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

Prognostic Analysis of the Tactical Quiet Generator

The U.S. Army needs prognostic analysis of mission-critical equipment to enable condition-based maintenance before failure. ORNL has developed and patented prognostic technology that quantifies condition change from noisy, multi-channel, time-serial data. This report describes an initial application of ORNL's prognostic technology to the Army's Tactical Quiet Generator (TQG), which is designed to operate continuously at 10 kW. Less-than-full power operation causes unburned fuel to accumulate on internal components, thereby degrading operation and eventually leading to failure. The first objective of this work was identification of easily-acquired, process-indicative data. Two types of appropriate data were identified, namely output-electrical current and voltage, plus tri-axial acceleration (vibration). The second objective of this work was data quality analysis to avoid the garbage-in-garbage-out syndrome. Quality analysis identified more than 10% of the current data as having consecutive values that are constant, or that saturate at an extreme value. Consequently, the electrical data were not analyzed further. The third objective was condition-change analysis to indicate operational stress under non-ideal operation and machine degradation in proportion to the operational stress. Application of ORNL's novel phase-space dissimilarity measures to the vibration power quantified the rising operational stress in direct proportion to the less-than-full-load power. We conclude that ORNL's technology is an excellent candidate to meet the U.S. Army's need for equipment prognostication

EEG Signal Classification for Epilepsy Seizure Detection Using Improved Approximate Entropy

Epilepsy is a common chronic neurological disorder. Epilepsy seizures are the result of the transient and unexpected electrical disturbance of the brain. About 50 million people worldwide have epilepsy, and nearly two out of every three new cases are discovered in developing countries. Epilepsy is more likely to occur in young children or people over the age of 65 years; however, it can occur at any age. The detection of epilepsy is possible by analyzing EEG signals. This paper, presents a hybrid technique to classification EEG signals for identification of epilepsy seizure. Proposed system is combination of multi-wavelet transform and artificial neural network. Approximate Entropy algorithm is enhanced (called as Improved Approximate Entropy: IApE) to measure irregularities present in the EEG signals. The proposed technique is implemented, tested and compared with existing method, based on performance indices such as sensitivity, specificity, accuracy parameters. EEG signals are classified as normal and epilepsy seizures with an accuracy of ~90%

Epileptic Seizure Classification Using Image-Based Data Representation

Epilepsy is a recurrence of seizures caused by a disorder of the brain in over 3.4 million people nationwide. Some people are able to predict their seizures based off prodrome, which is an early sign or symptom that usually resembles mood changes or a euphoric feeling even days to an hour before occurrence. Consequently, the natural instincts of the body to react to an upcoming attack lends credence to the existence of a pre-ictal state that precedes seizure episodes. Physicians and researchers have thus sought for an automated approach for predicting or detecting seizures. In this research, we evaluate the image-based representation of EEG as a basis for classification and training of machine learning algorithms. We explore only the raw EEG data for images in lossless image file formats, though there are other forms including symbolized and noise-filtered that can be explored. Furthermore, we evaluate different color mapping schemes (symbolized, default, chromatic, and binned) that assign EEG data values to Red-Green-Blue (RGB) pixel values. We report the performance of machine learning algorithms such as Random Forest to accurately classify EEG-based images as either event (with a seizure) or non-event (without a seizure)

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

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