UGC Approved Journal no 63975(19)

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

Volume 8 Issue 4
April-2021
eISSN: 2349-5162

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

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Published Paper ID:
JETIR2104196


Registration ID:
307853

Page Number

738-742

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Title

Machine Failure Prediction Using Machine Learning

Abstract

Industrial equipment performance control and failure prediction are important not just for the quality of the produced material, but also for the amount of time and money saved in overall maintenance. This project aims to monitor the evolution of AI/ML techniques for equipment fault prediction in industries over time. The topics covered in this paper include machine learning algorithms, use cases, and principles related to the application of such technology in a variety of industries such as software and hardware. This survey looks at early research from the late 1980s to the early 2000s, as well as recent research from the early 2000s to 2017, and the most recent research from the last two years. It can be inferred that this project offers a detailed review of various machine learning and artificial intelligence (ML/AI) approaches used in the Industrial Manufacturing domain. LSTM was discovered to be one of the most commonly used processes.

Key Words

Machine Failure Prediction Using Machine Learning

Cite This Article

"Machine Failure Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 4, page no.738-742, April-2021, Available :http://www.jetir.org/papers/JETIR2104196.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

"Machine Failure Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 4, page no. pp738-742, April-2021, Available at : http://www.jetir.org/papers/JETIR2104196.pdf

Publication Details

Published Paper ID: JETIR2104196
Registration ID: 307853
Published In: Volume 8 | Issue 4 | Year April-2021
DOI (Digital Object Identifier):
Page No: 738-742
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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