DocumentCode :
252614
Title :
Classification of electrical appliances using magnetic field and probabilistic neural network
Author :
Mohd Rosdi, Nurul Aishah ; Nordin, Farah Hani ; Ramasamy, Agileswari K. ; Ahmad Mustafa, Nur Badariah
Author_Institution :
Dept. of Electron. & Commun. Eng., Univ. Tenaga Nasional, Kajang, Malaysia
fYear :
2014
fDate :
11-12 Aug. 2014
Firstpage :
268
Lastpage :
273
Abstract :
Many researches have proven that power lines and electrical appliances do emit electromagnetic fields and can be harmful to human´s health. However, research on the effect of the magnetic fields on human´s health is not yet conclusive. Instead of letting the magnetic fields emit by the electrical appliances be wasted, this paper aims to use the magnetic fields to classify or identify the electrical appliances being used. Table fans, blenders and hairdryers are the electrical appliances used for this purpose where they are divided into three different categories of usage i.e. (i) used less than 1year (ii) used between 1 to 5 years and (iii) used more than 5 years. The magnetic fields are measured from all the nine appliances. Then, the features of the magnetic fields are extracted and trained offline using the Probabilistic Neural Network (PNN). From the results, it is shown that the PNN is able to identify the type of electrical appliance being used regardless of the appliances years of usage using magnetic fields emitted by the appliances.
Keywords :
condition monitoring; domestic appliances; domestic safety; electromagnetic fields; neural nets; probability; production engineering computing; electrical appliances classification; electromagnetic fields; probabilistic neural network; Electrical products; Feature extraction; Frequency measurement; Home appliances; Magnetic field measurement; Magnetic fields; Neural networks; Classification; Electrical Appliances; Magnetic Field; Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and System Graduate Research Colloquium (ICSGRC), 2014 IEEE 5th
Conference_Location :
Shah Alam
Print_ISBN :
978-1-4799-5691-3
Type :
conf
DOI :
10.1109/ICSGRC.2014.6908735
Filename :
6908735
Link To Document :
بازگشت