1. Seminar 2005-4 Predictive Maintenance Technologies in Nuclear Power Plants - Data Analysis 26 August 2005 Center for Advanced Reactor Research Jun-Seok Lee

2. 2 / 20 Contents I.Introduction II.Analysis methods III.Expert systems IV.PlantView V.Summary and Further works VI.References

3. 3 / 20 I. Introduction Review on the previous seminar Predictive Maintenance (PdM) Definition With technologies and people skills Integration of all available parameters Equipment condition indicators (diagnostic and performance data, operator-logged data) Maintenance histories Design knowledge Making timely decisions about maintenance requirements of important equipment. Object Availability improvement Equipment life enhancement Saving maintenance costs Procedure DetectDetermineRestore

4. 4 / 20 I. Introduction Technologies Acoustic analysis Motor testing Oil analysis Thermography Ultrasonic testing Vibration analysis …… Topics of this seminar PdM data analysis Analysis methods Expert systems

5. 5 / 20 II. Analysis methods Methods Trend analysis Pattern recognition Correlation Test against limits or ranges Relative comparison of data Statistical process analysis Usage Analysts derive information on equipment future performance and condition from the raw data. Each plant and component has its own ranges, limits, patterns, trends, correlations, relative comparisons, and statistical process variations. ⇒ Analyst must adapt these ranges to suit the needs of individual programs.

6. 6 / 20 II. Analysis methods Trend analysis Plotting the value against time with noting the proximity to any system or equipment limits. Looking at the difference or margin between the trend projection and the alert value. Judging the safety of the system or equipment from trend. Exceeding the alert value ⇒ planning the problem correction Exceeding the alarm value ⇒ failure of the system or equipment Predictions with each new reading for an active predictive maintenance program. Alarm Value Alert Value

7. 7 / 20 II. Analysis methods Pattern recognition Using repeatable or characteristic patterns of performance or condition degradation in system and equipment. Requiring an understanding of what signs or patterns of deterioration look like. Possible to predict when recognizing the early formation of patterns and understanding how specific patterns develop. Implementation methods Visual : photographic, video, written observation record Mathematical : including plots used in trending By exception : deviation from normal

8. 8 / 20 II. Analysis methods Issues Deciding the data to record. Deciding how or in what form to record the data. Determining how to ensure reliable recall of recorded data and past analysis results. Documenting the procedures for recording to establish patterns. The needs for the pattern recognition Personnel continuity with long development periods or long repetitive cycles. Making a deliberate, long-term efforts to understand patterns in system and equipment degradation

9. 9 / 20 II. Analysis methods Correlation Confirming a condition from related technologies. Methods Time coincident correlation Looking at data from two or more technologies simultaneously Pattern recognition Time sequenced correlation Looking at data from one, two, or more technologies taken at different times Combining flow analysis with thermal analysis Making the analysis more accurate and credible.

10. 10 / 20 II. Analysis methods Test against limits or ranges Discontinuous trends or non-repeatable patterns of some parameters and giving only indications that are near or beyond an established limit or allowable range of some problems. Method of such data manipulation Recording the number of times a limit or range is exceeded in a plant for similar systems or equipment. Recording the out-of-range direction deviation (high and low) to see if some pattern is developing that shows root cause. Useful in monitoring performance of installed instruments Plotting the data to determine If the plant should change the calibration check period. If the instrument requires more frequent preventive maintenance. Applications for the test against limits or ranges Finite element analysis (FEA), Operating deflection shape (ODS), Computational fluid dynamics (CFD)

11. 11 / 20 II. Analysis methods Finite element analysis (FEA) Numerical method based on the differential equations of motion. Does not require any physical testing although it is highly recommended to correlate with experimental data. Usage : Stress, heat transfer, magnetostatics, large deformation metal forming process, etc. Advantages Possible to estimate deflection shape of equipment and structure. Able to analyze in much greater detail than with experimental method. Disadvantages Necessary to define all of the information such as distribution of mass and stiffness. Making an accurate assessment of all boundary conditions. 3D modelMeshingAnalysis

12. 12 / 20 II. Analysis methods Operating deflection shape (ODS) Providing an animation of the deflected shape of a vibrating system under actual operating conditions. Performed with either a single-channel or multi-channel analyzer. Advantages Possible to perform while the equipment is in operation. Easy and quick to perform. Able to be used to identify areas of structure weakness or looseness. Disadvantages Not providing enough information that excessive vibration is caused by the excitation of resonance. Not providing information regarding the nature of other mode shapes. Not able to be used to quantify the effect that proposed structural modification might have on the magnitude of natural frequency.

