PM Optimization: Using Data-driven Analytics for Life Centered Maintenance
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1 5/8/4 WE KNOW WHAT HAPPENS NEXT PM Optimization: Using Data-driven Analytics for Life Centered Edzel Lapira, PhD David Siegel, PhD Rodrigo Vieira OUR HISTORY & CORE TECHNOLOGY Predictronics was started by senior researchers and developers from the National Science Foundation Industry/University Cooperative Research Center for Intelligent Systems (IMS), which has been a leader in predictive maintenance technologies since. At the core of Predictronics solutions is the Watchdog Agent Toolbox: A collection of intelligent, rapidly deployable software agents that can enable users to realize worry-free uptime of critical assets.
2 5/8/4 INDUSTRY VALIDATIONS E ABL R GU FI PREDICTRONICS VALUE PROPOSITION R E C ON 4
3 5/8/4 EVOLUTION OF MAINTENANCE PARADIGMS Trend of Paradigm for Engineering Systems Reactive Preventive Condition- Based Prognostics and Health Management Fail & Fix Time based Reliability centered maintenance FMECA usage based (unit, hours, ) Need based Condition monitoring and assessment Predict & Prevent Predictive maintenance CBM+ ISHM, IVHM Precision & Optimal PHM is a system engineering discipline focusing on detection, prediction, and management of the health and status of complex engineered systems. -- the First International Conference on PHM (8) 5 PM OPTIMIZATION REACTIVE and/or PREVENTIVE LIFE CENTERED MAINTENANCE CONDITION- BASED and/or PROGNOSTICS & HEALTH MANAGEMENT EVENT-BASED ( Records, alarms, and fault logs) HYBRID MODEL DATA-DRIVEN (Controller or process data, sensor signals and measurements) 6
4 5/8/4 IMS SYSTEMATIC PHM SYSTEM DEVELOPMENT 7 ROBOT TORQUE MONITORING Data Performance Feature Torque Joint Joint 6 Joint Normal Behavior Most Recent Behavior Joint 5 Joint Health Assessment Joint 4 Health Radar Chart Health Visualization Performance Confidence Value Health Information 8 4
5 5/8/4 TORQUE DATA DURING NORMAL/HEALTHY CONDITION The RMS torque data for each of the six robot axis consists of a single value for one complete cycle of the robot and the value is acquired once per hour. After each cycle, the disturbance torque values in the positive and negative directions are also taken for each joint 9 TORQUE DATA DURING A KNOWN FAULTY CONDITION In this example, the joint profile for axis starts to deviate from the torque profiles for the other 5 axis, indicating that it is experiencing degradation and eventual failure. The failure signature is visually noticeable; however detecting the early signs of this problem requires more advanced analytical methods. 5
6 5/8/4 DATA-DRIVEN ROBOT TORQUE MONITORING ROBOT MONITOR SHOW TREND OPTION 6
7 5/8/4 Life Centered (LCM) Rodrigo Vieira Visiting Scholar Center for Intelligent Systems (IMS) University of Cincinnati Decision Making Process Data / Information (Measurements and Events) Decision Making Support Agent (Events) Support Ex.: Strategy Models 4 7
8 5/8/4 Life-Centered (LCM) Methodology and Practical Example: Asset: Wind Turbine Component: Gearbox : Gearbox Preventive (MTBM = 6 Months)... Theoretical Plan Last Now Why? - Asset Condition - Production Windows - Team availability - Spare Parts availability - Work Tools availability - Weather Constraints, etc... Real Plan Last Now 5 Life-Centered (LCM)... Last Now # s 5 4 Interval Interval Gearbox Preventive Interval Interval PrevMaintCost >> / Preventive Cost PrevMaintCost/h CorrMaintCost >> Corrective Cost CorrMaintCost/h TotalDownTimeCorr TBM >> Total No Produced Energy due to TotalDownTimeCorr/h Corrective TotalNoProdCorr >> Total Downtime due to Corrective TotalNoProdCorr/h Label Description Very Good + Good + Ok Bad - Very Bad - = - Unsatisfactory Life-Requirement - Below Life-Requirement Reference Achieve Life-Requirement Reference + Above Life-Requirement Reference + Greatly Above Life-Requirement Ref. Life-Requirements (Very Bad) (Bad) (Ok) (Good) (Very Good) PrevMaintCostHour($/h) Life-Requirements Class 6 8
