UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 8 Issue 9
September-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2109158


Registration ID:
314886

Page Number

b478-b491

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Title

Fault Diagnosis in Rotor Blades using Machine Learning and Internet of Things

Abstract

Condition monitoring of machines is gaining importance in industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. The use of vibration and acoustic emission (AE) signals is quite common in the field of condition monitoring of rotating machinery. By comparing the signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, rotor rub, shaft misalignment, gear failures and bearing defects is possible. These signals can also be used to detect the incipient failures of the machine components, through the on-line monitoring system, reducing the possibility of catastrophic damage and the machine down time. A study is presented to compare the performance of rotor fault detection using two different classifiers, namely, artificial neural networks (ANNs) and support vector machines (SMVs). The RPM, current, voltage and lift force of a rotating machine with normal and defective rotor are processed for feature extraction. The extracted features from preprocessed signals are used as inputs to the classifiers for two-class (normal or fault) recognition. Further, this pretrained model is deployed on a cloud platform to carry out predictions over the cloud. This gives mobility to the system which allows us to monitor condition from anywhere. With the help of HTML, an interface is set up so user can interact with the instance. The web-based interface lets user enter values of required feature and outputs the condition of the rotor blade.

Key Words

Fault Diagnosis; Rotor Blades; kernel Support Vector Machine; Artificial Neural Network

Cite This Article

"Fault Diagnosis in Rotor Blades using Machine Learning and Internet of Things", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.b478-b491, September-2021, Available :http://www.jetir.org/papers/JETIR2109158.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Fault Diagnosis in Rotor Blades using Machine Learning and Internet of Things", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppb478-b491, September-2021, Available at : http://www.jetir.org/papers/JETIR2109158.pdf

Publication Details

Published Paper ID: JETIR2109158
Registration ID: 314886
Published In: Volume 8 | Issue 9 | Year September-2021
DOI (Digital Object Identifier):
Page No: b478-b491
Country: Bangalore, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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