Annual Report for NERI Proposal No.2000-0109 on Forewarning of Failure in Critical Equipment at Next-Generation Nuclear Power Plants

This annual report describes the first year's accomplishments under the NERI2000-109 project. We present a model-independent approach to quantify changes in the nonlinear dynamics underlying time-serial data. From time-windowed data sets, we construct discrete distribution functions on the phase space. Condition change between base case and test case distribution functions is assessed by dissimilarity measures via L{sub 1}-distance and {chi}{sup 2} statistic. The discriminating power of these measures is first tested on noiseless model data, and then applied for detecting dynamical change in power from a motor-pump system. We compare the phase-space dissimilarities with traditional linear and nonlinear measures used in the analysis of chaotic systems. We also assess the potential usefulness of the new measures for robust, accurate, and timely forewarning of equipment failure

Epilepsy Forewarning Using A Hand-Held Device

Over the last decade, ORNL has developed and patented a novel approach for forewarning of a large variety of machine and biomedical events. The present implementation uses desktop computers to analyze archival data. This report describes the next logical step in this effort, namely use of a hand-held device for the analysis

Epileptic Seizure Forewarning by Nonlinear Techniques

Nicolet Biomedical Inc. (NBI) is collaborating with Oak Ridge National Laboratory (ORNL) under a Cooperative Research and Development Agreement (CRADA) to convert ORNL.s patented technology for forewarning of epileptic seizures to a clinical prototype. This technical report describes the highlights of the first year.s effort. The software requirements for the clinical device were specified from which the hardware specifications were obtained. ORNL's research-class FORTRAN was converted to run under a graphical user interface (GUI) that was custom-built for this application by NBI. The resulting software package was cloned to desktop computers that are being tested in five different clinical sites. Two hundred electroencephalogram (EEG) datasets from those clinical sites were provided to ORNL for detailed analysis and improvement of the forewarning methodology. Effort under this CRADA is continuing into the second year as planned

Annual Report Nucelar Energy Research and Development Program Nuclear Energy Research Initiative

NERI Project No.2000-0109 began in August 2000 and has three tasks. The first project year addressed Task 1, namely development of nonlinear prognostication for critical equipment in nuclear power facilities. That work is described in the first year's annual report (ORNLTM-2001/195). The current (second) project year (FY02) addresses Task 2, while the third project year will address Tasks 2-3. This report describes the work for the second project year, spanning August 2001 through August 2002, including status of the tasks, issues and concerns, cost performance, and status summary of tasks. The objective of the second project year's work is a compelling demonstration of the nonlinear prognostication algorithm using much more data. The guidance from Dr. Madeline Feltus (DOE/NE-20) is that it would be preferable to show forewarning of failure for different kinds of nuclear-grade equipment, as opposed to many different failure modes from one piece of equipment. Long-term monitoring of operational utility equipment is possible in principle, but is not practically feasible for the following reason. Time and funding constraints for this project do not allow us to monitor the many machines (thousands) that will be necessary to obtain even a few failure sequences, due to low failure rates (<10{sup -3}/year) in the operational environment. Moreover, the ONLY way to guarantee a controlled failure sequence is to seed progressively larger faults in the equipment or to overload the equipment for accelerated tests. Both of these approaches are infeasible for operational utility machinery, but are straight-forward in a test environment. Our subcontractor has provided such test sequences. Thus, we have revised Tasks 2.1-2.4 to analyze archival test data from such tests. The second phase of our work involves validation of the nonlinear prognostication over the second and third years of the proposed work. Recognizing the inherent limitations outlined in the previous paragraph, Dr. Feltus urged Oak Ridge National Laboratory (ORNL) to contact other researchers for additional data from other test equipment. Consequently, we have revised the work plan for Tasks 2.1-2.2, with corresponding changes to the work plan as shown in the Status Summary of NERI Tasks. The revised tasks are as follows: Task 2.1--ORNL will obtain test data from a subcontractor and other researchers for various test equipment. This task includes development of a test plan or a description of the historical testing, as appropriate: test facility, equipment to be tested, choice of failure mode(s), testing protocol, data acquisition equipment, and resulting data from the test sequence. ORNL will analyze this data for quality, and subsequently via the nonlinear paradigm for prognostication. Task 2.2--ORNL will evaluate the prognostication capability of the nonlinear paradigm. The comparison metrics for reliability of the predictions will include the true positives, true negatives, and the forewarning times. Task 2.3--ORNL will improve the nonlinear paradigm as appropriate, in accord with the results of Tasks 2.1-2.2, to maximize the rate of true positive and true negative indications of failure. Maximal forewarning time is also highly desirable. Task 2.4--ORNL will develop advanced algorithms for the phase-space distribution function (PS-DF) pattern change recognition, based on the results of Task 2.3. This implementation will provide a capability for automated prognostication, as part of the maintenance decision-making. Appendix A provides a detailed description of the analysis methods, which include conventional statistics, traditional nonlinear measures, and ORNL's patented nonlinear PSDM. The body of this report focuses on results of this analysis

Failure Forewarning in NPP Equipment NERI2000-109 Final Project Report

The objective of this project is forewarning of machine failures in critical equipment at next-generation nuclear power plants (NPP). Test data were provided by two collaborating institutions: Duke Engineering and Services (first project year), and the Pennsylvania State University (Applied Research Laboratory) during the second and third project years. New nonlinear methods were developed and applied successfully to extract forewarning trends from process-indicative, time-serial data for timely, condition-based maintenance. Anticipation of failures in critical equipment at next-generation NPP will improve the scheduling of maintenance activities to minimize safety concerns, unscheduled non-productive downtime, and collateral damage due to unexpected failures. This approach provides significant economic benefit, and is expected to improve public acceptance of nuclear power. The approach is a multi-tiered, model-independent, and data-driven analysis that uses ORNL's novel nonlinear method to extract forewarning of machine failures from appropriate data. The first tier of the analysis provides a robust choice for the process-indicative data. The second tier rejects data of inadequate quality. The third tier removes signal artifacts that would otherwise confound the analysis, while retaining the relevant nonlinear dynamics. The fourth tier converts the artifact-filtered time-serial data into a geometric representation, that is then transformed to a discrete distribution function (DF). This method allows for noisy, finite-length datasets. The fifth tier obtains dissimilarity measures (DM) between the nominal-state DF and subsequent test-state DFs. Forewarning of a machine failure is indicated by several successive occurrences of the DM above a threshold, or by a statistically significant trend in the DM. This paradigm yields robust nonlinear signatures of degradation and its progression, allowing earlier and more accurate detection of the machine failure

Using invariants to determine change detection in dynamical system with chaos

Change detection, Dynamical systems,