13. 13 / 20 II. Analysis methods Computational fluid dynamics (CFD) Concerned with obtaining numerical solutions to fluid flow problems by using computers. Limits of computational power ⇒ Using parallel programming methods Visualization and graphics techniques for analyzing, validating, and presenting the data. Viewing the computed flow field Understanding the nature of the problem Interaction of algorithms with the computer architecture Performance analysis of the code Debugging the solution Heat exchanger Turbine Blade ExperimentCFD result Pipe

14. 14 / 20 II. Analysis methods Relative comparisons of data Binding repeatable readings, curves, or other characteristics to establish direction or amount of data change over time Using relative comparison when Absolute data are not available. Absolute data would be too expensive to obtain. Traceability to a national standard is too difficult or impossible. Statistical process control A technique that is concerned with monitoring process capability and process stability Methods Weibull analysis : Determining whether or not more than one failure mode is causing component loss Weibayes analysis : Performing probabilistic risk assessment calculations for the expected number of component failure Able to determine whether components are at or near a stage of rapid failure rate.

15. 15 / 20 III. Expert systems Expert systems Monitoring systems with diagnostic capability that use artificial intelligence software Object To collect sensor information To analyze sensor information To interpret sensor information To provide early diagnosis of developing problems Components A knowledge base containing the equipment facts, component relationships, mathematical models, etc. Data acquisition hardware Software for sensor inputs Artificial intelligence software Graphical user interface Installation software Simulator

16. 16 / 20 III. Expert systems Benefits Looking at all available sensor data in real time, correlating it as an expert would, and continuously updating the diagnosis based on changing sensor readings Allowing operators to react quickly at the onset of equipment problems Giving a tool for predicting future maintenance needs Example : Generator Expert Monitoring System (GEMS) Developed by EPRI in 1992 Able to analyze several hundred specific generator problems and conditions Handling 4 operating modes Turning gear : While the generator is being operated on turning gear, GEMS focuses on generator auxiliary systems and verifies the accuracy and function of many of the temperature sensors used for on-line monitoring. Startup/shutdown : While the generator is brought to speed, and during coast-down, GEMS continues to monitor the auxiliary systems and process dynamic parameters, such as vibration. Field applied : Once the field breaker is closed, more significant heating begins in the generator rotor and core. GEMS begins to diagnose most parameters used in normal on-line monitoring. Synchronized and on-line : This is when the generator is connected to the substation transmission system and is the most critical mode of operation. The stator current flows and stator temperatures begin to rise.

17. 17 / 20 IV. PlantView PlantView A plant process information system provided by EPRISolutions in 1999 Providing the capability for plant personnel to have common access to vital plant information Using Intranet and Internet technology to provide: Web browser operation Multi-plant views Universal access Efficient conversion of data to information Access to historical records PlantView PdM module Organizing condition-based data and the associated data analyses to facilitate accurate and comprehensive assessments of the operating condition of plant equipment With the module, the user can Record component data Manage data and generate reports Check equipment status Track case histories Calculate savings

18. 18 / 20 IV. PlantView

19. 19 / 20 V. Summary and Further works Summary Introduction to data analysis for PdM Trend analysis Pattern recognition Correlation Test against limits or ranges Relative comparison of data Statistical process analysis Expert systems PlantView Further works Survey on PdM assessments PdM applications Turbine generator, Emergency diesel generator, etc. Advanced PdM technologies

20. 20 / 20 VI. References 1.“Predictive Maintenance Primer”, EPRI technical report, 2003. 2.http://www.ansys.comhttp://www.ansys.com 3.http://www.fluent.comhttp://www.fluent.com 4.http://www.smartplantworks.com/http://www.smartplantworks.com/