Big Data Based Maintenance, BDBM--The Main Trend of Smart Maintenance Li Baowen
Future Development of Smart Maintenance Intelligent inspection and monitoring Intelligent lubrication Intelligent information processing Intelligent failure diagnosing Intelligent spare part managing Intelligent strategy generation Framework 智能维护 Smart 总体框架 Maintenance Intelligent knowledge managing Intelligent repair instruction, IETM Intelligent training Web safety intelligent managing Intelligent MRO managing Maintenance Robot OMAINTEC 2
What is BDBM Input From PLC or DCS From daily inspection From historical records From Condition Monitoring Big Data Based Maintenance Output Maintenance policy Maintenance standard Maintenance schedule Possible changed spare parts Multi-channel input,multi-dimension output! An accurate maintenance package should be delivered OMAINTEC 3
Why shall we introduce BDBM? The problem of preventive maintenance: Time based maintenance:according to current statistics, Only 20% preventive maintenance is effective, 80% is invalid, even induce new failures; Condition based maintenance:could avoid the defect of TBM,the input-output ratio should be evaluated; The current status: Although some data from the technological control system could reflect the degradation of the plant, but not being used at all; Main problems of current situation: Much useful data is not utilized, much relative datum are not integrated. OMAINTEC 4
The category suitable BDBM Distribution of Maintenance Modes Yes Yes? No No Yes? Yes The consequence is not serious Breakdown Small economic maintenance lost is the most Zero accident economical No environmental strategy damage No quality damage No health affect No obvious chain damage No measure to monitor the failure All plant Suited to BDBM Obvious exhausting interval Plant or part of it exists obvious exhausting interval, the Time regulation based maintenance is handled by is the us. best strategy The other parts will run normally within this period. OMAINTEC 5
Introduce the Risk management Definition of Risk Risk=Probability Consequence=P C Failure probability is complementary to reliability; P=1-r Where, C means consequence,c [0,1] r means reliability,r [0,1] P is the probability of failure,p [0,1] OMAINTEC 6
Risk Evaluation and Elimination Focus to Evaluation and Elimination: Event Identification Define the basic event Event Frequency Event Tree Event Consequence Probability Matrix Accident Event Consequence Tree Risk Evaluation and Elimination OMAINTEC 7
Relationship of Risk via Maintenance strategy Generator set Part 1 * Part 2 * Part n * * Data resource matrix Turbine set Part 1 * Part n * * Boiler Part 1 * * Part n * DCS Daily inspection Data resource Condition monitoring Historical record Othe r s tr a t e g y BDBM x x x x x CBM x x TBM x x x PIT STOP Maintenanc e stratety matrix 0.07 0.14 0.28 0.42 0.49 0.05 0.16 x x x x x OppT x x x x - - - ProA - - - x x x BrkD x x x x - - - Risk matrix 0.04 0.28 0.12 0.01 0.04 0.05 0.06 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.7 disaster >500 million 0.6 serious >100 million >100 death >10 death 0.5 key >10 million >1death Consequence matrix 0.4 big > million =1 death 0.3 general >100 k Labor hour lost 0.2 Small >10 k Medical treatment 0.1 Very small <10 k hurt image economic safety Imp ossi ble at all 1/1 000 00 imp ossi ble 1/1 000 0 few Low pos sibl e Probability 1/1 1/1 1/1 000 00Y 0Y Y matrix Ver y pos sibl e ofte n 1/Y 10/ Y OMAINTEC 8
Some concept of Risk Probability of Series System: P=1- r i Where,r i represents the reliability of the i th sub-system. The reliability will be lower while more and more sub-systems are series connected Probability of Parallel System: P= (1-r i ) Where,r i represents the i th parallel sub-system, The reliability will be higher while more and more sub-systems are parallel connected OMAINTEC 9
Color Management of the Risk Risk Evaluation and Color Management Grade of Risk Value of Risk Color Intolerant 0.5-1 Serious 0.2-0.5 General 0-0.2 OMAINTEC 10
Risk Map of Turbine Assuming case: Risk of Turbine High Serious General OMAINTEC 11
Risk Map of Generator Assuming case: Risk of Generator High Serious General OMAINTEC 12
Risk Map of Generator Assuming case: Risk of Generator in deferent time Time Peak period High Serious General Ordinar y time OMAINTEC 13
Risk Map of Escalator in Metro Assuming case: Risk of Escalator Of Metro High Serious General OMAINTEC 14
