RESEARCH INTERESTS

Reliability and Risk Analysis - My primary research involves the prediction and optimization of system reliability, including optimization of system reliability when there is incomplete or uncertain information on the components within the system. I am particularly interested in new and novel applications like systems comprised on micro and nano-structures. I am also active in reliability optimization problems with multi-state systems and networks. An article in the magazine Industrial Engineer describing my reliability optimization research is available here article

recent papers:

"Simultaneous Quality and Reliability Optimization for Micro-engines Subject to Degradation," Hao Peng, Qianmei Feng & David Coit, IEEE Transactions on Reliability, vol. 58, no. 1, March 2009.

"Unbiased Variance Estimates for System Reliability Estimate Using Block Decompositions," Tongdan Jin & David Coit, IEEE Transactions on Reliability, vol. 57, no. 3, September 2008.

"Multi-state Component Criticality Analysis for Reliability Improvement in Multi-state Systems," Jose Ramirez-Marquez & David Coit, Reliability Engineering & System Safety, Vol. 92, No. 12, December 2007.

Modeling & Optimization of Electricity Generation/Transmission/Distribution Systems - We are developing optimization models to determine recommended electricity generation expansion plans considering the multiple objectives of cost, greenhouse gas emissions (CO₂) and pollution emissions (NO_x, SO₂), and the availability of renewable energy sources. As part of the model, we are considering the impact of smart-grid technologies. We have also developed models to estimate network reliability & availability and to identify critical components in the power grid that most significantly impact the overall system. Additionally, we are working on economic decision-making models based on planned retirements of aging equipment or components.

recent papers:

"Multi-period Multi-objective Electricity Generation Expansion Planning Problem with Monte Carlo Simulation," Hatice Tekiner, David Coit, Frank Felder, Rutgers University ISE Working Paper 08-025 (under review Electric Power Systems Research).

"Component Replacement Models for Electricity Distribution Systems," Jose Espiritu & David Coit, The Engineering Economist, vol. 53, no. 4, October 2008.

"Component Criticality Importance Measures for the Power Industry," Jose Espiritu, David Coit & Upyukt Prakash, Electric Power Systems Research, vol. 77, no. 5-6, April 2007.

Multiple Objective Optimization - For many engineering design and production planning problems, there are multiple problem objectives, and often, these objectives are conflicting and can not be measured or observed without uncertainty. This is further complicated by the consideration of risk-aversion, i.e., a decision-maker may prefer a solution with a slightly less desirable estimated objective function if it is known that it is a more "certain" estimate. We are developing models to efficiently identify a subset of promising solutions, we call the "pruned" Pareto set.

recent papers:

"Multiple Objective Scheduling Problems: Determination of Pruned Pareto Sets," Heidi Taboada & David Coit, IIE Transactions, vol. 40, no. 5, May 2008.

"Pruned Pareto-Optimal Sets for the System Redundancy Allocation Problem Based on Multiple Prioritized Objectives," Sadan Kulturel-Konak, David Coit & Fatema Baheranwala, Journal of Heuristics, vol. 14, no. 4, August 2008.

"Data Clustering of Solutions for Multiple Objective System Reliability Optimization Problems," Heidi Taboada & David Coit, Quality Technology & Quantitative Management, vol. 4, no. 2, June 2007.

Replacement Analysis of Aging Assets - A complex system is often composed of aging assets and requires an optimal plan or strategy to determine replacement and restoration (or overhaul) times to minimize total cost of ownership, or to determine an optimal plan to increase the capacity of the system or efficiently change to a different technology. This is further complicated by secondary objectives, such as the minimization of energy consumption or waste. We have developing applied optimization models to provide solutions to this difficult problem.

Applied Operations Research Models - I am interested in other types of optimization problems including scheduling and production planning, and maintenance planning, and the development of evolutionary optimization models.

FUNDED RESEARCH PROJECTS

Development of Reliability Models to Support the Recovery Gear Service Life Analysis Program (Principal Investigator: David Coit) - U.S. Naval Air Engineering Station (NAVAIR/NAES) grant ($261,000, 7/09 to 9/10) - This grant involves the development of component-level and system-level reliability models for aircraft carrier recovery gear systems, to be able to anticipate and compensate for changing future load distributions. Proportional hazard rate and proportional life models are being developed for the critical components to predict reliability as a function of the important design and stress covariates. The component-level models are aggregated into a discrete event simulation model to predict system reliability behavior and to optimize maintenance planning.

Future Electric Power System in a Carbon Constrained World (Principal Investigators: David Coit, Frank Felder) - grant funded by the Rutgers Energy Institute (REI) and Center for Energy, Economic & Environmental Policy (CEEEP) ($60,000, 9/07 to 8/09) - This grant involves investigating the future of electricity transmission & distribution systems, including distributed resources, that are needed in a carbon constrained world using multi-objective optimization under uncertainty to model explicitly the trade-offs necessary to produce an energy efficient future. This design and modeling work will lead to a systematic investigation of public policies that would be necessary to effectuate the necessary transformation of the electric power system.

