San Diego, California
Oct 19 - Oct 24, 2015

Key Dates

- Abstract Due: Apr 10, 2015
- Doctoral Symposium Proposals: May 04, 2015
- Paper Submission Due: May 15, 2015
- Technology Demos proposals: June 12, 2015
- Panel Session proposals: June 12, 2015
- Paper Review Feedback: July 17, 2015
- Final Papers Due: August 07, 2015

Just Concluded


Fort Worth, Texas
Sep 29 - Oct 2, 2014

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Predictive Model Evaluation for PHM
Chunsheng Yang, Yanni Zou, Jie Liu, Kyle R Mulligan    Read
An Effective Predictive Maintenance Approach based on Historical Maintenance Data using a Probabilistic Risk Assessment—PHM14 Data Challenge
Seyed Mohammad Rezvanizaniani, Jacob Dempsey, Jay Lee    Read
Improved Probabilistic Modeling of Multi-Site Fatigue Cracking
Abdallah Al Tamimi, Mohammad Modarres    Read
Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets
Emmanuel Ramasso, Abhinav Saxena    Read

Data Scientist-Milwaukee WI

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This enables technicians to perform regular, quick, and easy software updates as well as to browse popular automotive websites to assist with the <a href="">tm100 key programmer</a> diagnosis.World class technical support, with built-in datalogger which records communication data and unidentified ECUs then wirelessly transmits the data back to the technicians.Software for the MaxiDAS is continually developed and updated in-house, with updates available 24hrs a day via the internet.

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Internship opportunity available at PARC in Monitoring, Prognostics, Diagnostics, and Action Recommendations

About the Job

PARC is working on an innovative project to monitor the health condition of devices, enabling accurate inferences about their state. This will be used to deliver recommendations for decision support. The analytics will focus on fusion of multiple sources of data from device; fusion of physics-based modeling and machine learning approaches, which allows insight generation and accurate “actionable” recommendations.


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One post-doctoral position is available at the Singapore University of Technology and Design (SUTD) for a highly qualified candidate in the field of prognostics and system health management (PHM). The candidate should have a relevant background in applied mathematics / statistics and be interested in applying different mathematical tools and techniques for real-time lifecycle prediction of engineering devices and systems in general.

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