Si-Detector Developments at BARC Dr. S.K.Kataria Electronics Division, BARC, Mumbai.
Smt. A. Mayya, CnID, BARC Advances in Dependability Dr. Anup … · 2019-12-11 · Dr. Manoj Kumar,...
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Organized by Board of Research in Nuclear Sciences & Bhabha Atomic Research Centre, Mumbai Patrons Shri P.P. Marathe, AD, E&IG Shri P.R. Patil, AD, RDDG Advisory Committee Smt. A. Mayya, CnID, BARC Dr. Anup Bhattacharjee, RCnD, BARC Dr. J. Chattopadhyay, RSD, BARC Organizing Committee Dr. Manoj Kumar, CnID, BARC Dr. Gopika Vinod, RSD, BARC Dr. M. Hari Prasad, RSD, BARC Confirmed Speakers Prof. P.S.V. Natraj, IIT Bombay, India Prof. A.K. Verma, WNUAS, Norway Prof. N.S. Vyas, IIT Kanpur, India Prof. Uday Kumar, LTU, Sweden Dr. Curtis Smith, INL, USA Dr. M.K. Mandal, IIT Kgp, India Dr. D. Datta, BARC, India Dr. P.V. Varde, BARC, India Dr. Kallol Roy, BHAVINI, India BRNS Theme Meeting on Advances in Dependability Prediction of Engineering Systems using AI/Machine Learning December 13-14, 2019 Venue Multi Purpose Hall, Training School Hostel, Anushaktinagar, Mumbai - 400 094 Objectives Dependability of equipment/engineering systems is critical for asset intensive industries such as Energy, Transportation and Health Care. Dependability which encompasses meas- ures such as Reliability, Availability, Maintainability, Safety and Security, is an important attribute in minimizing the risk to people, society and environment. Engineering data ana- lytics along with an understanding of Physics of Failures is becoming an important tool in prognostics. These techniques are playing a major role in planning, predicting and maintain- ing critical equipment for safe and economic operation of these industries. Artificial intelligence in recent years had a great advancement owing to emergence of edge computing devices, parallel processing techniques and machine learning (ML) methods. One of the key factors responsible for the improvement in neural network’s accuracy and the improvement of the com- plexity of tasks they can solve is the dramatic increase in the size of the networks that can be used with the advent of low cost/low power computing devices for data collection and analysis. In this context, a more rigorous dependability analysis with better accuracy is now possible involving large data analytics, physics of failures and AI/ML techniques to build prediction models that help in early failure prediction of equipment. This paradigm shift in dependability analysis aids in planning a safe envelop of plan operation with good economics. Keeping in view the current scenario and future require- ments the theme meeting topics have been designed to dis- cuss the state-of-the-art methods, models and techniques to predict dependability of engineering systems. The topics would help the younger engineers as well as practitioners to take up these challenging areas for better design & analysis of future generation NPPs. The theme meeting comprises of invited talks from re- nowned experts in the field, and tutorials on practical aspects on the subject matter.
Transcript of Smt. A. Mayya, CnID, BARC Advances in Dependability Dr. Anup … · 2019-12-11 · Dr. Manoj Kumar,...
Organized by Board of Research in Nuclear Sciences
& Bhabha Atomic Research Centre,
Mumbai
Patrons
Shri P.P. Marathe, AD, E&IG Shri P.R. Patil, AD, RDDG
Advisory Committee
Smt. A. Mayya, CnID, BARC Dr. Anup Bhattacharjee, RCnD, BARC Dr. J. Chattopadhyay, RSD, BARC
Organizing Committee
Dr. Manoj Kumar, CnID, BARC Dr. Gopika Vinod, RSD, BARC Dr. M. Hari Prasad, RSD, BARC
Confirmed Speakers
Prof. P.S.V. Natraj, IIT Bombay, India
Prof. A.K. Verma, WNUAS, Norway
Prof. N.S. Vyas, IIT Kanpur, India
Prof. Uday Kumar, LTU, Sweden
Dr. Curtis Smith, INL, USA
Dr. M.K. Mandal, IIT Kgp, India
Dr. D. Datta, BARC, India
Dr. P.V. Varde, BARC, India
Dr. Kallol Roy, BHAVINI, India
BRNS Theme Meeting on
Advances in Dependability
Prediction of Engineering
Systems using AI/Machine
Learning
December 13-14, 2019
Venue Multi Purpose Hall, Training School Hostel,
Anushaktinagar, Mumbai - 400 094
Objectives
Dependability of equipment/engineering systems is critical
for asset intensive industries such as Energy, Transportation
and Health Care. Dependability which encompasses meas-
ures such as Reliability, Availability, Maintainability, Safety
and Security, is an important attribute in minimizing the risk
to people, society and environment. Engineering data ana-
lytics along with an understanding of Physics of Failures is
becoming an important tool in prognostics. These techniques
are playing a major role in planning, predicting and maintain-
ing critical equipment for safe and economic operation of
these industries.
Artificial intelligence in recent years had a great advancement
owing to emergence of edge computing devices, parallel
processing techniques and machine learning (ML) methods.