Stochastic Optimization of System Reliability with Risk-Averse Decision Makers (Principal Investigator: David Coit) - NSF CAREER grant ($310,000, 9/99 to 8/04) - This grant involves the development of optimization algorithms with the goal of maximizing system reliability when critical components within the system have uncertain reliability. Traditional approaches require explicit knowledge of reliability for all available components, thereby ignoring the variability and the risk implicit in using uncertain estimates. This research is explicitly considering uncertainty and variability with component and system reliability estimation and the propagation of uncertainty.

Probabilistic Decision Support for Evaluating Technology Insertion and Assessing Aviation Safety System Risk (Principal Investigator: James Luxhoj) - NASA grant ($1,164,000, 3/03 to 3/06) - I am collaborating with Jim Luxhoj (PI) in this grant involving the modeling of aviation safety and risk, and the assessment of risk mitigation technologies and insertions. An Aviation System Risk Model (ASRM) is being developed and enhanced based on Bayesian belief networks. ASRM is a risk-based decision support system designed to evaluate the safety risk by studying observed accidents and using analytical generalization. Bayesian probability theory is being used for model quantification.

Impact of Parameter Uncertainty on Asset Criticality and System Reliability (Principal Investigator: David Coit) - Industry grant ($30,000, 1/04 to 12/04) - This project is to model the uncertainty related to reliability evaluation of electricity transmission systems.  Reliability and uncertainty metrics are being developed for outage rates, repair times and downtimes for various electricity transmission configurations.  The metrics relate component reliability estimation uncertainty to the effect at system level.  They can be used to prioritize data collection and reliability improvement activities.

Relating Field Data to Accelerated Life Testing (Principal Investigators: David Coit, E. A. Elsayed) - funded by NSF ($100,000, 8/00 to 7/02) - This grant was to investigate the relationship between the actual wear, degradation and failure of vehicle controllers experienced in the field with that expected from accelerated testing conducted in the laboratory. DaimlerChyrsler Electronics, Huntsville, AL, is the controller designer and manufacturer. This is a TIE grant conducted with a corresponding grant awarded to Auburn University's Center for Advanced Vehicle Electronics (CAVE).

Reliability Modeling and Integrated Prognostics for Highly Reliable Systems (Principal Investigator: David Coit) - funded by the NSF/Industry QRE Center ($30,000, 10/00 to 12/01) - In this study, we reviewed and compiled new and novel approaches to the improvement of reliability of fielded systems. For many modern systems, traditional reliability engineering analyses, metrics and design practices are not leading to the very high reliability requirements often needed or expected. Although, many approaches were evaluated, particular emphasis was focused on different approaches to implement integrated prognostics to anticipate failures.

Reliability Prediction Based on Degradation with Multiple Changing Stresses (Principal Investigator: David Coit) - funded by the NSF/Industry QRE Center ($30,000, 10/99 to 12/00) - Degradation modeling represents an opportunity to observe and analyze performance deterioration and to predict reliability prior to the occurrences of failure. Parametric drift or deterioration is modeled as a dynamic (time-variant) probability distribution. Reliability predictions are made by considering shifts in the distribution compared to a failure threshold. In this project, currently available methods were extended to consider changing stress levels and uncertain stress conditions.

Repairable Systems Reliability: Planning and Assessment Tools (Principal Investigator: David Coit) - funded by the NSF/Industry QRE Center ($30,000, 10/98 to 12/99) - This project involved the development and automation of planning and assessment tools for repairable system reliability with non-constant rate of occurrence of failure. The distribution of failure times is often non-stationary for many products during (1) development testing where there is reliability growth, and (2) extended field usage where there is deterioration. The problem is further compounded because a system is designed with many subsystems and components, each with different failure patterns.

People Sensitive Processes (Principal Investigator: David Coit) - funded by U. S. Army ARDEC ($22,700, 8/98 to 12/99) - People related processes are an integral part of most manufacturing processes, whether they are highly automated or almost entirely manual in nature. Effective processes with significant manual processing must depend on consistent, sustained levels of human performance if they are to consistently produce units free from error. Those factors which either directly or indirectly influence human performance or impact the occurrences of human errors were compiled and mapped to activities at an Army contractor manufacturing facility.

System Reliability Models with Uncertain Component Reliability Estimates (Principal Investigator: David Coit) - funded by the NSF/Industry QRE Center ($30,000, 10/97 to 10/98) - Sufficient data are often not available for all components in a system for highly accurate system-level reliability predictions. System reliability models and sensitivity measures have been developed based on component reliability and the variability associated with the component reliability estimates or predictions. The models explicitly acknowledge the uncertainty with component reliability estimates or predictions. Sensitivity measures quantify the relationship between a lower-bound on system reliability and the component reliability estimation variance.