Preliminary Agenda

Preliminary Agenda - Overview

Note: Detailed agenda with information on session topics and presentation schedule will be available in the agenda soon.

Agenda


Monday, October 2, 2017 — Opening Remarks, Tutorials, Panels, Technical Paper Sessions, Welcome Reception
Tuesday, October 3, 2017 — Luminary, Keynote, Panels, Technical Paper Sessions, Technology Demos, Poster Session
Wednesday, October 4, 2017 — Luminary, Panel, Technical Paper Sessions, Technology Demos, Banquet Dinner
Thursday, October 5, 2017 — Joint PHM/DX Keynote, Technical Paper Sessions, Panel Sessions, Technology Demos, Closing Remarks

Monday, October 2, 2017 Location
7:00–17:00 Registration
7:00–17:00 Deep Learning Workshop St. Petersburg 1
7:00–17:00 Doctoral Symposium Bayboro
Tuesday, October 3, 2017 Location
7:00–17:00 Registration
7:30–8:00 Continental Breakfast Grand Bay Ballroom and Foyer
8:00–8:45 Opening Remarks Grand Bay Ballroom
8:00–8:45 Luminary Presentation: Elaine Spiller, Marquette University Grand Bay Ballroom
8:45–10:15 Session 1A: Batteries I
Session Chair: Kai Goebel–NASA Ames Research Center
St. Petersburg 2
  Energy Management of Electric Bicycles Given a Traveling Elevation Profile
Sebastián Seria1, Vanessa Quintero2, Pablo A. Espinoza3, Aramis Pérez4, Francisco Jaramillo5, Matías Benavides6, Marcos Orchard7
1,2,3,4,5,6,7 University of Chile, Chile
 
  Use of Teaching-Learning Based Optimization for Filter Parameter Tuning in the Prognostics of a Quadrotor Battery
Leonardo Ramos Rodrigues1, João Paulo Pordeus Gomes2, Vandilberto Pereira Pinto3, Roberto Kawakami Harrop Galvão4
1 Institute of Aeronautics and Space, Brazil
2,3 Federal University of Ceará, Brazil
4 Instituto Tecnológico de Aeronáutica, Brazil
  GPU Accelerated Prognostics
George E. Gorospe1, Matthew J. Daigle2, Shankar Sankararaman3, Chetan S. Kulkarni4, Eley Ng5
1,3,4 SGT Inc., NASA Ames Research Center
2 NIO USA, Inc.
5 Universities Space Research Association
  Lithium-ion Battery Remaining Useful Life Prediction with Long Short-term Memory Recurrent Neural Network
Yuefeng Liu1, Guangquan Zhao2, Xiyuan Peng3, Cong Hu4
1,2,3 Harbin Institute of Technology, China
1 Inner Mongolia University of Science & Technology, Inner Mongolia
4 Guilin University of Electronic Technology, China
8:45–10:15 Session 1B: Industrial & Manufacturing Applications
Session Chair: –
St. Petersburg 3
  Fleet Knowledge for Prognostics and Health Management – Identifying Fleet Dimensions and Characteristics for the Categorization of Fleets
Carolin Wagner1, Bernd Hellingrath2
1,2 Westfälische Wilhelms-Universität Münster, Germany
 
