AI-Based Health Monitoring System of Industrial Machines / (Record no. 603330)

000 -LEADER
fixed length control field 01605nam a22001817a 4500
003 - CONTROL NUMBER IDENTIFIER
control field NUST
040 ## - CATALOGING SOURCE
Original cataloging agency 0
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.382,AKH
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Akhtar, Muhammad Naveed
245 ## - TITLE STATEMENT
Title AI-Based Health Monitoring System of Industrial Machines /
Remainder of title Muhammad Naveed Akhtar, Maham Sajjad, Faisal Mahboob, Muzammil Abbas. (TCC-31 / BETE-56)
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture MCS, NUST
Name of producer, publisher, distributor, manufacturer Rawalpindi 2023
Date of production, publication, distribution, manufacture, or copyright notice Rawalpindi 2023
300 ## - PHYSICAL DESCRIPTION
Extent 48 p
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Industries are essential assets of an economy. Industrial machines such as rotors, motors, pumps etc. are building blocks of an industry. The maintenance of these bodies is important to keep them in working state. Many maintenance techniques are available for machine monitoring, but most of them include corrective maintenance, routine maintenance, and emergency maintenance. These techniques are not efficient enough in terms of being cost effective, time saving, and prolonging machines life expectancy.<br/>The world is moving towards new technology based on AI models. Such models are being used, in order to automate the industrial sector as well. One such innovative technology is AI based, predictive machine maintenance. Machine learning can be used to always monitor industrial machines via anomaly detection. It works on data acquired from machines, which can be vibrational data,<br/>sound data and so on. This innovation is new and is helping the industrial sector in becoming more productive and efficient.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element UG EE Project
690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN)
Topical term or geographic name as entry element TCC-31 / BETE-56
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Supervisor Dr. Ata Ur Rehman
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Koha item type Thesis
Holdings
Withdrawn status Permanent Location Current Location Shelving location Date acquired Full call number Barcode Koha item type
  Military College of Signals (MCS) Military College of Signals (MCS) Thesis 10/05/2023 621.382,AKH MCSPTC-451 Project Report
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