Application of neural networks in detection of abnormal brain perfusion regions

Hachaj, Tomasz; Ogiela, Marek R.

doi:10.1016/j.neucom.2013.04.030

Cited by 32 publications

(10 citation statements)

References 24 publications

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“…Scientists proposed many methods of classification in the literature [2,4,7,9,16,17,21,23,26,[31][32][33][34]. Linear Support Vector Machine (LSVM) classified feature vectors by finding the best hyperplane that separated all vectors of one class from those of the other class.…”

Section: Linear Support Vector Machinementioning

confidence: 99%

Recognition of acoustic signals of induction motor using FFT, SMOFS-10 and LSVM

Głowacz¹

2015

EiN

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Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl IntroductionThe induction motors are used in various industries such as: mining, fuel, metallurgical. These motors have low maintenance and low price. To reduce maintenance costs scientists analyze mechanical properties of materials [18,20,25,30].They also develop early fault detection methods [1,5,6,[10][11][12][13][14][15]. Especially non-invasive methods are developed such as : acoustic, vibrations, thermal, magnetic [3, 14, 19, 27, 28, 29, 35, 36, 38]. Noninvasive methods are capable to diagnose early faults without disassembly the induction motor. Many of them used patterns recognition and signal processing to identify type of fault.Incipient faults of motors may change into damages and may stop the production line. The stopped production line causes losses of resources and production time. It increases the cost of operation and maintenance.This article deals with a recognition method of early faults of induction motor. Proposed method uses Fast Fourier Transform (FFT), shortened method of radio frequencies selection (SMoFS-10) and Linear Support Vector Machine (LSVM). Proposed approach of recognition of acoustic signal of induction motorThe proposed approach of recognition of acoustic signal of induction motor contained two processes: a pattern creation process and an identification process. These processes were needed for proper recognition of acoustic signal (Fig. 1).The first of them recorded acoustic signal of motor with the help of a sound card and a microphone [22]. Acoustic signal was converted GLOWACZ A. Recognition of acoustic signals of induction motor using ffT, smOfs-10 and Lsvm. Eksploatacja i Niezawodnosc -maintenance and Reliability 2015; 17 (4)

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Section: Linear Support Vector Machinementioning

confidence: 99%

Recognition of acoustic signals of induction motor using FFT, SMOFS-10 and LSVM

Głowacz¹

2015

EiN

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“…Recently, many classification methods were developed in the literature [32][33][34][35][36][37][38][39][40][41][42][43][44][45]. The artificial intelligence in diagnostics of electric machines was discussed [15], [25].…”

Section: Bayes Classifiermentioning

confidence: 99%

Recognition of Thermal Images of Direct Current Motor with Application of Area Perimeter Vector and Bayes Classifier

Głowacz¹,

Głowacz²,

Głowacz³

2015

Measurement Science Review

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Infrared thermography can measure the temperature of a surface remotely. In this article authors present a diagnostic method of incipient fault detection. The proposed approach is based on pattern recognition. It uses monochrome thermal images of the rotor with the application of an area perimeter vector and a Bayes classifier. The investigations have been carried out for direct current motor without faults and motor with shorted rotor coils. The measurements were performed in the laboratory. The efficiency of recognition using the area perimeter vector and the Bayes classifier was 100 %. The investigations show that the method based on recognition of thermal images can be profitable for engineers. The proposed method can be applied in mining, metallurgy, fuel industry and in factories where electrical motors are used.

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“…Engineers and scientists developed a great variety of feature extraction and classification methods [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]. Classification methods are often based on artificial intelligence.…”

Section: G Neural Network With Backpropagation Algorithmmentioning

confidence: 99%

“…Classification methods are often based on artificial intelligence. This intelligence can be represented by backpropagation neural network [40][41][42][43][44][45][46][47].…”

Section: G Neural Network With Backpropagation Algorithmmentioning

confidence: 99%

Recognition of Acoustic Signals of Synchronous Motors with the Use of MoFS and Selected Classifiers

Głowacz

2015

Measurement Science Review

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This paper proposes an approach based on acoustic signals for detecting faults appearing in synchronous motors. Acoustic signals of a machine were used for fault detection. These faults contained: broken coils and shorted stator coils. Acoustic signals were used to assess the usefulness of early fault diagnostic of synchronous motors. The acoustic signal recognition system was based on methods of data processing: normalization of the amplitude, Fast Fourier Transform (FFT), method of frequency selection (MoFS), backpropagation neural network, classifier based on words coding, and Nearest Neighbor classifier. A plan of study of acoustic signals of synchronous motors was proposed. Software of acoustic signal recognition of synchronous motors was implemented. Four states of a synchronous motor were used in analysis. A pattern creation process was carried out for 28 training samples of noise. An identification process was carried out for 60 test samples. This system can be used to diagnose synchronous motors and other electrical machines.

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Application of neural networks in detection of abnormal brain perfusion regions

Cited by 32 publications

References 24 publications

Recognition of acoustic signals of induction motor using FFT, SMOFS-10 and LSVM

Recognition of acoustic signals of induction motor using FFT, SMOFS-10 and LSVM

Recognition of Thermal Images of Direct Current Motor with Application of Area Perimeter Vector and Bayes Classifier

Recognition of Acoustic Signals of Synchronous Motors with the Use of MoFS and Selected Classifiers

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