One of the key factors responsible for the improvement in
neural network’s accuracy and the improvement of the com-
plexity of tasks they can solve is the dramatic increase in the
size of the networks that can be used with the advent of low
cost/low power computing devices for data collection and
analysis.
In this context, a more rigorous dependability analysis with
better accuracy is now possible involving large data analytics,
physics of failures and AI/ML techniques to build prediction
models that help in early failure prediction of equipment.
This paradigm shift in dependability analysis aids in planning
a safe envelop of plan operation with good economics.
Keeping in view the current scenario and future require-
ments the theme meeting topics have been designed to dis-
cuss the state-of-the-art methods, models and techniques to
predict dependability of engineering systems. The topics
would help the younger engineers as well as practitioners to
take up these challenging areas for better design & analysis of
future generation NPPs.
The theme meeting comprises of invited talks from re-nowned experts in the field, and tutorials on practical aspects on the subject matter.
Who should participate ?
The workshop is mainly targeted to engi-neers engaged in design, development, life testing, maintenance and dependability analysis of engineering systems.
Registration details
No Registration fee for DAE participants Participation on nomination basis Total number of participants are fixed
Nominations:
For BARC, nominations from respective Groups shall be forwarded by e-mail to Convener for consolidation.
For other than BARC, consolidated nomi-nations from unit shall be forwarded through proper channel to Convener by e-mail.
Last date for receipt of nomination:
29.11.2019
Scope:
Recent Advances in Modeling Techniques in Dependability Predictions
Computational Intelligent Methods
AI/ML applications in dependability pre-diction
Applications in Prognostics and Health Management of Critical Engineering Assets
Human reliability prediction - AI based cognitive neuroscience
Case studies involving systematic data col-lection, learning algorithm design and building prediction models
Accommodation
Limited accommodation will be available in NBH and TSH, Anushaktinagar. Anushaktinagar is well connected to main parts of the Mumbai and Navi Mumbai. Chembur is 3 Km away and has several types of hotels. Reservation in the Anushaktinagar guesthouses will be on first come first serve basis.
REGISTRATION FORM
BRNS Theme meeting on
Advances in Dependability Predic-
tion of Engineering Systems using
AI/Machine Learning
December 13-14, 2019
Name (s): _____________________________
____________________________________
____________________________________
Organization: __________________________
____________________________________
Address: _____________________________
____________________________________
Phone: _____________ _________________
Email: _______________________________
Mobile: _____________ _________________
Date: _______________ Signature: _________
For Further Details Please Contact:
Dr. Manoj Kumar, Convener
Control & Instrumentation Division BARC, Trombay, Mumbai, 400 085
Phone: 022-2559 6190 Fax: 022-2559 1803
E-mail: [email protected]
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BRNS Theme meeting on “Advances in Dependability Prediction of Engineering Systems using AI/Machine Learning”
13-14 Dec., 2019, Multipurpose Hall, TSH, Anushaktinagar
13th December 2019
08:30 – 09:30 Registration
09:30 – 10:30 Inauguration Welcome Address: Shri P. P. Marathe, AD, E&IG, BARC Address by Chief Guest: Dr. R. Chidambaram, DAE Homi Bhabha Chair Professor Inaugural Address : Prof. A. K. Verma, Western Norway University of Applied Sciences, Haugesund, Norway Release of Souvenir: Shri P. R. Patil, Director, RDDG, BARC
10:30- 11:00 High Tea
11:00-12:30 Lecture 1: Some laboratory applications of machine and deep learning for dynamic modeling and fault classification Prof. P. S. V. Nataraj, IIT Bombay
12:30-13:00 Lecture 2: Fundamentals of Artificial Intelligence and Machine Learning Dr. D. Datta, Ex-BARC
13:00-14:00 Lunch
14:00-14:30 Lecture 3: Dr. Kallol Roy, CMD, BHAVINI
14:30-15:30 Lecture 4: Deep Learning paradigm for Asset Management Prof. N. S. Vyas, Chairman, Technology Mission for Indian Railways (TMIR)
15:30-16:00 Lecture 5: Role of Artificial Intelligence in Risk-conscious Operations Management in Nuclear Plants Dr. P. V. Varde, Ex-BARC
16:00-16:15 Tea break
16:15- 17:00 Panel Discussion : All experts (Moderator Prof. A. K. Bhattacharjee, BARC)
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14th December 2019
10:00 – 10:30 Lecture 6: Design of a Collaborative Filtering Based Recommender System using Machine Learning Dr. D. Datta, Ex-BARC
10:30 – 11:30 Lecture 7: Transformative maintenance technologies and solutions: Issues and Challenges Prof. Uday Kumar, Luleå University of Technology, SWEDEN
11:30-11:45 Tea break
11:45-13:00 Lecture 8: Computational Risk Assessment Dr. Curtis Smith, Idaho National Laboratory, USA
13:00-14:00 Lunch
14:00-15:00 Lecture 9: Human Factors in Risk Assessment and Prevention Prof. Manas K. Mandal, IIT Kharagpur
15:00-15:30 Lecture 10: Mr. H. S. Kushwaha, Ex-BARC
15:30-15:45 Tea break
15:45- 16:45 Panel Discussion : All experts (Moderator Prof. A. K. Bhattacharjee, BARC)
