A new methodology for multiple incipient fault diagnosis in transmission lines using QTA and Naïve Bayes classifier

Silva, Paula Renatha Nunes da; Vieira, P.; Costa, Carlos Araújo da

doi:10.1016/j.ijepes.2018.05.036

Cited by 37 publications

(18 citation statements)

References 54 publications

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“…The effectiveness is 95% for SVM, 91.66% for Naïve-Bayes, and 96.7% for PNN, where their response times are 0.03s, 0.012s, and 0.016s respectively. Da Silva et al [110] apply qualitative trend analysis and NB for the diagnosis of multiple failures in transmission lines. This hybrid diagnosis system can be generalized to deal with other types of faults along the transmission line.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

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Section: Hybrid Techniquesmentioning

confidence: 99%

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

et al. 2020

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“…Naive Bayesian classifier (NB), which assumes that each attribute is conditional independence and has the same impact on the classification, is an effective way to extract the main information. The Bayesian classifier as a data classification method has been developed to calculate probability of category differentiation in a series of practical problems [23]- [25]. For example, Da Silva et al [25] use NB for detection of transmission line damage.…”

Section: Introductionmentioning

confidence: 99%

“…The Bayesian classifier as a data classification method has been developed to calculate probability of category differentiation in a series of practical problems [23]- [25]. For example, Da Silva et al [25] use NB for detection of transmission line damage. In [24], it is used for detecting the welded joints based on vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Design of Hypervelocity-Impact Damage Evaluation Technique Based on Bayesian Classifier of Transient Temperature Attributes

et al. 2020

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With the rapid increasement of space debris on earth orbit, the hypervelocity-impact (HVI) of space debris can cause some serious damages to the spacecraft, which can affect the operation security and reliability of spacecraft. Therefore, the damage detection of the spacecrafts has become an urgent problem to be solved. In this paper, a method is proposed to detect the damage of spacecraft. Firstly, a variableinterval method is proposed to extract the effective information from the infrared image sequence. Secondly, in order to mine the physical meaning of the thermal image sequence, five attributes are used to construct a feature space. After that, a Naive Bayesian classifier is established to mine the information of different damaged areas. Then, a maximum interclass distance function is used choose the representative of each class. Finally, in order to visualize damaged areas, the Canny operator is used to extract the edge of the damage. In the experiment, ground tests are used to simulate hypervelocity impacts in space. Historical data of natural damaged material and artificial damaged material are used to build different classifiers. After that, the effective of classifiers is illustrated by accuracy, F-score and AUC. Then, two different types of materials are detected by proposed method, Independent Component Analysis (ICA) and Fuzzy C-means (FCM). The results show that the proposed method is more accurate than other methods. INDEX TERMS Damage evaluation, transient temperature attributes, hypervelocity impact, Bayesian classifier.

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“…The implementation of CBM systems for IMs [6] can reduce these risks, with the goal of detecting machine problems prior to failure [7], and can also help to optimize the schedule of maintenance stops, with the goal of reducing the production losses during the stop. From a broader point of view, CBM systems for IMs can be integrated in maintenance systems for electrical installations, along with CBMs for inverters [8], generators [9], transformers [10][11][12][13], power systems [14], transmission lines [15,16] or microgrids [17,18].…”

Section: Introductionmentioning

confidence: 99%

Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

Sapena-Bañó

Martínez-Román

Puche-Panadero

et al. 2018

International Journal of Electrical Power & Energy Systems

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Condition based maintenance systems (CBM) of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults, in order to help in developing new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, such models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. But the development of a fast, analytic model of the eccentric IM is a challenging task, due to the nonuniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position. The proposed method is validated first using a finite elements method (FEM) model, and then, through an experimental test-bed using commercial induction motors with a forced mixed eccentricity fault.

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A new methodology for multiple incipient fault diagnosis in transmission lines using QTA and Naïve Bayes classifier

Cited by 37 publications

References 54 publications

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

Design of Hypervelocity-Impact Damage Evaluation Technique Based on Bayesian Classifier of Transient Temperature Attributes

Induction machine model with space harmonics for fault diagnosis based on the convolution theorem

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