Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform Techniques
About sixty percent of the power in industries is consumed by induction machines, which implies induction machines are an integral part of industries. Even though these motors are stalwart and rugged in construction, they often experiences faults due to long time usage without maintenance. Bearing d...
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doaj-e3d5c2ae32e34d1480038b29f33796b92020-11-25T00:13:05ZengESRGroupsJournal of Electrical Systems1112-52091112-52092017-03-01131143159Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform TechniquesK.C.Deekshit Kompella0Dr.M. Venu Gopala Rao1Dr.R.Srinivasa Rao2EEE Department, PVPSIT, Kanuru, AP, EEE Department, PVPSIT, Kanuru, AP, India.EEE Department, JNTUK, Kakinada, AP, India. About sixty percent of the power in industries is consumed by induction machines, which implies induction machines are an integral part of industries. Even though these motors are stalwart and rugged in construction, they often experiences faults due to long time usage without maintenance. Bearing damage accounts 40% in the total faults and cause severe damage to the machine if unnoticed at nascent stage. So these faults should be continuously monitored for efficient operation, otherwise may cause severe damage to the machine. Conventional vibration monitoring is difficult due to requirement of high manpower and costly sensors. So motor current signature analysis (MCSA) is widely used for detection and localization of these faults. In this paper, the bearing faults are estimated by means of current frequency spectral subtraction using discrete wavelet transform. In addition to this, the current signature analysis after spectral subtraction is carried out using Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT) and Wavelet Packet Decomposition (WPD) and a comparative analysis is presented to estimate fault severity using statistical parameters. The proposed method is assessed based on current signatures obtained from a 2.2kW induction machine. The experimental results acknowledged the effectiveness of proposed method.http://journal.esrgroups.org/jes/papers/13_1_11.pdfBearing faultsinduction machinescurrent monitoringmotor current signature analysisspectral subtraction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
K.C.Deekshit Kompella Dr.M. Venu Gopala Rao Dr.R.Srinivasa Rao |
spellingShingle |
K.C.Deekshit Kompella Dr.M. Venu Gopala Rao Dr.R.Srinivasa Rao Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform Techniques Journal of Electrical Systems Bearing faults induction machines current monitoring motor current signature analysis spectral subtraction |
author_facet |
K.C.Deekshit Kompella Dr.M. Venu Gopala Rao Dr.R.Srinivasa Rao |
author_sort |
K.C.Deekshit Kompella |
title |
Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform Techniques |
title_short |
Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform Techniques |
title_full |
Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform Techniques |
title_fullStr |
Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform Techniques |
title_full_unstemmed |
Bearing Fault Diagnosis in 3 phase Induction Machine Using Current Spectral Subtraction With Different Wavelet Transform Techniques |
title_sort |
bearing fault diagnosis in 3 phase induction machine using current spectral subtraction with different wavelet transform techniques |
publisher |
ESRGroups |
series |
Journal of Electrical Systems |
issn |
1112-5209 1112-5209 |
publishDate |
2017-03-01 |
description |
About sixty percent of the power in industries is consumed by induction machines, which implies induction machines are an integral part of industries. Even though these motors are stalwart and rugged in construction, they often experiences faults due to long time usage without maintenance. Bearing damage accounts 40% in the total faults and cause severe damage to the machine if unnoticed at nascent stage. So these faults should be continuously monitored for efficient operation, otherwise may cause severe damage to the machine. Conventional vibration monitoring is difficult due to requirement of high manpower and costly sensors. So motor current signature analysis (MCSA) is widely used for detection and localization of these faults. In this paper, the bearing faults are estimated by means of current frequency spectral subtraction using discrete wavelet transform. In addition to this, the current signature analysis after spectral subtraction is carried out using Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT) and Wavelet Packet Decomposition (WPD) and a comparative analysis is presented to estimate fault severity using statistical parameters. The proposed method is assessed based on current signatures obtained from a 2.2kW induction machine. The experimental results acknowledged the effectiveness of proposed method. |
topic |
Bearing faults induction machines current monitoring motor current signature analysis spectral subtraction |
url |
http://journal.esrgroups.org/jes/papers/13_1_11.pdf |
work_keys_str_mv |
AT kcdeekshitkompella bearingfaultdiagnosisin3phaseinductionmachineusingcurrentspectralsubtractionwithdifferentwavelettransformtechniques AT drmvenugopalarao bearingfaultdiagnosisin3phaseinductionmachineusingcurrentspectralsubtractionwithdifferentwavelettransformtechniques AT drrsrinivasarao bearingfaultdiagnosisin3phaseinductionmachineusingcurrentspectralsubtractionwithdifferentwavelettransformtechniques |
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