BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20211225T123158Z
UID:38041044-2B4A-4C59-8E42-CA49D062F734
DTSTART;TZID=Asia/Kolkata:20211220T180000
DTEND;TZID=Asia/Kolkata:20211220T190000
DESCRIPTION:CIS Chapter\, IEEE UP Section (CIS11) organized a Lecture Serie
 s on "Computational Intelligence". The speaker of the lecture was Dr. Sona
 l Dixit\, Dept. of Electrical Engineering\, Indian Institute of Technology
  Kanpur\, India.\n\nSpeaker(s): Sonal Dixit\, \n\nAgenda: \nAbstract of th
 e talk:\n\nMaintenance of major industrial assets like an air compressor\,
  engine\, bearing\, gearbox\, etc. is one of the most crucial aspects of i
 ndustrial plants and it directly affects the production\, safety\, downtim
 e\, and net revenue. Condition-based Maintenance (CBM) is the most widely 
 adopted maintenance approach due to its reliability and proactive performa
 nce. Fault diagnosis and Remaining Useful Life (RUL) prediction are the ma
 jor components of the CBM. The fault diagnosis system notifies the occurre
 nce of a fault\, its type\, and location and RUL assesses the criticality 
 of failure and predicts the remaining time of the machine before it requir
 es maintenance. Data-driven-based approaches have been widely adopted for 
 fault diagnosis purposes. In these methods firstly machine data is collect
 ed\, featured\, and analyzed using machine learning algorithms. The perfor
 mance of conventional data-driven fault diagnosis frameworks relies upon t
 he quality of extracted features and quantity of training data. To obtain 
 good quality features from data prior knowledge and domain expertise is ne
 eded. Moreover\, it is challenging to perform feature processing for large
  data. To resolve these issues deep learning-based fault diagnosis framewo
 rks will be discussed along with the future directions.\n\nKanpur\, Uttar 
 Pradesh\, India\, 208016\, Virtual: https://events.vtools.ieee.org/m/29668
 6
LOCATION:Kanpur\, Uttar Pradesh\, India\, 208016\, Virtual: https://events.
 vtools.ieee.org/m/296686
ORGANIZER:vrij@iiita.ac.in
SEQUENCE:0
SUMMARY:Expert Lecture on "Deep Learning Framework for Intelligent Fault Di
 agnosis of Rotary Machines"
URL;VALUE=URI:https://events.vtools.ieee.org/m/296686
X-ALT-DESC:Description: <br /><p>CIS Chapter\, IEEE UP Section (CIS11) orga
 nized a Lecture Series on "Computational Intelligence". The speaker of the
  lecture was Dr. Sonal Dixit\, Dept. of Electrical Engineering\, Indian In
 stitute of Technology Kanpur\, India.</p><br /><br />Agenda: <br /><h4><st
 rong>Abstract of the talk:</strong></h4>\n<p align="justify">Maintenance o
 f major industrial assets like an air compressor\, engine\, bearing\, gear
 box\, etc. is one of the most crucial aspects of industrial plants and it 
 directly affects the production\, safety\, downtime\, and net revenue. Con
 dition-based Maintenance (CBM) is the most widely adopted maintenance appr
 oach due to its reliability and proactive performance. Fault diagnosis and
  Remaining Useful Life (RUL) prediction are the major components of the CB
 M. The fault diagnosis system notifies the occurrence of a fault\, its typ
 e\, and location and RUL assesses the criticality of failure and predicts 
 the remaining time of the machine before it requires maintenance. Data-dri
 ven-based approaches have been widely adopted for fault diagnosis purposes
 . In these methods firstly machine data is collected\, featured\, and anal
 yzed using machine learning algorithms. The performance of conventional da
 ta-driven fault diagnosis frameworks relies upon the quality of extracted 
 features and quantity of training data. To obtain good quality features fr
 om data prior knowledge and domain expertise is needed. Moreover\, it is c
 hallenging to perform feature processing for large data. To resolve these 
 issues deep learning-based fault diagnosis frameworks will be discussed al
 ong with the future directions.</p>
END:VEVENT
END:VCALENDAR