9 5/8/4 Life-Centered (LCM) Wind Turbine Examples: Gearbox Preventive 4 Gearbox Preventive Gearbox Preventive 7 Gearbox Preventive # s 8 6 # s 6 4 # s 4 # s WindFarm Class for PrevMaintCostHour($/h) WindFarm Class for CorrMaintCostHour($/h) WindFarm Class for TotalDownTimeCorr(h) WindFarm Class for TotalNoProdCorr(kW) # s.5 Very Bad Bad Ok Good Very Good # s.5 Very Bad Bad Ok Good Very Good # s.5 Very Bad Bad Ok Good Very Good # s.5 Very Bad Bad Ok Good Very Good WindTurbine Class for PrevMaintCostHour($/h) WindTurbine Class for CorrMaintCostHour($/h) WindTurbine Class for TotalDownTimeCorr(h) WindTurbine Class for TotalNoProdCorr(kW) 7 Life-Centered (LCM) Interval 4x Life-Requirements 4x Life-Requirements Class Multi-Dimensional Scaling (MDS) MDS MDS 8 9
10 5/8/4 Life-Centered (LCM) GLR Indicator Evaluation Gearbox Preventive Gearbox Preventive GLR(t) = ( γ r LR r (t) Gearbox Preventive 4 Gearbox Preventive Gearbox Preventive 7 Gearbox Preventive 8 # s # s WindFarm Class for PrevMaintCostHour($/h) # s WindFarm Class for CorrMaintCostHour($/h) # # s WindFarm Class for TotalDownTimeCorr(h) # s WindFarm Class for TotalNoProdCorr(kW) Interval # s Gearbox Preventive WindFarm GLR Indicator WindFarm GLR Indicator # s # s # s Good WindTurbine Class for PrevMaintCostHour($/h) WindTurbine Class for CorrMaintCostHour($/h) # s WindTurbine Class for TotalDownTimeCorr(h) WindTurbine Class for TotalNoProdCorr(kW) MDS Ok Very Good + # s.5 # s - - WindFarm GLR.5 Indicator Very Bad - Bad WindTurbine GLR Indicator - - WindTurbine 4 GLR Indicator MDS Greater Cincinnati-Northern.5Kentucky 9 # s WindTurbine GLR Indicator Life-Centered (LCM) Clustering Decision ( Launch a ) For Similarity Normal v.s. Abnormal Label Based on System/Machine Objective Better v.s. Worse
11 5/8/4 Life-Centered (LCM) Wind Turbine Examples: Gearbox Preventive - GLR Indicator Evaluation - Wind Farm vs Wind Turbine Gearbox Preventive - GLR Indicator Evaluation - Wind Farm vs Wind Turbine 4 BetDec.5 BetDec MDS MDS WorDec WorDec MDS MDS - Life-Centered (LCM) Clustering Decision ( Launch a ) For Similarity Normal v.s. Abnormal Label Based on System/Machine Objective Better v.s. Worse Understanding Help/Support the Decision Making Staff to understand its decisions impact over the machine objective
12 5/8/4 Life-Centered (LCM) Wind Turbine Examples: Gearbox Preventive - GLR Indicator Evaluation - Wind Farm vs Wind Turbine Gearbox Preventive - GLR Indicator Evaluation - Wind Farm vs Wind Turbine 4 BetDec.5 BetDec MDS MDS WorDec WorDec MDS MDS - Life-Centered (LCM) Clustering Decision ( Launch a ) For Similarity Normal v.s. Abnormal Label Based on System/Machine Objective Better v.s. Worse Understanding Help/Support the Decision Making Staff to understand its decisions impact over the machine objective Learning From Previous Expert System (Adaptive Neuro Fuzzy Inference System) - ANFIS) 4
13 5/8/4 Life-Centered (LCM) GLR Estimation (Based on Health Indicators ) OUTPUT VARIABLES (Training Phase) Now GLR Life-Requirements INPUT VARIABLES Measurements AHRV AHRS... AHRVij AHRSij TBM Expert System GLR Estimation f( Measurements ) GLR Rules Interval Now 5 Decision Making Support GLR Estimation f( Measurements ) GLR Real f(life-requirements) GLR Prognosis f( Health Indicators Prediction ) Life-Centered (LCM) Clustering Decision ( Launch a ) For Similarity Normal v.s. Abnormal Label Based on System/Machine Objective Better v.s. Worse Understanding Help/Support the Decision Making Staff to understand its decisions impact over the machine objective Learning From Previous Expert System (Adaptive Neuro Fuzzy Inference System - ANFIS) Prognostic Predict/Prognosis the decisions impact over the machine objective Now 6
14 5/8/4 Life-Centered (LCM) Wind Turbine Examples: Gearbox Preventive - GLR Indicator Evaluation - Wind Farm vs Wind Turbine Weeks GLRp = +. BetDec.5 Gearbox Preventive - GLR Indicator Evaluation - Wind Farm vs Wind Turbine 4 Weeks GLRp = +. Weeks GLRp = +.6 BetDec.5 Weeks GLRp = Week GLRp = -..5 MDS MDS WorDec WorDec - - Week GLRp = MDS MDS Life-Centered (LCM) Clustering Decision ( Launch a ) For Similarity Normal v.s. Abnormal Label Based on System/Machine Objective Better v.s. Worse Understanding Help/Support the Decision Making Staff to understand its decisions impact over the machine objective Learning From Previous Expert System (Adaptive Neuro Fuzzy Inference System - ANFIS) Prognostic Predict/Prognosis the decisions impact over the machine objective Now 8 4
15 5/8/4 Thank you for your attention 9 5
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