Vibration Monitoring and Diagnostics of the Reactor Coolant Pump 22 October 2005 Center for Advanced Reactor Research Jun-Seok Lee

Contents Introduction Current Monitoring and Diagnostic Systems Problems and Root Causes Diagnostic System of KEPRI Conclusions & Recommendations References Introduction Current Monitoring and Diagnostic Systems Problems and Root Causes Diagnostic System of KEPRI Conclusions & Recommendations References

I. Introduction Background KEPRI-EPRI signed a joint research in late 1995. KEPRI joined NMAC project on reactor coolant pump (RCP) condition monitoring managed by EPRI. They found that condition monitoring and diagnostics (M&D) for RCP is one of the biggest issues in nuclear power plants (NPPs). Problems in RCP Seals Motor stators Vibration Fixing those problems often results in significant outages of NPPs. Monitoring systems have become more widely used, understood, and refined. Most of them detect RCP problems by monitoring RCP vibration amplitude. Few of them can diagnose the root causes of the problems. Background KEPRI-EPRI signed a joint research in late 1995. KEPRI joined NMAC project on reactor coolant pump (RCP) condition monitoring managed by EPRI. They found that condition monitoring and diagnostics (M&D) for RCP is one of the biggest issues in nuclear power plants (NPPs). Problems in RCP Seals Motor stators Vibration Fixing those problems often results in significant outages of NPPs. Monitoring systems have become more widely used, understood, and refined. Most of them detect RCP problems by monitoring RCP vibration amplitude. Few of them can diagnose the root causes of the problems.

I. Introduction M&D usage for RCP Oil analysis It uses magnetic classification and spectrometric analysis of oil to detect problems with rotating. Analyzing the data requires a lot of time when compared to the time between the detection of the problem and the failure of the machinery. Bearing-oil temperature analysis It uses a probe to measure bearing-oil temperature. The temperature increases significantly, the severity of the problem has also increased significantly. It is generally used with vibration analysis. Acoustic data analysis It uses the trend of the overall mean value and the spectral signatures. The results can be affected by noise from other sources. Vibration analysis It is currently the most effective method for RCP M&D because it takes most of the physical factors that are affected by vibration into account. It would be very helpful to relate the vibration characteristics to the corresponding factors. M&D usage for RCP Oil analysis It uses magnetic classification and spectrometric analysis of oil to detect problems with rotating . Analyzing the data requires a lot of time when compared to the time between the detection of the problem and the failure of the machinery. Bearing-oil temperature analysis It uses a probe to measure bearing-oil temperature. The temperature increases significantly, the severity of the problem has also increased significantly. It is generally used with vibration analysis. Acoustic data analysis It uses the trend of the overall mean value and the spectral signatures. The results can be affected by noise from other sources. Vibration analysis It is currently the most effective method for RCP M&D because it takes most of the physical factors that are affected by vibration into account. Relating the vibration characteristics to the corresponding factors would be very helpful.

I. Introduction Objective To review the RCP diagnostic system currently being used in NPPs To identify the major RCP vibration problems and their causes To use the information to create knowledge base for diagnosing these problems Objective To review the RCP diagnostic procedures currently being used in NPPs To identify the major RCP vibration problems and their causes To use this information to create knowledge base for diagnosing these problems

II. Current Monitoring and Diagnostic Systems RCP description Contents Specification Kori 1 Kori 2 Kori 3&4 Young gwang 1&2 Uljin 1&2 General Model 93-A 93-AS 93-A1 Model-100 No. of pumps per unit 2 3 Pump Type Vertical, 1 stage, centrifugal Design pressure & temperature (kg/㎠ & ℃) 174.7/343 No. of blades 7 Head (m) 79.9 86.3 84.7 85.3 80.0 Flow(㎥/min) 336.9 385.7 390.2 389.1 378.3 Motor Squirrel-cage induction motor Speed(rpm) 1190 1188 <Reactor Coolant Pumps and Motors Used in KEPCO(KHNP)> <Reactor Coolant Pump (Westinghouse 93A Model)>

