Maintenance Optimization

Transcription

1 Maintenance Optimization Predictive vs. Preventive Maintenance Thomas Seitz Product Portfolio Manager Lifetime Management AREVA GmbH Lyon, October 23, 2017

2 Content Lifetime Management Predictive Maintenance / Condition Monitoring Platform DIROM4i ADAM / SIPLUG Online Valve Diagnostics 4 Conclusion p.2 Property of -

3 Aging & Lifetime Management Nuclear power plant long term operation identifies new technical challenges since plants must operate reliably on a continuous high level of safety for 50 and more years. It is necessary to accurately predict the efforts associated with managing age-related degradation of structures and components to optimize maintenance programs and reduce maintenance costs without compromising safety and availability. Plant lifetime management programs are based on Capability to implement a comprehensive aging surveillance strategy based on analytical predictions and experience feedback Use of sophisticated degradation assessment methods with realistic, case specific data regarding materials, operation modes and design Establishment of optimized maintenance and inspection programs with online condition monitoring of critical components p.3 Property of -

4 Lifetime Management Strategy systematic surveillance for passive + active + critical SSCs Optimization of Inspections Maintenance Repair Replacement Spare parts Aging Management in accordance with IAEA NS-G-2.12, IGALL Passive SSCs Analytical prognosis assessment Trending based on inspection feedback COMSY single source SQL Database Active SSCs Health Monitoring for Critical SSCs Monitoring and diagnostics Condition trending Maintenance management Optimization based on maintenance feedback and failure rates Maintenance rule, RCM, AP-913 Diagnosis / Fatigue monitoring - Valves - Pumps - Electrical drives p.4 Property of -

5 Health Monitoring for Critical SSCs Monitoring and Diagnosis Solutions for Improvements in Safety, Availability, Economics and Reliability Slot cap in close position Protection tube Slots to insert wires Expertise areas Loose Parts & Acoustic Vibration (Rotating Machines and nonrotating structures) Valves & Drives (Electrical Signature Analysis) Fatigue Monitoring Leak Detection Process & Performance Monitoring Thermography Oil/Lubrication p.5 Property of -

6 1 2 3 Lifetime Management Predictive Maintenance / Condition Monitoring Platform DIROM4i ADAM / SIPLUG Online Valve Diagnostics 4 Conclusion p.6 Property of -

7 Maintenance Optimization Optimized Maintenance programs are focused on executing maintenance tasks at the right time before breakdown Preventive Maintenance: Based on average or expected life statistics Fixed time-based maintenance intervals Not considering actual condition Maintenance done if needed or not Predictive Maintenance: Based on actual status determination Periodic or continuous condition monitoring Predict future trend of equipment s condition Maintenance at most cost-effective point in time before equipment loses performance time-based interval prediction Reliability Centered Maintenance (RCM) Predictive maintenance + traditional preventive measures = lowest asset Net Present Costs (NPC) for a given level of performance and risk p.7 Property of -

8 The Motivation: data-driven decision to increase the profit (availability) and reduce the cost (maintenance) Hardware History Future Cost Value Increase: - Profit - Revenue - Reliability - Availability - Maintainability - Safety - Lifetime Reduce: - Operational Cost - Workload - Storage cost / spares - Maintenance time - Maintenance tasks - Maintenance cost Data Acquisition (Data) Data Collection (Information) Understanding Data Analytics (Insights) Data Prediction (decisions & actions) Data are a key factor in current and future maintenance p.8 Property of -

9 Architecture for Asset & Lifetime Management Asset Management Asset Pre-Operational & OEM Data Asset Operational Health & Performance Data Asset Operational Maintenance Data Data Analytics & Storage (DBU) Dashboard & Mimic diagram Event Logging & Notification Feature Extraction Data History Fault Priority Matrix Health Assessment & Prediction Data Monitoring & Collection (AMU) Data Logger Data acquisition static/dynamic, analog/digital Condition Monitoring Data processing Monitoring & Event detection Data storage Cyclic & Eventbased storage Sensors Performance Pressure, Flow, Temperature, Mechanical Force, Stress Vibration, Electrical Current, Voltage Resistance, Oil analysis Digital SCADA Interfaces Assets p.9 Property of -

10 Platform for Data Collection and Diagnostics p.10 Property of -

11 Diagnostic and Prediction p.11 Trending and Failure Prediction Use of Failure Mode Effect Analysis (FMEA) & Data Analysis Trending Continuous checking for deviations from normal operating patterns Automated indication of deviations by rule based diagnostics Predictive Analytics using machine learning Property of -

