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> > >Keeping technology alive.
Author:
Date:
Reference:
Algorithms and Processes for Maintenance Optimization
V1
F. Langmayr / E. Lichtenegger
10 2019
> > >10.10.2019 2
▪ Introduction and Motivation
▪ Information Merging
▪ Recommendation System
▪ Process Development
Content
© Uptime Engineering
> > >10/10/2019Uptime Engineering GmbH 3
The Experts for the Reliability Process
We provide reliability solutions
Consultancy and Software for
Analytics, Diagnostics and Prognostics
We support the entire lifecycle of mechatronic systems: verification & validation & operation.Our methods are based on a broad expertise in failure physics and applied statistics.
We have implemented more than 100 projects with leading OEMs and fleet operators.We optimize product development.
We support cost reduction via optimization of service and maintenance.
by Franz Langmayr et al. Employees European Locations
Founded in 2010 A team of 16 Based at 2
> > >
Uptime‘s Vision & Mission
Uptime‘s Visionis keeping technology alive
Uptime‘s Missionis to extract maximum value from datato support the reliability of mechatronic systemsthroughout their entire lifecycles
10/10/2019Uptime Engineering GmbH 4
> > >
CoreCompetences
B2B Software Reliability Engineering
Physics of Failure Applied Statistics
10/10/2019Uptime Engineering GmbH 5
> > >10.10.2019 6
Business Case: Unmanned operation in harsh environment
© Uptime EngineeringSource: Wikipedia
> > >10.10.2019 7
Sustainable Cost Reduction Performance Improvement
Objectives
© Uptime Engineering
Risk Mitigation Failure Minimization
> > >10.10.2019 8
The Potential of MODEL-BASED ANALYTICS:
© Uptime Engineering
Max. Availability
Operation Planning
Evaluate Load Factors
Minimize MTTR
Efficient Usage
Equipment Distribution
Benchmark Load Spectra
Minimize Idling
Max. Lifetime
Protective Operation
Alarm and Warn
Operate Preventive
Equipment
Health State
Agile Operation
Indicate Outliers
Trigger Service
Cost Transparency
Resource Allocation
Calculate Cost/Benefit
Buy /Lease
Risk Management
Identification
Mitigation
Verification
CustomerExpectation
Use Cases
Tasks
> > >10.10.2019 9
▪ Introduction and Motivation
▪ Information Merging – ROMEO Project
▪ Recommendation System
▪ Process Development
Content
© Uptime Engineering
ROMEO Project- The Objectives
Increase wind farm reliability and decrease the number of failures leading to downtime.
Increase the life time of key turbine components.
Reduce the WT O&M costs through the reduction of the resources required for annual inspections of the turbine.
Reduce the O&M costs associated to foundation through reduction in jacket substructures inspections.
Greater reliability, less repairs, more safety
This project has received funding from the European Union’s
Horizon 2020 research and innovation programme
under grant agreement No 745625.
10.10.2019Uptime Engineering GmbH 11
Uptime Engineering in ROMEO
Roles and responsibilities
▪ Integration of multiple data sources
▪ Analysis and combination of information
▪ Centralized O&M Platform for access by multiple stakeholders
▪ Support of maintenance process
▪ Reporting and communication on multiple channels
Making information usable
10.10.2019Uptime Engineering GmbH 12
Uptime Engineering in ROMEO
Roles and responsibilities
Degradation Analytics Engines
Historical and real time data• SCADA Data• Inspection Data• Environmental Data• Operational Data
Uptime Data Examination• Event Based Reasoning• KPI Calculation• HARVEST physical models
O&M Information• Analytics Modules Results• Monitoring• Visualization and analysis
Uptime Advisory Generation• Failure Diagnosis• Maintenance Process• KPI (Availability, MTTF,
Statisitics)
Data acquisition and cloud-based analytics
Data Merging End user application
> > >10.10.2019 13
▪ Introduction and Motivation
▪ Information Merging – ROMEO Project
▪ Recommendation System
▪ Process Development
Content
© Uptime Engineering
> > >10.10.2019 14
Machine Learning / Neural Network Knowledge/Physics-based Models
The Solutions
© Uptime Engineering
For both approaches: top-quality, risk-related (“wise”) data are essential for reliable results
INPUT
▪ SCADA▪ Events
Trained network
Failure
Prognosis
Physics-based
Models
INPUT
▪ SCADA▪ Events▪ Rules▪ System Info
OUTPUT
▪ Detection
OUTPUT
▪ Detection▪ Diagnosis▪ Prognosis
> > >10.10.2019 15
State DETECTIONIs there anything remarkable?
System Supervision
and
Pattern Recognition
and
System Response Models
→ Failure Indicators
→ Alarm & Warning
DIAGNOSISHow did it come?
Domain Failure Knowledge
and
Reasoning Engine
and
Reliable Observation
→ Failure Modes
→ Problem Solving
PROGNOSISWhat next?
Physics of Failure Models
and
Lifetime Models
and
Load History
→ Risk Prediction
→ Recommendation
The UPTIME Engineering Route to Predictive Maintenance
© Uptime Engineering
> > >10.10.2019 16
State DETECTION | System Response Modelling
© Uptime Engineering
Residual-Trend-Analysis delivers outlier detection with high sensitivity AND extremely low frequency of false alarms
Time [10 min]
Bea
rin
g Te
mp
erat
ure
[°C
]= Measured
= Model
Res
idu
al [
°C]
Time [10 min]
Subsequent Outliers, Running average, Slope, …
Fixed Alarm Level: not sufficient
> > >10.10.2019Uptime Engineering GmbH 17
DIAGNOSIS | Model based Reasoning Engine
Turbine Operation
Model based Reasoning
Formalized expert
Knowledge
FeedbackUE Knowledge base
Root Cause Observations : Diagnosis : Maintenance Task
UE Reasoning EnginePropositional Logic
Reduced Power AND NOTWear of blade AND NOTCurtailment ANDGenerator de-rating
Formalized ObservationsDiagnosis:Generator
Overheating: Loss of insulation
State: Generator overheated Maintenance
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Cavitation
Wear
HCF
TMF
KNOWLEDGE BASEDMONITORING
EventsCorrelations
Patterns
-----------------------------
MODEL BASEDANALYTICS
DamageFailure Probability
Alarm
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PROGNOSIS | Damage Accumulation terminates Lifetime
© Uptime Engineering
> > >10.10.2019 19
▪ Introduction and Motivation
▪ Information Merging – ROMEO Project
▪ Recommendation System
▪ Process Development
Content
© Uptime Engineering
> > >10.10.2019 20
Service Process Re-Engineering
▪ Identify objectives and BUSINESS CASE(S)
▪ Map organisation in PROCESS
▪ INVOLVE all contributing parties
▪ Provide BENEFIT for each involved party
SERVICE PROCESS | Design
© Uptime Engineering
> > >
SERVICE PROCESS | Digitization for CBM
Service Staff
Spare Parts
Equipment
Issues & DiagnosisOptimised Service
FeedbackReasoning
EngineKnowledge
Base
Time Series DB
10.10.2019© Uptime Engineering
> > >10.10.2019 22
Quick Wins
▪ Elimination of O&M weak-points
▪ Value from available data
Corporate Development
BENEFIT
© Uptime Engineering
▪ Technical & process lessons learned
▪ Feedback drives quality and involves the staff
Hands-on Approach
▪ User-acceptance via integration of service staff
▪ Focus on risk-drivers
Analytics & Process
▪ Results trigger the CBM-process
▪ Functional extension acc. to process evolution
> > >
www.uptime-engineering.com