Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine

Leksir, Yazid Laib Dit; Mansour, M.; Moussaoui, Abdelkrim

doi:10.1016/j.infrared.2017.12.015

Cited by 48 publications

(23 citation statements)

References 17 publications

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“…Previous studies have adopted several conventional algorithms for classifying sitting postures such as the Hidden Markov Models (HMM), Naïve Bayes (NB) classifier and k-nearest neighbor (kNN) classifier [8][9][10]. Conventional machine learning algorithms including neural network, support vector machine (SVM), and kNN are still being adopted in various research objectives such as fault diagnosis, wind speed prediction, and thermal anomalies identification [11][12][13][14]. Recently, it has been proven that high performance can be obtained by using deep learning in various research fields such as image processing, and speech recognition.…”

Section: Introductionmentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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Section: Introductionmentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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“…Although the training precision is reduced to some extent, it is conductive to avoiding over learning and achieving the accuracy of detection results for botnets. We also present our results with Support Vector Machines (SVM) that is a supervised learning approach and it is one of the three major learning types in machine learning [32][33][34][35][36][37][38]. SVMs are known to be "large margin classifiers" and we expect them to perform well in situations which require good generalization.…”

Section: Classification Calculationmentioning

confidence: 99%

A Feature Extraction Method for P2P Botnet Detection Using Graphic Symmetry Concept

Yang

Wang

2019

Symmetry

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A DDoS (Distributed Denial of Service) attack makes use of a botnet to launch attacks and cause node congestion of wireless sensor networks, which is a common and serious threat. Due to the various kinds of features required in a Peer-to-Peer (P2P) botnet for DDoS attack detection via current machine learning methods and the failure to effectively detect encrypted botnets, this paper extracts the data packet size and the symmetric intervals in flow according to the concept of graphic symmetry. Combined with flow information entropy and session features, the frequency domain features can be sorted so as to obtain features with better correlations, which solves the problem of multiple types of features required for detection. Information entropy corresponding to the flow size can distinguish an encrypted botnet. This method is implemented through machine learning techniques. Experimental results show that the proposed method can detect the P2P botnet for DDoS attack and the detection accuracy is higher than that of traditional feature detection.

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“…For the methods of fault recognition, neural networks (NNs) [ 23 ] and support vector machine (SVM) [ 24 , 25 , 26 ] are commonly used. Although NNs have good anti-noise and self-learning ability, they require a large number of samples to train, while HVCBs cannot operate frequently due to their working characteristics.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Fault Diagnosis of HVCBs Based on Multi-Feature Entropy Fusion and Hybrid Classifier

Wan

Chen²,

Dou³

et al. 2018

Entropy

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As high-voltage circuit breakers (HVCBs) are directly related to the safety and the stability of a power grid, it is of great significance to carry out fault diagnoses of HVCBs. To accurately identify operating states of HVCBs, a novel mechanical fault diagnosis method of HVCBs based on multi-feature entropy fusion (MFEF) and a hybrid classifier is proposed. MFEF involves the decomposition of vibration signals of HVCBs into several intrinsic mode functions using variational mode decomposition (VMD) and the calculation of multi-feature entropy by the integration of three Shannon entropies. Principle component analysis (PCA) is then used to reduce the dimension of the multi-feature entropy to achieve an effective fusion of features for selecting the feature vector. The detection of an unknown fault in HVCBs is achieved using support vector data description (SVDD) trained by normal-state samples and specific fault samples. On this basis, the identification and classification of the known states are realized by the support vector machine (SVM). Three faults (i.e., closing spring force decrease fault, buffer spring invalid fault, opening spring force decrease fault) are simulated on a real SF6 HVCB to test the feasibility of the proposed method. The detection accuracies of the unknown fault are 100%, 87.5%, and 100% respectively when each of the three faults is assumed to be the unknown fault. The comparative experiments show that SVM has no ability to detect the unknown fault, and that one-class support vector machine (OCSVM) has a weaker ability to detect the unknown fault than SVDD. For known-state classification, the adoption of the MFEF method achieved an accuracy of 100%, while the use of a single-feature method only achieved an accuracy of 75%. These results indicate that the proposed method combining MFEF with hybrid classifier is thus more efficient and robust than traditional methods.

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Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine

Cited by 48 publications

References 17 publications

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

A Feature Extraction Method for P2P Botnet Detection Using Graphic Symmetry Concept

Mechanical Fault Diagnosis of HVCBs Based on Multi-Feature Entropy Fusion and Hybrid Classifier

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