Expert Microsystems (United States)

This project focused on developing advanced methods for thermal performance monitoring of a coal-fueled power plant. The specific goal was to develop and demonstrate a new thermal performance monitoring approach using a hybrid model that integrates a physics-based heat balance model with a machine learning-based pattern recognition model. The hybrid model enables increased accuracy and scope of the thermal analysis and an improved ability to monitor and detect changes in plant operation. This new approach takes full advantage of the individual model capabilities and creates an important new set of capabilities not previously possible using the two types of models separately. Using the heat balance model, a rich set of derived parameters (virtual sensors) are calculated from the measured plant operating data at each time point. The combined measured and derived data values are used by machine learning algorithms to create pattern recognition models over the range of normal unit operation. To create the monitoring models, historical data from normal operation of the plant is first processed by the heat balance model to compute the derived parameter data. The result is a greatly expanded set of normal operating data that can be used as input to create the pattern recognition model. Once the models are calibrated for normal operation, the hybrid model is suitable for use in continuous online monitoring. During online monitoring, new plant operating data is processed first by the heat balance model and then by the pattern recognition model. Results from the pattern recognition model quantify the deviation of each measured or derived parameter from its expected value in normal operation. The hybrid models can detect abnormal changes in plant operating data with very high accuracy and sensitivity. When abnormal behavior is detected, alerts are generated automatically for evaluation by the plant monitoring staff. The new hybrid solution product was developed and verified in the performance of the project. The hybrid solution was tested first in a simulation environment that mimicked the plant data systems and infrastructure used by U.S. power generating plants and utilities. The hybrid solution was then deployed for real-time, online monitoring of an operating coal-fueled power plant at a field test site. Field testing demonstrated that all hybrid solution development objectives were accomplished. The project work was based on combining the capabilities of two existing software products to create the new hybrid solution product. One of these was the existing MapEx® heat balance product and the other was the existing SureSense® advanced pattern recognition product. Each of these separate products was assessed to be at a Technology Readiness Level (TRL) of 9 at the start of the effort. The hybrid solution product was assessed to be at a TRL of 2 at the start of the project based on early feasibility work by the project team. At completion of the field testing performed in the project, the hybrid solution product was assessed to be at a TRL of 7. The project team expects that the hybrid solution product will be deployed commercially and will achieve a TRL of 9 within one year after completion of the project.

The ability to extend calibration intervals for nuclear plant instrumentation has multiple benefits for improving productivity and reducing operating costs at nuclear plants. Benefits include fewer calibrations inside containment during an outage and associated reduced critical path time and ALARA exposure, reduced risk of calibration error or instrument damage during removal and replacement, and reduced operations and maintenance cost for instrument removal, calibration and replacement. A good instrument calibration program ensures instruments are checked frequently enough to provide a high level of confidence that they are performing within acceptable limits, but no more frequently. Over-testing of plant instruments and equipment should be avoided for two reasons: valuable resources are expended on maintenance that might not measurably improve plant safety, reliability, or efficiency; and the potential exists for adjustment errors or equipment damage each time an instrument is removed from service for testing. Over-testing increases the risk of errors or damage being introduced without a justifiable improvement in reliability. This paper discusses the regulatory framework for extending calibration intervals of safety related instruments for U.S. based nuclear power plants. Necessary changes to licensing, plant processes and procedures, training, and configuration management are summarized. An example application of pattern recognition modeling is provided to highlight the analytical support for the processes provided by active monitoring to confirm on-going instrument heath. The paper concludes with a listing of recommended steps to implement a practical program for extending calibration intervals of safety related instruments within the U.S. nuclear regulatory environment.

The US Air Force’s two main aeropropulsion test centers, Arnold Engineering Development Center and the Air Force Flight Test Center, are developing a common suite of modeling and simulation tools employing advanced predictive modeling technologies. This common set of modeling and simulation tools incorporates real-time data validation, system identification, parameter estimation model calibration, and automated model updating as new test results or operational data become available. The expected benefit is improved efficiency and accuracy for online diagnostic monitoring of Air Force assets. These resultant models could also be used for flight manual development, determining compliance to specifications, or to aid in real-time equipment monitoring. This paper describes the integrated approach to system identification, parameter estimation, and model updating. Implementation of a software package to enable efficient model handoff between test groups and centers is discussed. An F/A-22 inlet model is used to demonstrate the approach. Compact polynomial function models of the distortion and recovery flow descriptors and 40-probe pressure values are derived from quasi-steady and instantaneous subscale wind tunnel data. The model parameters are then calibrated with F/A-22 flight test data. Results show that the modeling algorithm captures the relevant nonlinear physics of the application, and the calibration and updating procedure improves the model match to flight data. A companion paper provides preliminary results from integrating the calibrated total-pressure inlet distortion and recovery models into a real-time equipment health monitoring system to support test operations.