Welcome to the Prognostics and Health Management Group

Dedicated to providing a research and knowledge base to support the advancement of diagnostics, prognostics, and system health management.

  • Prognostics is the process of monitoring the health of a product and predicting its remaining useful life (RUL) by assessing the extent of deviation or degradation from its expected state of health in its expected usage conditions.
  • Health Management utilizes prognostic information to make decisions related to safety, condition-based maintenance, ensuring adequate inventory, and product life extension.
  • Prognostics and Health Management (PHM) permits the evaluation of a system’s reliability in its actual life-cycle conditions.

The Prognostics and Health Management (PHM) Group has a multi-faceted approach to PHM focused on demonstrating that health monitoring can be implemented using a variety of methodologies, tools, and analyzing techniques for effective prognostics. Our approaches for PHM implementation include: (1) the use of expendable devices, such as canaries and fuses that fail earlier than the host product to provide advance warning of failure; (2) monitoring and reasoning of parameters that are precursors to impending failure, such as shifts in performance parameters; and (3) modeling of stress and damage in electronic parts and structures utilizing exposure conditions (e.g., usage, temperature, vibration, radiation) to compute accumulated damage.

The PHM Group conducts research and development of prognostics and health management applications for electronic products and systems, as well as systems-of-systems. The research focuses on computational algorithms, advanced sensors and data collection techniques, condition-based maintenance, PHM for the application of in-situ diagnostics and prognostics. The group is using physics-based models along with empirical models for prognostics. It is pioneering the use of a fusion approach, which combines physics of failure and data-driven methods for accurate prognostics and diagnostics. 

The goal of the group is to develop novel ways to identify anomalies and patterns within very large data sets containing multiple parameters both qualitative and quantitative and has developed real-time reduced-order modeling for failure prediction. Work in the areas of reliability modeling and prediction, pattern recognition, time series forecasting, machine learning, and fusion technologies is ongoing. The group is evaluating the use of intelligent reasoning technologies to model and manage the life cycle of electronic products. In addition, optimal maintenance planning and business case development to assess the return on investment associated with the application of PHM to systems is being researched by the group.

The PHM Group collaborates with industry and research partners to develop advanced sensors for diagnostics and prognostics applications. Applications such as tamper proof low-cost autonomous sensors that incorporate wireless communication, and high onboard memory capacity and can be attached to any product with minimal interference to the functioning of that product are being developed. The PHM Group enables real-time prognostics and health management of electronic products in their application environment.

PHM Application Areas

  

Recent Research Projects

Lockheed Martin: Remaining Useful Life Generation and Indicator Algorithms (2015-2016)
NSF: Prognostic Methods for Battery Management Systems (2012-2015)
US Navy: Condition Based Maintenance Plus (CBM+) Advanced Fault Diagnostics (2013-2014)
US Navy: Ground Tactical Value Prognostic Health Management for U.S. Marine Corps (2011)
US Army Research Laboratory: Autonomous Prognostics and Health Monitoring Systems for Weapons Platforms (2010)
US Army: Dynamic Data-Driven Prognostics and Condition Monitoring of On-board Electronics (2008 – 2010)
NASA: Reliable Diagnostics and Prognostics for Critical Avionics Systems (2008-2011)
Lockheed Martin: Prognostics of Systems-of-Systems (2007)
US Navy: Advanced Prognostic and Health Management (PHM) and Model Based Prognostic Useful Life Remaining Capabilities for Aircraft Tactical Information and Communication (2007)
Office of the Secretary of Defense: A Prototype Web-based Health (Prognostics) Assessment of a Test System (2006)
Dell: Baseline Characterization of Notebook Computers Subject to Changing Environmental and Operational Conditions for Diagnostics and Prognostics (2006)
NASA: Remaining Life Assessment of Space Shuttle Solid Rocket Booster Electronic Hardware (2004)

NASA: Remaining Life Assessment of Space Shuttle Remote Manipulator System (2001)


Anomaly Detection and Prognostics of Insulated Gate Bipolar Transistors (IGBTs) 

In most transportation and renewable energy applications, there are long times of dormancy of the parts with possibility of changes in aging pattern and parameter recovery. This project involves developing anomaly detection methods under intermittent power cycling taking into consideration possible healing and aging pattern variations.