  Dynamic Modeling of Maintenance Prices in the Aerospace Industry
George S. Ekladious1, Xiaojun Zhao2, Hala Mostafa3, Ramona Georgescu4
1,2,3,4 United Technologies Research Center
  Case Studies in using Consumer Analytics with PHM Strategy
Sameer Vittal1, Mark Sporer2
1 GE Power
2 GE Renewables
  A New Application for Failure Prognostics – Reduction of Automotive Electronics Reliability Test Duration
Andre Kleyner1, Arvind Vasan2, Michael Pecht3
1 Delphi Electronics & Safety
2 Empower Micro Systems Inc.
3 CALCE Research Center, University of Maryland
8:45–10:15 Panel Session 1: Smart Manufacturing PHM St. Petersburg 1
10:15–10:30 Break St. Petersburg Foyer
10:30–12:00 Session 2A: Batteries II
Session Chair: Chetan Kulkarni–SGT Inc., NASA Ames Research Center
St. Petersburg 2
  Flight Tests of a Remaining Flying Time Prediction System for Small Electric Aircraft in the Presence of Faults
Edward F. Hogge1, Chetan S. Kulkarni2, Sixto L. Vazquez3, Kyle M. Smalling4, Thomas H. Strom5, Boyd L. Hill6, Cuong C. Quach7
1,4,5 Northrop Grumman Technology Services
2 SGT, Inc., NASA Ames Research Center
1,3,4,5,6,7 NASA Langley Research Center
6 Analytical Mechanics Associates, Inc.
 
  A Simulation Engine for Predicting State-of-Charge and State-of-Health in Lithium-Ion Battery Packs of Electric Vehicles
Pablo A. Espinoza1, Aramis Pérez2, Marcos E. Orchard3, Hugo F. Navarrete4, Daniel A. Pola5
1,2,3,4,5 University of Chile, Chile
  An Improved Model for Remaining Useful Life Prediction on Capacity Degradation and Regeneration of Lithium-ion Battery
Li-Ming Deng1, Yu-Cheng Hsu2, Han-Xiong Li3
1,2,3 City University of Hong Kong, China
10:30–12:00 Session 2B: Deep Learning
Session Chair: Neil Eklund–Analatom
St. Petersburg 3
  Deep Feature Learning Network for Fault Detection and Isolation
Gabriel Michau1, Thomas Palmé2, Olga Fink3
1,3 Zurich University of Applied Sciences, Switzerland
2 General Electric (GE), Switzerland
 
  Unsupervised Deep Learning for Gear Health Monitoring
Tyler Cody1, Stephen Adams2, Peter A. Beling3
1,2,3 University of Virginia
  Bearing Health Condition Prediction Using Deep Belief Network
Guangquan Zhao1, Xiaoyong Liu2, Bin Zhang3, Guohui Zhang4, Guangxing Niu5, Cong Hu6
1,2,4 Harbin Institute of Technology, China
3,5 University of South Carolina
6 Guilin University of Electronic Technology, China
10:30–12:00 Panel Session 2: Human Machine Interfaces for Smart PHM St. Petersburg 1
12:00–13:15 Conference Lunch and Keynote Speech - Ravi Rajmani, drR2 Consulting Grand Bay Ballroom
1:15–3:00 Session 3A: Wind Turbines
Session Chair: –
St. Petersburg 2
  Adaptive Training of Vibration-based Anomaly Detector for Wind Turbine Condition Monitoring
Takanori Hasegawa1, Jun Ogata2, Masahiro Murakawa3, Tetsunori Kobayashi4, Tetsuji Ogawa5
1,4,5 Waseda University, Japan
1,2,3,5 National Institute of Advanced Industrial Science and Technology, Japan
 
  Wind Turbine Intelligent Gear Fault Identification
Sofia Koukoura1, James Carroll2, Alasdair McDonald3
1,2,3 University of Strathclyde, UK
  Small-Scale Wind Turbine Recurrence and Cost Modeling as a Function of Operational Covariates from Supervisory Control and Data Acquisition Systems
Michael S. Czahor1, William Q. Meeker2
1,2 Iowa State University
13:15–15:00 Tutorial Session 1: Model-Based Prognostics - An Introduction, Indranil Roychoudhury, SGT Inc., NASA Ames Research Center St. Petersburg 3
13:15–15:00 Panel Session 3: Automotive PHM and Emerging Standards, Steven W. Holland, General Motors St. Petersburg 1
13:15–15:00 Technology Demonstration: Automotive Application of PHM Concepts via Cadillac SRX Rig [General Motors] Williams and Demens
15:00–15:30 Break St. Petersburg Foyer
3:30–5:00 Session 4A: Diagnostics
Session Chair: Michael Sharp–NIST
St. Petersburg 2
  Diagnostics of machine tool linear axes via separation of geometric error sources
Gregory W. Vogl1, Michael E. Sharp2
1,2 National Institute of Standards and Technology
 
