGRU methods. There is a temporal component to the fault input sample sequences. By using Transformer’s multi-headed attention mechanism to extract deep features from fault input sample sequences, the temporal association between latent variables can be fully exploited. As a result of the extraction of features, BiGRU is used to generate fault category coding as an output. The experimental results indicate that using the proposed algorithm achieves better results than using a single model, which is useful for the study and application of fault diagnosis in power grids for current transformers.

Quantitative rough sets based on subsethood measures