II. Current Monitoring and Diagnostic Systems Current RCP M&D systems No. System Company Country Type Tool Application Remarks 1 DIAPO (DIAgnostic des POmpes) EDF France Model-based Vibration Process signals Part of PSAD 2 COMOS (Condition Monitoring System) Germany Vibration analysis Noise Grafenrheinfeld KWU PWR 3 LPMS (Loose Part Monitoring System) Acoustic signal analysis 4 MAINS (MAINtenance Support expert system) Toshiba Japan Offline prototype Cause-consequence matrix Interface with the software developed for Toshiba pump monitoring system that TEPCO is using 5 NSSS-DS (Nuclear Steam Supply System-Diagnostic System) KEPCO(KEPRI) Korea Rule-based deduction with certainty factor operation Seek the primary causal alarm among existing multiple alarms Automatic actions Probabilistic failure modes Kori-2 PWR IBM/PC (Prolog) Diagnosis of 3 main systems: rod control system, RCP & pressurizer 6 Mechanical Diagnostic Expert System G.E. Corporation R&D Center USA Rule-based symptom-fault matrix Vibration analysis (FFT & time-based signal) BWR MS-DOS/UNIX (C) 7 Diagnostic System Gensym Corporation USA & Japan Rule-based Vibrations Tokai-2 BWR Expert shell GEIEMS/G2 8 Decision tree Acoustic emission Process data (pump head, motor temp. & current) 15 possible anomalies 9 Automated surveillance system Argonne National Lab. Pattern recognition Sequential probability ratio test (SPRT) EBR-2 MS-DOS (C)

II. Current Monitoring and Diagnostic Systems DIAPO (DIAgnostic des POmpes) - Electrcité de France (EDF) and Jeumont Industrie Information Identified initial causes Recognized problems Recognized abnormal conditions Locations of faulty components Fault model Associative model Causal model Prototypical model Failure localization model Part of PSAD (Poste de Surveilance et d’Aide an Diagnostic) system An overall diagnostic system Automatically monitors and examines the vibrations and operational data from turbine generators, primary circuits, and RCPs DIAPO (DIAgnostic des POmpes) - Electrcité de France (EDF) and Jeumont Industrie Information Identified initial causes Recognized problems Recognized abnormal conditions Locations of faulty components Fault model Associative model Causal model Prototypical model Failure localization model Part of PSAD (Poste de Surveilance et d’Aide an Diagnostic) system An overall diagnostic system Automatically monitors and examines the vibrations and operational data from turbine generators, primary circuits, and RCPs

II. Current Monitoring and Diagnostic Systems Diagnostic System - Toshiba Corporation Consists of 2 basic parts Data Acquisition System (DAS) Diagnostic Computer (DIC) Diagnostic algorithm Decision tree Data evaluation function To determine the cause of a problem, it uses Data evaluation Cause inference Quantitative and trend estimation Application : TEPCO Diagnostic System - Toshiba Corporation Consists of 2 basic parts Data Acquisition System (DAS) Diagnostic Computer (DIC) Diagnostic algorithm Decision tree Data evaluation function To determine the cause of a problem, it uses Data evaluation Cause inference Quantitative and trend estimation Application : TEPCO

II. Current Monitoring and Diagnostic Systems Mechanical Diagnostic and Expert System - GE Corporation and Gensym Corporation Diagnostic and expert system for BWR Consists of 2 systems General Electric Integrated Equipment Monitoring System (GEIEMS) : Used for collecting and analyzing problem-related data G2 System : Used for diagnosing the causes of problems Application : Tokai-2 Mechanical Diagnostic and Expert System - GE Corporation and Gensym Corporation Diagnostic and expert system for BWR Consists of 2 systems General Electric Integrated Equipment Monitoring System (GEIEMS) : Used for collecting and analyzing problem-related data G2 System : Used for diagnosing the causes of problems Application : Tokai-2