  Towards Diagnosing Cascading Outages in Cyber Physical Energy Systems using Temporal Causal Models
Ajay Chhokra1, Nagabhushan Mahadevan2, Abhishek Dubey3, Daniel Balasubramanian4, Gabor Karsai5
1,2,3,4,5 Vanderbilt University
  Internet of Turbines: An Outlook on Smart Diagnostics
Gulnar Mehdi1, Mikhail Roshchin2, Thomas Runkler3
1,2,3 Siemens AG, Germany
1,3 Technical University Munich, Germany
  Diagnosis and Prognosis of Fuel Injectors based on Control Adaptation
Azeem Sarwar1, Chaitanya Sankavaram2, Xiangxing Lu3
1,2,3 General Motors Company
15:30–17:00 Tutorial Session 2: Design, Development, and Testing of PHM Software, Chris Teubert, NASA Ames Research Center St. Petersburg 3
15:30–17:00 Panel Session 4: PHM for the Electric Power Grid, Avi Gopstein, NIST St. Petersburg 1
15:30–17:00 Technology Demonstration: Machine Health Monitoring via Internet of Things Platform [Mathworks] Williams and Demens
18:00–19:30 Cocktail Reception with Posters Grand Bay Ballroom
Wednesday, October 4, 2017 Location
7:00–17:00 Registration
7:30–8:00 Continental Breakfast Grand Bay Ballroom and Foyer
8:00–8:45 Opening Remarks Grand Bay Ballroom
8:00–8:45 Keynote Speaker, Steve Holland, Issues and Opportunities in Automotive PHM Grand Bay Ballroom
8:45–10:15 Session 5A: Aviation I
Session Chair: Karl Reichard–Pennsylvania State University
St. Petersburg 2
  Fast Optimization for aircraft Descent and Approach Trajectory
Dmitry G. Luchinskiy1, Stefan Schuet2, J. Brenton3, Dogan Timucin4, David Smith5, John Kaneshige6
1 SGT, Inc.
2,3,4,5,6 NASA Ames Research Center
 
  Health-Informed Uncertainty Quantifications via Bayesian Filters with Markov Chain Monte Carlo Simulations for Fatigue Critical Rotorcraft Components
Michael Shiao1, Anindya Ghoshal2
1,2 Army Research Laboratory
  Reducing the Impact of Test Bench Component on the Thrust Margin Measurement
Mohammed Meqqadmi1, Pierre-Etienne Mosser2, Thierry Brichler3, Jérôme Lacaille4
1,2,3,4 Safran Aircraft Engines, France
8:45–10:15 Session 5B: Data Driven Methods I
Session Chair: Jamie Coble–University of Tennessee, Knoxville
St. Petersburg 3
  A Method for Measuring the Robustness of Diagnostic Models for Predicting the Break Size during LOCA
Xiange Tian1, Victor Becerra2, Nils Bausch3, Gopika Vinod4, T.V. Santhosh5
1,2,3 University of Portsmouth, UK
4,5 Bhabha Atomic Research Centre, India
 