Risk Map of Escalator in Metro Assuming case: Risk of Escalator Of Metro in different time Time Holidays High Serious General Big event Rush hour Ordinary time OMAINTEC 15
The Significant to set up the Risk Map The Significant: Let higher leader focus to High Risk Area or Equipment ; Reinforce the investment to the high risk area or equipment; BDBM will first choose the High Risk Area or System to test; Risk is a dynamic concept, changing with different loading time, different age of the equipment. So, reviewing and drawing a Risk Map every half year is necessary. OMAINTEC 16
Big Data Based Maintenance--BDBM Logic Process: Determine the high risk equipment; Extract the Feature Value according to different failure of equipment ; Design the feature value trap threshold X-day, Y-day and Z-day before the breakdown occurs based on historical failure process ; Monitoring the feature value continuously or with high frequency; Minus 50% Feature Values drop in the trap, then a light warning start, and a predictive period is given; Exact 50% Feature Values drop in the trap, then a middle warning start, and a predictive period is given; All Feature Values drop in the trap, then a strong warning start, and a predictive period is given; A maintenance decision is made and a maintenance package is generated. OMAINTEC 17
Big Data Based Maintenance--BDBM Inspecting Historical record PLC/DCS Condition monitoring Feature value extracting Trap value ranging Middle warning Failure prediction Y N Monitoring N Y Light warning Failure prediction Strong warning Maintenance package generation What When Who Content of maintenance Where Adress or equipment Time and to period maintain for maintenance Allocation or repair Which point Maintenance technician or team Package number Why and reason Safety Safety Instruction OMAINTEC 18
Big Data Based Maintenance--BDBM BDBM--Case Simulation: From the failure record, we extract 3 feature values :Voltage, Temperature and Flow, and get the changing trend curve like below: Equipment condition record From condition monitoring From DCS control system Voltage Temperature From mobile inspection Flow -30 day -7 day -15 day Failure time t OMAINTEC 19
Big Data Based Maintenance--BDBM BDBM--Case Simulation: We design 3 threshold on the curve before 30, 15 and 7 days as traps to monitor the future status of equipment. Tr7-1 Voltage Equipment condition record Tr30-1 Tr15-1 Temperature Tr30-2 Tr30-3 Tr15-2 Tr15-3 Tr7-2 Tr7-3 Flow -30 day -15 day -7 day Failure time t OMAINTEC 20
Big Data Based Maintenance--BDBM BDBM--Case Simulation: During the process of monitoring, we discover Flow get intotr30-1, then the light warning is started, and the failure will happen within 30 days is predicted; While 3 feature value all get into traps, then the strong warning is started, and the failure will happen within 7 days, and the maintenance package is generated. Tr7-1 Voltage Equipment condition record Tr30-1 Tr15-1 Temperature Tr30-2 Tr30-3 Tr15-2 Tr15-3 Tr7-2 Tr7-3 Flow -30 day -15 day -7 day Failure time t OMAINTEC 21
Maintenance Package--Output of BDBM 6W2H1C What Content of maintenance When Adress or equipment to maintain Time and period for maintenance Where Allocation or repair point Which Safety Safety Instruction Who Maintenance technician or team Package number and reason Why OMAINTEC 22
Maintenance Package--Output of BDBM Three optional maintenance modes of dynamic maintenance package: Restorative maintenance Part replacement maintenance Upgrading or proactive maintenance Performance Tim High maintenance e frequency with degrading performance Tim Maintenance frequency e keep constant with periodic restoration of performance Tim Maintenance frequency e reduced with improving performance Restoring limit Safety warning limit OMAINTEC 23
Big Data Based Maintenance--BDBM Self-learning process of BDBM: Maintenance execution according to the package N N N Y Y Y Y Monitoring continuously Change feature Replenish new one Redesign the threshold N Leaning again OMAINTEC 24
Three Transformation of BDBM 3 transformation of BDBM : 1 Orders awaiting Accurate delivery Human, material saving 2 Guaranteed by quantity Guaranteed by speed Breakdown shorten 3 Reactive action Proactive action Accident reduce OMAINTEC 25
BDBM is the core of Smart Maintenance Data Text Integration in here CBM BDBM Artificial Intelligence OMAINTEC 26
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