II. Current Monitoring and Diagnostic Systems COMOS (COndition MOnitoring System) and LPMS (Loose Part Monitoring System) – Germany’s nuclear power industry COMOS Specifically to monitor shaft and bearing vibrations Using the power densities and the correlated functions of the pump signals LPMS Using knowledge base that relates acoustical signature data to pump components Automated surveillance system – Argonne National Laboratory An expert system to provide early problem detection to diagnose RCP problems Using Sequential Probability Ratio Test (SPRT) and If-Then rules SPRT : A statistical-based, pattern recognition technique Continually analyzing digitized signals from sensors in RCP Monitoring parameters : rotor speed, vibration level, power, discharge pressure Comparison of two rotor-speed signals to determine the discrepancy of them. COMOS (COndition MOnitoring System) and LPMS (Loose Part Monitoring System) – Germany’s nuclear power industry COMOS Specifically to monitor shaft and bearing vibrations Using the power densities and the correlated functions of the pump signals LPMS Using knowledge base that relates acoustical signature data to pump components Automated surveillance system – Argonne National Laboratory An expert system to provide early problem detection and to diagnose RCP problems Using Sequential Probability Ratio Test (SPRT) + If-Then rules SPRT : A statistical-based, pattern recognition technique Continually analyzing digitized signals from sensors in RCP Monitoring parameters : rotor speed, vibration level, power, discharge pressure Comparison of two rotor-speed signals to determine the discrepancy of them.

III. Problems and Root Causes Key Problems and Root Causes Searching the INPO Nuclear Plant Reliability Data System (NPRDS) database for problems from 1993 to 1996 Yielding 57 problems that resulted in plant trip, shutdown, and power reduction Major causes of problems : vibration, leakage Major causes of vibration problem: shaft, bearing, misalignment Key Problems and Root Causes Searching the INPO Nuclear Plant Reliability Data System (NPRDS) database for problems from 1993 to 1996 Yielding 57 problems that resulted in plant trip, shutdown, and power reduction Major causes of problems : vibration, leakage Major causes of vibration : shaft, bearing, misalignment < Cause of RCP Failures > < Root causes of RCP vibrations >

IV. Diagnostic System of KEPRI Structure of NSSS-DS (Nuclear Steam Supply System-Diagnostic System) < Probe System for Monitoring RCP Vibration > < Structure of Monitoring System >

IV. Diagnostic System of KEPRI General Approach Classify the types of abnormal vibration by cause and problem, using information from NPRDS search and NMAC survey. Determine the diagnostic methods to analyze the data. Evaluate the abnormal vibration characteristics, using information from the following to help with evaluations: Experts, field engineers, and analysts Papers that have been published Case histories on RCP failures Experimental verification Construct the knowledge base. Develop a diagnostic algorithm that does the following: Uses evaluation functions to quantify knowledge expressions. Uses certainty factors to improve the reliability of diagnoses. Shows the probabilistic diagnostic results of each abnormal vibration condition. Estimates the vibration trend and the amount of time needed to shut down the RCP. Identifies the component that may be defective and provides a repair procedure. General Approach Classify the types of abnormal vibration by cause and problem, using information from NPRDS search and from NMAC survey. Determine which diagnostic methods to use to analyze the data. Evaluate the abnormal vibration characteristics, using information from the following to help with evaluations: Experts, field engineers, and analysts Papers that have been published Case histories on RCP failures Experimental verification Construct the knowledge base. Develop a diagnostic algorithm that does the following: Uses evaluation functions to quantify knowledge expressions. Uses certainty factors to improve the reliability of diagnoses. Shows the probabilistic diagnostic results of each abnormal vibration condition. Estimates the vibration trend and the amount of time needed to shut down the RCP. Identifies the component that may be defective and provides a repair procedure.