  Condition Monitoring of a Reciprocating Compressor Using Wavelet Transformation and Support Vector Machines
Shawn Falzone1, Jason R. Kolodziej2
1,2 Rochester Institute of Technology
  Data Driven Modeling and Estimation of Accumulated Damage in Mining Vehicles using On-board Sensors
Erik Jakobsson1, Erik Frisk2, Robert Pettersson3, Mattias Krysander4
1,3 Atlas Copco Rock Drills AB, Sweden
1,2,4 Linköping University, Sweden
  Fault Detection By Segment Evaluation Based On Inferential Statistics For Asset Monitoring
Vepa Atamuradov1, Kamal Medjaher2, Benjamin Lamoureux3, Pierre Dersin4, Noureddine Zerhouni5
1,2 INP-ENIT, France
3,4 ALSTOM Transport, France
5 FEMTO-ST Institute, France
8:45–10:15 Smart Manufacturing Standards Worshop Bayboro
8:45–10:15 Invited Session: Data Challenge Winners St. Petersburg 1
10:15–10:30 Break St. Petersburg Foyer
10:30–12:00 Session 6A: Aviation II
Session Chair: –
St. Petersburg 2
  A Case for the Use of Data-driven Methods in Gas Turbine Prognostics
Marcia Baptista1, Cairo L. Nascimento2, Helmut Prendinger3, Elsa Henriques4
1,4 Universidade de Lisboa, Portugal
2 Instituto Tecnologico de Aeronautica, Brazil
3 National Institute of Informatics, Japan
 
  Effect of Ambient Temperature on Performance of Gas Turbine Engine
Yuan Liu1, Avisekh Banerjee2, Amar Kumar3, Alka Srivastava4, Nita Goel5
1,2 Life Prediction Technology Inc., Canada
3,4,5 Tecsis Corporation, Canada
  Prospective Architectures for Onboard vs Cloud-based Decision Making for Unmanned Aerial Systems
Shankar Sankararaman1, Christopher Teubert2
1 SGT, Inc.
1,2 NASA Ames Research Center
10:30–12:00 Session 6B: Data Driven Methods II
Session Chair: –
St. Petersburg 3
  A Compressed Sensing Feature Extraction Approach for Diagnostics and Prognostics in Electromagnetic Solenoids
Christian Knoebel1, Hanna Wenzl2, Johannes Reuter3, Clemens Guehmann4
1,2,3 University of Applied Sciences Konstanz, Germany
4 Technische Universität Berlin, Germany
 
  Fault Detection and Prognosis of Time Series Data with Random Projection Filter Bank
Sepideh Pourazarm1, Amir-massoud Farahmand2, Daniel Nikovski3
1 Mitsubishi Electric Research Laboratories
  A Comparison of Acoustic Emission and Vibration Measurements for Condition Monitoring of an Offshore Drilling Machine
Martin Hemmer1, Tor I. Waag2
1 University of Agder, Norway
2 Teknova AS, Norway
10:30–12:00 Smart Manufacturing Standards Worshop Bayboro
10:30–12:00 Panel Session 5: PHM Applications Deployment St. Petersburg 1
12:00–13:15 Lunch on Your Own
1:15–3:00 Session 7A: Systems I
Session Chair: George Gorospe–SGT Inc., NASA Ames Research Center
St. Petersburg 2
  Inferential Framework for Autonomous Cryogenic Loading Operations
Dmitry G. Luchinskiy1, Michael Khasin2, Dogan Timucin3, Jarred Sass4, Jose Perotti5, Barbara Brown6
1,2 SGT, Inc.
3 NASA Ames Research Center
4,5,6 Kennedy Space Center
 
  Integration of Prognostics at a System Level: a Petri Net Approach
Manuel Chiachío1, Juan Chiachío2, Shankar Sankararaman3, John Andrews4
1,2,4 University of Nottingham, UK
3 NASA Ames Research Center
  Why Autonomous Assets are Good for Reliability – the Impact of ‘Operator-related Component’ Failures on Heavy Mobile Equipment Reliability
Melinda R. Hodkiewicz1, Zac Batsioudis2, Tyler Radomiljac3, Mark T.W. Ho4
1,2,3,4 University of Western Australia, Australia
1:15–3:00 Session 7B: Structural Health Management
Session Chair: Juan Chiachio–University of Nottingham
St. Petersburg 3
  Low Computation Acoustic Emissions Structural Health Monitoring Through Analog Signal Pre-Processing
Rune Schlanbusch1, Eric Bechhoefer2, Thomas J. J. Meyer3
1,3 Teknova, Norway
2 GPMS Inc.
 
