Detection of Broken Bars in Induction Motors Using a Neural Network

M. Moradian,M. Ebrahimi,M. Danesh,M. bayat 전력전자학회 2006 JOURNAL OF POWER ELECTRONICS Vol.6 No.3

This paper presents a method based on neural networks to detect the broken rotor bars and end rings of squirrel cage induction motors. At first, detection methods are studied, and then traditional methods of fault detection and dynamic models of induction motors by using winding function model are introduced. In this method, all of the stator slots and rotor bars are considered, thus the performance of the motor in healthy situations or breakage in each part can be checked. The frequency spectrum of current signals is derived by using Fourier transformation and is analyzed in different conditions. In continuation, an analytical discussion and a simple algorithm are presented to detect the fault. This algorithm is based on neural networks. The neural network has been trained by using information of a 1.1 ㎾ induction motor. This system has been tested with a different amount of load torque, and it is capable of working on-line and of recognizing all normal and ill conditions.

Carbon Sequestration in the Plantations of Chittagong Hilly Areas of Bangladesh

( Danesh Miah ),( M. Farid Uddin ),( M. Kalimuddin Bhuiyan ),( Man Yong Shin ) 한국산림과학회 2004 한국산림과학회지 Vol.93 No.4

N/A The study was conducted in the plantations of 13 tree species of Chittagong hilly areas, Bangladesh, with the objective of quantifying carbon sequestration. It was revealed that there was a gross 191 ton/ha carbon stock in the studied plantations. The highest soil(including humus) carbon content(113 ton/ha) was found in the Jarul(Lagerstroemia speciosa) plantation and the lowest(83 ton/ha) was found in the Pine(Pinus caribaea) plantation. Most of the plantations were found to possess the litter carbon contents as 3 ton/ha except Mehegoni(Swietenia mahagoni), Pine(Pinus caribaea) and Dhakijam(Syzygium grande) plantation, which possessed only 2 ton/ha carbon. The results represented that 3.13 ton/ha/year for fuelwood collection and 1.24 ton/ha/year for litter collection by the surrounding people, carbon were lost from the plantations. The net increment of carbon in the plantations was 3.86 ton/ha/year. The study revealed a promising result of carbon sequestration by the plantation species in the Chittagong hilly areas, which shows a potential of plantation species to participate in the international carbon trading.

Greenhouse Gas Emissions from Municipal Solid Waste Management of the Chittagong City Corporation of Bangladesh

( M Danesh Miah ),( M Abubokor Siddik ) 한국폐기물자원순환학회(구 한국폐기물학회) 2015 한국폐기물자원순환학회 3RINCs초록집 Vol.2015 No.-

Municipal solid waste (MSW) management has an impact on climate generating greenhouse gases (GHG). To quantify GHG emission from MSW management activities (transportation, composting, recycling and land-filling) of the Chittagong City Corporation (CCC), this study was conducted in Chittagong city of Bangladesh. Data on MSW management activities of the CCC were collected through conducting a structured questionnaire survey. To collect data about recycling, another questionnaire survey was conducted including all junkshops near to the dumping sites (Anandabazar and Arefin Nogor) of the CCC. The study found that composting and recycling of MSW have a positive contribution in reducing GHG emission. Transportation and land-filling of MSW have a contribution in GHG emission. The amount of GHG emission from existing MSW management system of the CCC is 31,904.68 tons of CO₂-eq per month. The life cycle assessment (LCA) study on composting shows that, the reduction of GHG emission is 3.66 tons of CO₂-eq per ton of produced compost. The study is expected to contribute in the field of climate change mitigation in Bangladesh.

Detection of Broken Bars in Induction Motors, Using Neural Network

M. Ebrahimi,M. Moradian,M. Danesh,M. bayat 전력전자학회 2006 JOURNAL OF POWER ELECTRONICS Vol.6 No.3

This paper presents a method based on neural networks to detect the broken rotor bars and end rings of squirrel cage induction motors. At first, detection methods are studied, and then traditional methods of fault detection and dynamic models of induction motors by using winding function model are introduced. In this method, all of the stator slots and rotor bars are considered, thus the performance of the motor in healthy situations or breakage in each part can be checked. The frequency spectrum of current signals is derived by using Fourier transformation and is analyzed in different conditions. In continuation, an analytical discussion and a simple algorithm are presented to detect the fault. This algorithm is based on neural networks. The neural network has been trained by using information of a 1.1 KW induction motor. This system has been tested with a different amount of load torque, and it is capable of working on-line and of recognizing all normal and ill conditions.

Detection of Broken Bars in Induction Motors, Using Neural Network

M. Moradiyan,M. Ebrahimi,M. Danesh,M. baiat 전력전자학회 2004 ICPE(ISPE)논문집 Vol.- No.-

This paper presents a method based on neural network to detect the broken rotor bars and end rings of squirrel cage induction motors. At first, detection methods are studied, and then traditional methods of fault detection and dynamic model of induction motors by using winding function model are introduced. In this method, all of the stator slots and rotor bars are considered, thus the performance of the motor in healthy situation or breakage in each part can be checked. The frequency spectrum of current signals is derived by using Fourier transform and is analyzed in different conditions. In continuation, an analytical discussion and a simple algorithm are presented to detect the fault. This algorithm is based on neural networks. The neural network has been trained by using information of a 1.1 KW induction motor. This system has been<br/> tested with a different amount of load torque, and it is capable<br/> of working on-line and of recognizing all normal and ill conditions.

