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DTSTAMP:20221021T170048Z
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DTSTART;TZID=America/Chicago:20221021T110000
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DESCRIPTION:Available online health monitoring systems (HMS) using mechanic
 al signals such as vibration & temperature for commercial air-conditioning
  (HVAC/ACMV) systems detect some of the critical faults only at high sever
 ity levels resulting in higher operation and maintenance costs. Moreover\,
  multiple monitoring systems are required one for each single component at
  the sub-system level further decreasing affordability. Our aim is to deve
 lop a unique\, single hybrid scheme involving both feature extraction and 
 classification using electrical signals-based holistic HMS for various typ
 es of critical faults of an HVAC/ACMV and its associated component. The de
 veloped approach is capable of detecting anomalies at an early stage and p
 rovides efficient condition monitoring and predictive maintenance (PdM) sc
 heduling in advance using mostly electrical signals in a non-intrusive way
 .\n\nSpeaker(s): Dr. Hasmat Malik\, \n\nVirtual: https://events.vtools.iee
 e.org/m/323107
LOCATION:Virtual: https://events.vtools.ieee.org/m/323107
ORGANIZER:irfankhan@tamu.edu
SEQUENCE:1
SUMMARY:Data-Driven Intelligent Approach for Condition Monitoring of HVAC/A
 CMV System
URL;VALUE=URI:https://events.vtools.ieee.org/m/323107
X-ALT-DESC:Description: <br /><p>Available online health monitoring systems
  (HMS) using mechanical signals such as vibration &amp\; temperature for c
 ommercial air-conditioning (HVAC/ACMV) systems detect some of the critical
  faults only at high severity levels resulting in higher operation and mai
 ntenance costs. Moreover\, multiple monitoring systems are required one fo
 r each single component at the sub-system level further decreasing afforda
 bility. Our aim is to develop a unique\, single hybrid scheme involving bo
 th feature extraction and classification using electrical signals-based ho
 listic HMS for various types of critical faults of an HVAC/ACMV and its as
 sociated component. The developed approach is capable of detecting anomali
 es at an early stage and provides efficient condition monitoring and predi
 ctive maintenance (PdM) scheduling in advance using mostly electrical sign
 als in a non-intrusive way.&nbsp\;</p>
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