demo day iot driven engineering - aalto · 2019. 1. 22. · 7 21/01/2019 iot driven engineering /...
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IOT DRIVEN ENGINEERING
18.1.2019Matti LehtoValtteri Peltoranta
ENGINEERING ASSUMPTIONS CHALLENGED BY IOT DATA
Crane engineering
Analytics IoT data
Digital Twin
Assumptions from standards, books, …
21/01/2019 IoT driven Engineering / Matti Lehto, Valtteri Peltoranta2
Physical crane
DIGITAL TWIN IN PLM AS A NEW APPROACH
21/01/2019 IoT driven Engineering / Matti Lehto, Valtteri Peltoranta3
21/01/2019 IoT driven Engineering / Matti Lehto, Valtteri Peltoranta4
PLM EXTENDED TOWARDS REAL WORLD
Analyzed component information from Service and Engineering viewpoints
IoT data:Usage & condition monitoring
Analyses:EngineeringcriteriaVs.Usage & service history
Service data:Faults & repairs of components
21/01/2019 IoT driven Engineering / Matti Lehto, Valtteri Peltoranta5
ANALYZED COMPONENT INFORMATION
Service view
• Maintenance tasks & inspections based on Twin analyses
• Lifetime estimations for components
• “Digital Twin based maintenance”
Engineering view
• Comparison of design calculations and realized use
• Information about weak points of products
• Optimized dimensioning & selection of componentsà
à
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STATISTICAL VIEW TO SIMILAR COMPONENTS
100%
0%
• Find similar parts in cranes on field• Equalize data from different cranes• Analyse statistically
Average
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USE CASE
Z Ve
rtic
al p
ositi
onLoad forces
FL
Fr1Fr2 Fr3
Rope forcesFr4
R1 Bear
ing
rota
tions
Bearin
g typ
e and
capa
city i
nfo
Algorithms analyzing and reporting bearing status & estimation
Sensored raw dataProcessed info from raw data
a=b*c < E
R2 Bear
ing
rota
tions
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CONCLUSIONS
• IoT and Digital Twins give a chance to make a cultural revolution in engineering
– From assumptions to real world information – design to fit purpose – no over or under engineering
– Connects engineering and service more tightly together (real usage and condition based service)
– Collected facts from field to support discussion between machine manufacturer and customer
– Continuous learning by utilizing field data in every day engineering and service work
• Challenges
– Raw data varies from asset to another and is not always similarly formalized
– Currently available data does not fit directly to existing engineering formulas as such
– Customers are used to refer to international standards
• Machine manufacturer needs to have a lot of analyzed knowledge from the field to renew approach
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