Iot-based condition monitoring system for predictive maintenance of induction motors / (Record no. 30687)

MARC details
000 -LEADER
fixed length control field 02358nam a2200325 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field CSPC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251010135458.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 251010s2024 ph a b 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency CSPC
Language of cataloging eng
Transcribing agency CSPC
Description conventions rda
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) UTH BSME A149i 2024
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Abion, Cristy H.
Relator term author.
245 10 - TITLE STATEMENT
Title Iot-based condition monitoring system for predictive maintenance of induction motors /
Statement of responsibility, etc. Cristy H. Abion, [and 4 others].
300 ## - PHYSICAL DESCRIPTION
Extent 135 leaves.
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
338 ## - CARRIER TYPE
Source rdacarrier
Carrier type term volume
502 ## - DISSERTATION NOTE
Dissertation note Undergraduate Thesis (BSME) - Mechanical Engineering.
Name of granting institution Camarines Sur Polytechnic Colleges, 2024.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographic references.
520 3# - SUMMARY, ETC.
Summary, etc. The reliability of induction motors is crucial in industrial operations, as unexpected failures can lead to costly downtimes and production losses. This study presents an IoT-Based Condition Monitoring System designed for the predictive maintenance of induction motors, leveraging real-time data collection and cloud-based analysis. The system employs temperature and current sensors interfaced with a NodeMCU v3 microcontroller, transmitting data to a MySQL cloud database for monitoring and fault prediction. By integrating predictive analytics, the system can detect anomalies and schedule maintenance proactively, reducing unplanned breakdowns. Experimental results demonstrate that the proposed system enhances fault detection accuracy, improves maintenance planning, and extends motor lifespan. Compared to traditional monitoring methods, the loT-based approach ensures higher reliability, reduced maintenance costs, and improved operational efficiency. This research contributes to the advancement of smart industrial automation by providing a scalable, cost-effective solution for predictive maintenance in various industries.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Flores, John Roland L.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Gerobin, Paul Christian L.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Pana, Yoran Anton J.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tombado, Ralph Alfred P.
Relator term author.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Gutierrez, Christopher C.
Relator term adviser.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Merca, Rodolfo Jr., A.
Relator term chair, panelist.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Anore, Pia Margarette L.
Relator term panelist.
720 ## - ADDED ENTRY--UNCONTROLLED NAME
Name Peñales, Gilbert A.
Relator term panelist.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Unpublished Materials
Classification part A149i
Item part BSME
Call number prefix UTH
Call number suffix 2024
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     Main Library Main Library Undergraduate Thesis Section 10/10/2025   UTH BSME A149i 2024 006891UM 10/10/2025 1-1 10/10/2025 Unpublished Materials