  Low-complexity Behavioral Model for Predictive Maintenance of Railway Turnouts
Pegah Barkhordari1, Roberto Galeazzi2, Alejandro de Miguel Tejada3, Ilmar F. Santos4
1,2,3,4 Technical Univerisity of Denmark, Denmark
  Impact Damage Prediction for Wave Energy Converters
Ryan Meekins1, Stephen Adams2, Kevin Farinholt3, Nathan Hipwell4, Michael Desrosiers5, Peter Beling6
1,2,6 University of Virginia
3,4,5 Luna Innovations, Inc.
  Preliminary Results on Condition Monitoring of Fiber Ropes using Automatic Width and Discrete Length Measurements
Shaun Falconer1, Andreas Gromsrud2, Espen Oland3, Geir Grasmo4
1,2,4 University of Agder, Norway
3 Teknova AS, Norway
13:15–15:00 Panel Session 6: PHM in Railway Maintenance, Parham Shahidi, PARC St. Petersburg 1
13:15–15:00 Technology Demonstration: Automotive Application of PHM Concepts via Cadillac SRX Rig [General Motors] Williams and Demens
15:00–15:30 Break St. Petersburg Foyer
3:30–5:00 Session 8A: Systems II
Session Chair: –
St. Petersburg 2
  Gear Fault Diagnostics Using Extended Phase Space Topology
T. Haj Mohamad1, C. Nataraj2
1,2 Villanova Center for Analytics of Dynamical Systems
 
  Actuator Fault-Detection for Autonomous Underwater Vehicles Using Unsupervised Learning
Matt Kemp1, Ben Raanan2
1,2 Monterey Bay Aquarium Research Institute
  Unobtrusive Software and System Health Management with R2U2 on a parallel MIMD Coprocessor
Johann Schumann1, Patrick Moosbrugger2
1 SGT, Inc., NASA Ames Research Center
2 Vienna University of Technology, Austria
3:30–5:00 Session 8B: Anomaly Detection
Session Chair: Dmitry Luchinsky–SGT Inc., NASA Ames Research Center
St. Petersburg 3
  Anomaly Detection Using Dynamical Linear Models and Sequential Testing on a Marine Engine System
Erik Vanem1, Geir Olve Storvik2
1 DNV GL, Norway
1,2 University of Oslo, Norway
 
  Leak Detection in Compressed Air Systems using Unsupervised Anomaly Detection Techniques
Antoine Desmet1, Matthew Delore2
1 Komatsu Mining Corporation, Australia
2 University of Newcastle, Australia
  Early Warnings for failing Train Axle Bearings based on Temperature
M.F.E. Peters1
1 Netherlands Railways (NS), Nederland
15:30–17:00 Panel Session 7: PHM Education and Standards, Jeff Bird (TECnos) and Ravi Rajmani (drR2 Consulting) St. Petersburg 1
15:30–17:00 Technology Demonstration: Model-based Predictive Maintenance Solutions for Specific Turbine Engines [Life Prediction Technologies] Williams and Demens
18:00–22:00 PHM Conference Banquet Museum of Fine Arts
Thursday, October 5, 2017 Location
7:00–17:00 Registration
7:30–8:00 Continental Breakfast Grand Bay Ballroom and Foyer
8:00–8:45 Opening Remarks Grand Bay Ballroom
8:00–8:45 Luminary Presentation Gilbert Haddad, Sparkcognition, Digital Transformation Across Industries: Is PHM only for Industrial Assets? Grand Bay Ballroom
8:45–10:15 Session 9A: Standards & Methodologies
Session Chair: –
St. Petersburg 2
  Identification of Industrial Robot Arm Work Cell Use Cases and a Test Bed to Promote Monitoring, Diagnostic, and Prognostic Technologies
Brian A. Weiss1, Alexander Klinger2
1 National Institute of Standards and Technology
 