IV. Diagnostic System of KEPRI Construction of Knowledge Base Survey A survey on RCP condition monitoring and diagnostic techniques which was sent to all EPRI NMAC members of US and international plants Database The NPRDS database was searched for information covered from 1990 to 1996 Experts and case histories Lots of RCP experts were consulted and several RCP case histories were reviewed to obtain information based on RCP experience Literature survey RCP related papers were reviewed to get the information of abnormal vibration characteristics of RCP. Construction of Knowledge Base Survey A survey on RCP condition monitoring and diagnostic techniques which was sent to all EPRI NMAC members of US and international plants Database The NPRDS database was searched for information covered from 1990 to 1996 Experts and case histories Lots of RCP experts were consulted and several RCP case histories were reviewed to obtain information based on RCP experience Literature survey RCP related papers were reviewed to get the information of abnormal vibration characteristics of RCP.

IV. Diagnostic System of KEPRI Cause-Symptoms Matrix for Knowledge Base

IV. Diagnostic System of KEPRI Diagnostic Algorithm Possible method for the diagnostic system : Fuzzy set method, Bayesian method, Dampster shaper method, Certainty factor method Using Fuzzy set method for the system Defining the membership functions to be used to evaluate each fact Calculating the weighting factor of each type of vibration data, using several evaluation function Diagnostic Algorithm Possible method for the diagnostic system : Fuzzy set method, Bayesian method, Dampster shaper method, Certainty factor method Using Fuzzy set method for the system Defining the membership functions to be used to evaluate each fact Calculating the weight factor of each type of vibration data, using several evaluation function < Procedure for Weighting Factor Calculations > < Evaluations Functions >

IV. Diagnostic System of KEPRI Flow chart *Did not conduct in this research.

IV. Diagnostic System of KEPRI

IV. Diagnostic System of KEPRI Output of the Diagnostic System

V. Conclusions & Recommendations Most of RCP monitoring systems can detect RCP problems with monitoring abnormal vibrations, but few of these system can diagnose the root causes of problems. From the survey on NPPs, the identified mechanical faults of the RCP are still on-going issues. From the NPRDS search Vibration is one of the most important symptoms of key failures on the RCP. Major cause of vibration : cracked shaft, misalignment, rubbing, unbalance Recommendations Conduct an experimental verification for improving the reliability of knowledge. Identify the vibration characteristics due to changes in the operation parameters. Determine the relationship between the root causes and the RCP components. Define the various evaluation functions and determine their relationship with the knowledge base. Use probabilistic theory to estimate the optimum amount of time for repairing RCP problems. Share the documented knowledge for each type of RCP with all nuclear power plants. Conclusions Most of RCP monitoring systems can detect RCP problems with monitoring abnormal vibrations, but few of these system can diagnose the root causes of problems. From the survey on NPPs, the identified mechanical faults of the RCP are still on-going issues. From the NPRDS search Vibration is one of the most important symptoms of key failures on the RCP. Major cause of vibration : cracked shaft, misalignment, rubbing, unbalance Recommendations Conduct an experimental verification for improving the reliability of knowledge. Identify the vibration characteristics due to changes in the operation parameters. Determine the relationship between the root causes and the RCP components. Define the various evaluation functions and determine their relationship with the knowledge base. Develop a means of using pattern recognition to automatically enter other parameters. Use probabilistic theory to estimate the optimum amount of time for repairing RCP problems. Share the documented knowledge for each type of RCP with all nuclear power plants.

VI. References Yong Chae Bae, “Consideration for Vibration Monitoring and Diagnostics of the Reactor Coolant Pump”, EPRI NMAC Tech Note, TR-108480, July, 1997. Yong Chae Bae, “A Study on the Diagnostic System for Reactor Coolant Pump”, Journal of Korea Society for Noise and Vibration Engineering, Vol.8, No. 4, pp. 723-732, 1998. Se Woo Cheon, Soon Heung Chang, Hak Yeong Chung, “Development strategies of an expert system for multiple alarm processing and diagnosis in nuclear power plants”, IEEE Transactions on Nuclear Science, Volume 40, Issue 1, pp. 21-30, February, 1993.