Short Communication : Carbon Sequestration by the Indigenous Tree Species in the Reforestation Program in Bangladesh-Aphanamixis Polystachya Wall. and Parker

( Md. Danesh Miah ),( M. Farid Uddin ),( M. K. Bhuiyan ),( Masao Koike ),( Man Yong Shin ) 한국임학회 2009 Forest Science And Technology Vol.5 No.2

Carbon Sequestration in the Plantations of Chittagong Hilly Areas of Bangladesh

Md. Danesh Miah,M. Kalimuddin Bhuiyan,신만용,M. Farid Uddin 한국산림과학회 2004 한국산림과학회지 Vol.93 No.4

The study was conducted in the plantations of 13 tree species of Chittagong hilly areas, Bangladesh, with the objective of quantifying carbon sequestration. It was revealed that there was a gross 191 ton/ha carbon stock in the studied plantations. The highest soil(including humus) carbon content(113 ton/ha) was found in the Jarul(Lagerstroemia speciosa) plantation and the lowest(83 ton/ha) was found in the Pine(Pinus caribaea) plantation. Most of the plantations were found to possess the litter carbon contents as 3 ton/ha except Mehegoni(Swietenia mahagoni), Pine(Pinus caribaea) and Dhakijam(Syzygium grande) plantation, which possessed only 2 ton/ha carbon. The results represented that 3.13 ton/ha/year for fuelwood collection and 1.24 ton/ha/year for litter collection by the surrounding people, carbon were lost from the plantations. The net increment of carbon in the plantations was 3.86 ton/ha/year. The study revealed a promising result of carbon sequestration by the plantation species in the Chittagong hilly areas, which shows a potential of plantation species to participate in the international carbon trading.

Structural characteristics of Cu/Ti bimetal composite produced by accumulative roll-bonding (ARB)

Hosseini, M.,Pardis, N.,Danesh Manesh, H.,Abbasi, M.,Kim, D.I. Elsevier Ltd 2017 Materials & Design Vol.113 No.-

Cu/Ti nanostructured bimetal composite was produced by accumulative roll-bonding (ARB) technique and the structural characteristics of this composite were studied both experimentally and by finite element analysis. Macrostructure evolution of the composite layers was investigated by optical and scanning electron microscopes. In addition, transmission electron microscopy (TEM) as well as energy-dispersive X-ray spectroscopy (EDX) techniques were utilized for detailed microstructural investigations. Different finite element simulations were designed based on the experimental results. The results were combined with macrostructural observations to gain a better insight into the fragmentation mechanism of the Ti reinforcements. Necking of the Ti layers was observed after 3cycles of ARB processing which was found to happen at shear bands, after sufficient work-hardening of the constituents. Further processing resulted in fragmentation and distribution of lens shaped Ti constituents. It was found that such a shape evolution makes it more difficult to deform the Ti segments by further straining and consequently, a more localized strain concentration happens within the Cu matrix near these segments. A significant grain refinement was observed by TEM investigation of the highly strained composite. ARB processing of Cu/Ti bimetal composite resulted in nanostructured Ti reinforcements distributed within an ultrafine-grained Cu matrix.

Detection of Broken Bars in Induction Motors Using a Neural Network

Moradian M.,Ebrahimi M.,Danesh M.,Bayat M. The Korean Institute of Power Electronics 2006 JOURNAL OF POWER ELECTRONICS Vol.6 No.3

This paper presents a method based on neural networks to detect the broken rotor bars and end rings of squirrel cage induction motors. At first, detection methods are studied, and then traditional methods of fault detection and dynamic models of induction motors by using winding function model are introduced. In this method, all of the stator slots and rotor bars are considered, thus the performance of the motor in healthy situations or breakage in each part can be checked. The frequency spectrum of current signals is derived by using Fourier transformation and is analyzed in different conditions. In continuation, an analytical discussion and a simple algorithm are presented to detect the fault. This algorithm is based on neural networks. The neural network has been trained by using information of a 1.1 KW induction motor. This system has been tested with a different amount of load torque, and it is capable of working on-line and of recognizing all normal and ill conditions.

Design of a virtual acoustic sensor for estimating the radiated sound of cylindrical shells

A. Loghmani,M. Danesh,M.K. Kwak,M. Keshmiri 한국소음진동공학회 2015 한국소음진동공학회 학술대회논문집 Vol.2015 No.10

In this paper, a virtual acoustic sensor is designed and evaluated experimentally to be used instead of microphones in active noise control of vibrating cylindrical shells. Dynamic motion equations of a cylindrical shell along with the effect of bonded piezoelectric patches are derived based on the Hamilton’s principle and Rayleigh-Ritz method. The Sanders shell theory is used to relate the strains to the displacements of the cylinder. Modal structural acoustic sensing technique estimates the far-field radiated sound pressure of the cylinder. Experimental frequency response function (FRF) of the actual microphone at various locations are obtained and compared to that of the virtual one. The theoretical estimated sound pressure is in good agreement with the experimental results.