  Trends in Research Techniques of Prognostics for Gas Turbines and Diesel Engines
Joseph T. Bernardo1, Karl M. Reichard2
1,2 The Pennsylvania State University Applied Research Laboratory
  The Role of Transactional Data in Prognostics and Health Management Work Processes
Sarah Lukens1, Manjish Naik2, Xiaohui Hu3, Donald S. Doan4, Shaddy Abado5
1,2,3,4,5 GE Digital
  A Generic Software Architecture for Prognostics
Christopher Teubert1, Matthew Daigle2, Shankar Sankararaman3, Kai Goebel4, Jason Watkins5
1,2,3,4 NASA Ames Research Center
3 SGT, Inc.
5 University of California,
8:45–10:15 Tutorial Session 3: Electronics PHM, Patrick Kalgren, Singularity - Intelligence Amplified St. Petersburg 3
8:45–10:15 Panel Session 8: Corrosion Assessment and Remediation, Edward Manns (NACE) St. Petersburg 1
10:15–10:30 Break St. Petersburg Foyer
10:30–12:00 Session 10A: Bearings PHM
Session Chair: Manuel Chiachio–University of Nottingham
St. Petersburg 2
  Challenges And Opportunities in Applying Vibration Based Condition Monitoring in Railways
Diego A. Tobon-Mejia1, Pierre Dersin2, Gerard Tripot3
1,2,3 ALSTOM, France
 
  Steps Toward Prognostics of Faults in Bearings
Eyal Madar1, Gideon Kogan2, Dmitri Gazizulin3, Renata Klein4, Jacob Bortman5
1,2,3,5 Ben-Gurion University of the Negev, Israel
4 R.K. Diagnostics, Israel
  Feature Extraction for Bearing Prognostics using Correlation Coefficient Weight
Seokgoo Kim1, Chaeyoung Lim2, Joo-Ho Choi3
1,2,3 Korea Aerospace University, Korea
  Condition Based Maintenance of Low Speed Rolling Element Bearings using Hidden Markov Model
Guru Prakash1, Sriram Narasimhan2, Mahesh Pandey3
1,2,3 University of Waterloo, Canada
10:30–12:00 Tutorial Session 4: Deep Learning for PHM, Emilien Dupont, Schlumberger St. Petersburg 3
10:30–12:00 Panel Session 9: PHM for Human Assets, Wolfgang Fink, University of Arizona St. Petersburg 1
12:00–13:15 Lunch on Your Own
1:15–3:00 Session 11A: Railway PHM
Session Chair: Ian Jennions–Cranfield University
St. Petersburg 2
  Combination of Data-driven Feature Selection Methods with Domain Knowledge for Diagnosis of Railway Vehicles
Bernhard Girstmair1, Andreas Haigermoser2, Justinian Rosca3
1,2 SIEMENS AG, Austria
3 SIEMENS Corporate Technology
 
  A Reliability-based Prognostics Framework for Railway Track Management
Juan Chiachío1, Manuel Chiachío2, Darren Prescott3, John Andrews4
1,2,3,4 University of Nottingham, UK
  Some Influencing Factors for Passenger Train Punctuality in Sweden
Carl-William Palmqvist1, Nils O. E. Olsson2, Lena Winslott Hiselius3
1,2,3 Lund University, Sweden
2 Norwegian University of Science and Technology, Norway
  Anomaly Detection and Severity Prediction of Air Leakage in Train Braking Pipes
Wan-Jui Lee1
1 Dutch Railways, Delft University of Technology, the Netherlands
1:15–3:00 Session 11B: Prognostics I
Session Chair: Abbas Chokor–Seagate Technologies
St. Petersburg 3
  Remaining Useful Life prediction method using an observer and statistical inference estimation methods
Toufik Aggab1, Frédéric Kratz2, Pascal Vrignat3, Manuel Avila4
1,2 INSA CVL, France
3,4 Orleans University, France
 
