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PROFILE:

 

Linxia Liao

Linxia Liao’s research interests include predictive and prescriptive analytics; system and components fault diagnostics and prognostics; signal processing; and machine learning algorithms as well as their integration on embedded systems. Currently, he is developing device fleet health management solutions for intelligent transportation systems.

Prior to joining PARC, Linxia worked as a research scientist with Siemens Corporate, Corporate Technology (previous Siemens Corporate Research) located in Princeton, NJ. He conducted research and implemented various prognostics and health management-related applications in the fields of manufacturing, energy, and transportation. He also previously worked at Siemens Technology-To-Business (TTB) Center in Berkeley, CA to transfer the patented ‘Methods for prognosing mechanical systems’ technology from the university to industry applications.

Dr. Liao received his Ph.D. degree in Industrial Engineering from the University of Cincinnati, where he conducted research at the NSF I/UCR Center for Intelligent Maintenance Systems (IMS). He earned his M.S. and B.S. degrees in Mechanical Science and Engineering at Huazhong University of Science and Technology (HUST) in China. Linxia has one issued patent and seven pending patents, and he has published one book chapter and 20+ papers in leading journals and conferences.

Linxia enjoys cycling and hiking around the Bay Area.

 

PARC publications

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2014

 

 

other publications

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2014

Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction

Industrial Electronics IEEE Transactions on. 2014; 61 (5): 2464--2472.

2014

2013

A multi-model approach for anomaly detection and diagnosis using vibration signals

Prognostics and Health Management (PHM) 2013 IEEE Conference on; 1--7.

2013

Machinery time to failure prediction-Case study and lesson learned for a spindle bearing application

Prognostics and Health Management (PHM) 2013 IEEE Conference on; 1--11.

2013

2012

Machine Tool Feed Axis Health Monitoring Using Plug-And-Prognose Technology

2012 Conference of the Society for Machinery Failure Prevention Technology.

2012

Plug and Prognose – Condition Monitoring, Diagnosis and Life Time Prediction

ATP Edition. 2012; 54(10):52-56.

2012

2011

Machine Anomaly Detection and Diagnosis Incorporating Operational Data Applied to Feed Axis Health Monitoring

ASME 2011 International Manufacturing Science and Engineering Conference.

2011

Experimental Study on Control-Oriented Simulation Models for Building Control and Energy Management

12th Conference of International Building Performance Simulation Association.

2011

Prognostics enabled resilient control for model-based building automation systems

Proceedings of the 12th Conference of International Building Performance Simulation Association. 2011; : 286--293.

2011

2010

Design of a reconfigurable prognostics platform for machine tools

Expert systems with applications. 2010; 37 (1): 240--252.

2010

2009

Informatics Platform for Designing and Deploying e-Manufacturing Systems

Collaborative Design and Planning for Digital Manufacturing. 2009; : 1--35.

2009

2008

A Reconfigurable Watchdog Agent for Machine Health Prognostics

International Journal of COMADEM. 2008; 11 (3): 2.

2008

A reconfigurable embedded prognostics platform for machinery performance management

62nd machinery failure prevention technology. Virginia Beach Virginia US. 2008;

2008

2007

Bearing Health Assessment and Fault Diagnosis Using the Method of Self-Organizing Map

61st Meeting of the Society for Machinery Failure Prevention Technology.

2007

2006

Logistic Regression-based Machine Health Assessment Method on Application of Smart Machine Tool

International Manufacturing Leaders Forum 2006.

2006

 

 

 

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