 


Light Emitting Diode (LED) PHM

Parameterization of all optical characteristics of LEDs starts from Spectral Power Distribution (SPD), based on raw data from optical measurement of LEDs. All optical parameters of LEDs such as light output (lumen) and color coordinate values ((u', v'), or (x, y)) are derived from this SPD.

 

 
 
 
 
 
 
 

Optimized Maintenance of Railway Track Circuits

When a track circuit fault is detected during routine inspection, the maintenance staff, under time pressure or inability to correctly diagnose faults, replace the faulty equipment. CALCE is developing a prognostics and health management system that relates circuit abnormalities to the track circuit components that cause it. With improved fault detection and diagnosis, maintenance logistics and effectiveness are increased. 
 

 

 

 

 


Health Monitoring Based on Dynamic System Modeling: Wind Turbine Example


Dynamic System Modeling Approach


Gear Fault Modeling


 

•Motivations
–Most PHM datasets are not open to public.
–Providing datasets will give chances for current PHM researchers to validate their PHM methods.
–As a result, CALCE can be cited in many journals.
•Bearings datasets
–Experiments were conducted on computer fan bearings for prognostics.
–Unlike other datasets, failure mechanisms are labeled to datasets.
–The page will provide the experimental setups and datasets.

CALCE PHM Maintenance Planning Tool 

Latest version of the tool

CALCE has developed a stochastic decision model that determines when scheduled maintenance makes good business sense, i.e., makes possible a business objective such as a balance of cost and availability.  The model enables the optimal interpretation of life consumption monitoring damage accumulation or health monitoring precursor data, and applies to failure events that appear to be random or appear to be clearly caused by defects. 

The initial version of the tool focused on modeling and optimizing the cost avoidance associated with the application of PHM to systems. The tool implements a stochastic discrete event simulation applied to single and multi LRU systems where the LRUs can have no PHM structures, fixed interval maintenance, life consumption monitoring, or precursor to failure health monitoring.  The tool can be used to optimize safety margins and prognostic distances for single LRUs and to determine best maintenance strategies for multiple LRU systems. In Spring 2007, the tool was extended to address false positives.  The tool has been used to perform ROI studies on single LRU systems.  The metrics computed by the tool include:

  • Life cycle cost (of a socket or group of sockets)
  • Failures avoided
  • Operational availability

Recently the tool was  enhanced to model PHM implementation costs (non-recurring development costs, recurring costs at the LRU, socket and multi-socket levels, and infrastructure costs.  A revenue model is also being added to provide to differentiate between unscheduled maintenance that occurs before during and after missions. 
For more information contact sandborn@calce.umd.edu


 

Prof. Michael Pecht

pecht@calce.umd.edu

Director of CALCE                       

 

Dr. Michael Azarian

mazarian@calce.umd.edu

Materials Science, Photonic Devices, Tribology, and Reliability

Dr. Diganta Das

diganta@calce.umd.edu

Parts Selection & Management, Uprating, and Component Reliability

 

 

Prof. Abhijit Dasgupta

dasgupta@calce.umd.edu

Interconnect Reliability and Accelerated Testing

 

Prof. Peter Sandborn

sandborn@calce.umd.edu

Technology Tradeoff Analysis and Life Cycle Cost

Varun Khemani

vkheman@umd.edu

Ph.D. Student

Neda Shafiei

nshafiei@umd.edu

Ph.D. Student

Namkyoung Lee

nklee@terpmail.umd.edu

Ph.D. Student

 


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