  HPPN-based Prognosis for Hybrid Systems
Pauline Ribot1, Elodie Chanthery2, Quentin Gaudel3
1,2 LAAS-CNRS, Université de Toulouse, France
3 Easymile, France
  PFsuper: Simulation-Based Prognostics to Monitor and Predict Sparse Time Series
Javier Echauz1, Andrew Gardner2, Ryan R. Curtin3, Nikolaos Vasiloglou4, George Vachtsevanos5
1,2,3,4 Symantec Corporation
5 Georgia Institute of Technology
  A Condition Based Maintenance Implementation for an Automated People Mover Gearbox
Ali Ashasi-Sorkhabi1, Stanley Fong2, Guru Prakash3, Sriram Narasimhan4
1,2,3,4 University of Waterloo, Canada
13:15–15:00 Panel Session 10: Data Analytics in Commercial Aviation, Rhonda Walthall, UTC Aerospace Systems St. Petersburg 1
15:00–15:30 Break St. Petersburg Foyer
3:30–5:00 Session 12A: Electronics PHM
Session Chair: Jeff Bird–TECnos Consulting Services
St. Petersburg 2
  Application of a Relative Humidity Sensor for Monitoring Water Vapor Concentration inside Enclosures
Brian Hatchell1, Eric Gonzales2, Anton Sinkov3, Lorenzo Luzi4, Azem Cakerri5
1,2,3,4 Pacific Northwest National Laboratory
5 U.S. Army ARDEC
 
  Prognosis of Connector Disconnection Using a Canary-Based Approach
Xinyu Du1, Atul Nagose2, Aaron Bloom3, Timothy Julson4
1,2,3,4 General Motors
  Impact of Modulation Frequencies on the Lifetime of Power Semiconductor Modules for EV Applications
Quentin Gestes1, Nicolas Degrenne2
1 Ecole Normale Superieure de Rennes, France
2 Mitsubishi Electric R&D Centre Europe, France
  An Observer-based On-line Electrolytic Capacitor Health Monitoring System
Laurent Foube1
1 Mitsubishi Electric R&D Centre Europe, France
3:30–5:00 Session 12B: Prognostics II
Session Chair: Stephen Adams–University of Virginia
St. Petersburg 3
  Spatio-temporal Probabilistic Modeling Based on Gaussian Mixture Models and Neural Gas Theory for Prediction of Criminal Activity
Francisco Jaramillo1, Vanessa Quintero2, Aramis Pérez3, Marcos Orchard4
1,2,3,4 Universidad de Chile, Chile
 
  A Comparison of Feature Selection and Feature Extraction Techniques for Condition Monitoring of a Hydraulic Actuator
Stephen Adams1, Ryan Meekins2, Peter A. Beling3, Kevin Farinholt4, Nathan Brown5, Sherwood Polter6, Qing Dong7
1,2,3 University of Virginia
4,5 Luna Innovations Inc.
6,7 Naval Surface Warfare Center
  Improvement of a Hydrogenerator Prognostic Model by using Partial Discharge Measurement Analysis
Mélanie Lévesque1, Normand Amyot2, Claude Hudon3, Mario Bélec4, Olivier Blancke5
1,2,3,4 Institut de Recherche d’Hydro-Québec, Canada
5 École de Technologie Supérieure, Canada
  A Bi-Level Weibull Model with Applications to Two Ordered Events
Shuguang Song1, Hanlin Liu2, Mimi Zhang3, Min Xie4
1 The Boeing Company
2,4 City University of Hong Kong, Hong Kong SAR
3 University of Strathclyde, UK
15:30–17:00 Panel Session 11: Fielded Systems Lessions Learned, Andy Hess, The Hess PHM Group St. Petersburg 1
17:00–17:30 Closing Remarks St. Petersburg 1
Last modified: 20-Sep-2017 10:45:20
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