12 Remote Services (1): FMEA-based Diagnostics Shaft Imbalance 1*F0 > 2*F0 Misalignment 2*F0>1*F0 Rub, impacts, loose, clearance M/N*F0 Resonance Blade passing frequency Vane passing frequency Blade-Vane-Passing- Frequency Blade rate frequency Blade tip frequency p.12 N*F0 = Fcrit N1*F0 N1: # blades N2*F0 N2: # vanes N1*N2*F0 blade pass frequency*number of diffusor vanes/common factor (N1, N2) F0*pi*blade length Rolling element bearing Outer ring frequency Inner ring frequency Cage frequency Rolling element frequency N*BPF O N*BPFI N*FC N*BFS Sideband ± k*f0 ± k*fc ± k*f0 ± k*(f0-fc) ± k*fc ± k*(f0-fc) Gearbox Belt Beltpassfrequency belt length Fp=pi*D1*F1/L=pi*D2*F2/L=pi*n1*D1/(Bel t length*60) Fp =N1*F1/N=N2*F2/N=belt frequency * number of belt teeth = n1*z1/60 pi*0.5*(d1+d2)+2*wurzel((d1^2+d2^2)^2/ 4+Centerdistance^2) Stage frequency Tooth meshing frequency Hunting tooth frequency Journal bearing Oil Whirl (0.41, 0.49)*F0 Oil Whip F0>=2*Fcrit Rub M/N*F0 Fi Zi: tooth number M/N * Zi*Fi Zahneingriffsfrequenz *commonfactor/(z1*z2) Sideband ±k*fi Motor/Generator Supply frequency (stator problem, static gap, Shorted Laminations and Loose Iron) Eccentric rotor (variable air gap) Synchronous frequency Rotor speed frequency (static eccentricity, Slip frequency Polpassing frequency (rotorproblem, imbalance, ) Slot passing frequency (Loose rotor bars) 2*n*FL 2*n*FL Fsf = FL/p k*f0 FL/p-F0 = s*fsf n*fl k*rb*f0 k*ss*f0 Property of - Sideband ±fppf ±2*n*fL ±i*fl±k*frs ±k*fppf Fppf=p*Fsf ±2*n*fL Static eccentricity F0 * RB ±j*fl, j = 1,3,5 Dynamic eccentricity Stator mechanical damage (winding problem) Rotor defect (electric anisotropy, Broken or Cracked rotor bars or shorting rings) F0 * RB F0* SS ± n*fl n*fl n*f0 ±j*fl ±k*f0 j =1,3,5 k=1,2 ±k*fppf

13 Remote Services (2): FMEA-based Diagnostics p.13 Property of -

14 Remote Services (3): FMEA-based Diagnostics p.14 Property of -

15 Be predictive, see problems coming, be prepared! How can you benefit? Example Large 8 MW Pump Repair No data Changes in the spectra indicate dry friction The indicators were disregarded The plant was surprised and was not prepared 6 weeks of unplanned downtime p.15 Damages on carbon bearing & cladding near piston rings Property of -

16 Content Lifetime Management Predictive Maintenance / Condition Monitoring Platform DIROM4i ADAM / SIPLUG Online Valve Diagnostics 4 Conclusion p.16 Property of -

17 Valve Condition Monitoring Installed in nearly all plant systems valve & actuator safe and reliable operation is a key factor for overall plant performance = Significant part of maintenance program Conventional approach: Recurrent at-the-valve testing during outages mainly linked to preventive maintenance tasks Implementation of predictive maintenance: Periodic or continuous condition monitoring Several valve specific parameters (mechanical and electrical) can be used for diagnosis and assessment of MOV, AOV, SOV Measuring of electrical parameters (current, voltage, active power) is continuously possible at the Motor Control Center MCC via SIPLUG Continuous online monitoring during system operation Minimization of direct measurements on-site & disruption of normal operation Assessment of mechanical parameters (e.g. torque / force, friction coefficient) based on actuator characteristics p.17 Property of -

18 ADAM / SIPLUG Valve Diagnostics Modular solution based on integral valve and actuator concept Data acquisition Signature analyses Automated assessment Measurements of mech. parameters SIPLUG ADAM Software SIPLUG DAW Analytical Evaluation Qualification Testing Diagnostics (Active Power) Baseline Measurement Periodical Inspections Detailed Evaluation (Torque, Stem Thrust, Friction) Trending & Statistics Special Measurements Verification of Function Condition Based Maintenance p.18 Property of -

19 SIPLUG Online Networked online monitoring of valves Automated recording of each valve operation Zero effort for performing a measurement Data immediately available Ring buffer always keeps the last data in SIPLUG s memory Allows post-event analyzing If something happens, it is recorded! Fully automated evaluation ADAM Software Measurement + analysis + assessment SIPLUG data as well as records from other valve diagnostic systems p.19 Property of -

20 1 2 3 Lifetime Management Predictive Maintenance / Condition Monitoring Platform DIROM4i ADAM / SIPLUG Online Valve Diagnostics 4 Conclusion p.20 Property of -

21 Conclusion RCM and predictive maintenance experiences and benefits: Implementation of predictive technologies Maintenance optimization > cost savings Continuous verification of plant asset status in operating and safetyrelated conditions Increased plant safety Early detection of changes + trending > prevention of breakdown Increased plant availability A state-of-the-art asset monitoring platform and diagnostic solutions provide the technology base for implementation of preventive maintenance strategies Reliability Centered Maintenance is combining predictive and preventive measures to optimize maintenance programs and reduce maintenance costs without compromising safety and availability p.21 Property of -

22 Thank you very much for your attention Questions? Thomas SEITZ, AREVA GmbH, p.22 Property of -

23 Any reproduction, alteration, transmission to any third party or publication in whole or in part of this document and/or its content is prohibited unless has provided its prior and written consent. This document and any information it contains shall not be used for any other purpose than the one for which they were provided. Legal action may be taken against any infringer and/or any person breaching the aforementioned obligations. p.23 Property of -

24 Remote Diagnostic Service Increasing amount of complex data requires new solutions Asset monitoring of one plant or even a fleet generates massive amounts of data Efficient management of large amounts of data (different sources) and timely expert analysis via Remote Diagnostic Service Combination of Big-Data analytics with diagnostic expert know-how and FMEA resulting in structured data and less data transfer System based on modern Industrial Internet-of-Things network with proven cyber security p.26 Property of -