Marvin Zaluski, National Research Council Canada.

This case study presents new analytical and statistical tools/techniques developed for the CF-18 aircraft exploiting: operational flight data from sensors and Built-In Test Equipment (BITE) and maintenance activities recorded by personnel. This case study first investigates the utility of readily available data to develop data mining-based models for PHM systems. Then lessons learned in the development of data mining models/tools are discussed. Next some synergies are identified in the surface transportation industry and are linked to expected outcomes of the National Research Council of Canada new Fleet Forward 2020 program’s Vehicle Diagnostic and Prognostic activities.

b. Prognosis in Structural Health Monitoring – Case Studies on Metallic and Composite Structures:

Carl Byington, Sikorsky Aircraft Corporation and Dr. Abhinav Saxena, SGT Inc., NASA Ames Research Center.

Majority of structural health monitoring literature tends to focus on damage detection and diagnosis and not so much on prognostics. This session will illustrate some recent developments in predicting damage growth to assess Remaining Useful Life (RUL) of structures through several case studies. These case studies will briefly touch upon various different methods used for prognostics in structures. A more detailed discussion will focus on prognostics for composites. While modeling fault growth in metallic structures is well understood, it has been very challenging to develop damage growth models for composites due to their non-homogenous structure. Results from a case study will be presented where experimental data collection and analysis was augmented by Bayesian model selection for identifying the most suitable damage growth model and making predictions. Overall, this session will provide an appreciation of what is needed for making condition based life-predictions for structures beyond existing SHM technologies and what approaches may be taken to accomplish this.

c. Battery Health Management and Reliability Analysis:

Dr. Marcos Orchard University of Chile and Dr. George Vachtsevanos, The Georgia Institute of Technology.

This talk describes the implementation of particle-filtering-based prognostic frameworks for state-of-charge (SOC) and state-of-health (SOH) monitoring in energy storage devices (ESDs), and more specifically in Li-ion batteries. A probabilistic characterization of battery use profiles is used to incorporate the uncertainty associated with the future operation of the system, thus helping to generate a more realistic estimate of the Time-of-Failure probability density function. Analyzed data include voltage, current, and temperature measurements.

d. Corrosion Detection, Prediction and Mitigation Strategies:

Carl Byington, Sikorsky Aircraft Corporation and Dr. George Vachtsevanos, The Georgia Institute of Technology.

Corrosion of critical components, structures and a variety of complex systems/ processes has attracted the attention of the research community over the past years due to the severe consequences of corrosion damage. Corrosion detection, prediction and mitigation technologies are taking central stage in government and industrial enterprises.

In this case study, we introduce an integrated methodology to sensing, detection and prediction and exploit a typical aircraft surface to illustrate the efficacy of the approach. We highlight those innovative aspects of the application domain and point out areas of need for improvement.

e. PHM Implementation Strategies on Legacy Tactical Trucks:

Karl Reichard, The Pennsylvania State University Applied Research Laboratory

Case study will share lessons learned from the implementation of technologies to meet Army CBM+ requirements on tactical wheeled vehicles. The presentation will briefly describe the results of cost benefit analyses for the introduction of health monitoring technology on the vehicles, and discuss the infrastructure improvements required to implement prognostic health management on these vehicles.

f. Maintenance Optimization for Wind Turbines in Wind Farms:

Peter Sandborn and Xin Lie, CALCE, University of Maryland.

This case study describes the use of remaining useful life (RUL) predictions for wind turbine subsystems to assess and optimize the value of maintenance. Results for individual turbines and multiple turbines within a farm will be presented. The case study uses a simulation-based real options analysis to determine the value of waiting after obtaining a RUL indication from prognostics and health management (PHM) structures. This methodology accounts for the anticipated future availability of maintenance resources, uncertainties in future wind resources, and market uncertainties (e.g., anticipated future price and demand for energy).

g. Uncertainty Representation and Management in PHM:

Shankar Sankararaman, NASA Ames.

This case study focuses on understanding the significance, interpretation, and quantification of uncertainty in prognostics and health management, with an emphasis on remaining useful life prediction. In order to facilitate meaningful decision-making, it is important to analyze the various sources of uncertainty that affect prognostics and quantify their combined effect on the remaining useful life prediction through rigorous statistical methods. First, a conceptual example will be discussed to illustrate the importance of uncertainty quantification, and this will be followed by practical example where the goal is to predict the end-of-discharge of a lithium-ion battery. It will be demonstrated that the quantification of uncertainty in the remaining useful life prediction is non-trivial even in simple problems involving linear models and Gaussian variables. A few computational methods for uncertainty quantification in prognostics will be discussed in detail and the challenges related to uncertainty quantification in prognostics will be outlined. Finally, some aspects of uncertainty management will be discussed with numerical illustrations.