AI-based Virtual Sensors Platform for Development Fleets
Published in
Conference paper | CTI Symposium Dec. 2021
Application
Automotive
Keywords
Virtual sensors, vehicle data, EV development, data analytics
Content in a nutshell
A methodology how to use data science for fatigue and durability engineering in automotive R&D, obtained test results and a reflection on trust into AI-based methods.
Development
Maximize insights from series production vehicles
Minimize component cost, CO2 and weight
Testing
Maximize insights from development vehicles
Minimize sensor and testing costs
Operation
Maximize component lifetime
Minimize unplanned failures
Reuse
Maximize reuse quota
Minimize waste and replacement part costs
Virtual Sensors based on CAN bus signals
Published in
Blog article | Medium.com
Application
Vehicles from 2-wheelers to 18-wheelers and other mechatronic equipment
Keywords
Virtual sensors basics, CAN bus signals, vehicle application example
Content in a nutshell
What are virtual sensors? Benefits of replacing physical sensors partly or completely by virtual sensors – reliability, cost and simplicity. How it works: sensor fusion based on CAN-bus signals.
Development
Maximize insights from series production vehicles
Minimize component cost, CO2 and weight
Testing
Maximize insights from development vehicles
Minimize sensor and testing costs
Operation
Maximize component lifetime
Minimize unplanned failures
Reuse
Maximize reuse quota
Minimize waste and replacement part costs
AI-based end-to-end drivetrain monitoring and optimization
Published in
Conference paper | CTI Symposium Berlin 2020
Application
Automotive
Keywords
Automotive durability testing, virtual mileage accumulation, predicted reliability
Content in a nutshell
Our approach to enhance automotive durability testing with digitalization, using virtual sensors, a virtual fleet and virtual mileage accumulation.
Development
Maximize insights from series production vehicles
Minimize component cost, CO2 and weight
Testing
Maximize insights from development vehicles
Minimize sensor and testing costs
Operation
Maximize component lifetime
Minimize unplanned failures
Reuse
Maximize reuse quota
Minimize waste and replacement part costs
Predicting and Forecasting Fatigue Damage of Automotive Components
Published in
Blog article | Medium.com
Application
Automotive
Keywords
Fatigue damage, Remaining Useful Life, RUL prediction, predicted reliability
Content in a nutshell
In-depth explanation about an improved remaining useful life (RUL) prediction of automotive components based on artificial yet realistic fleet data and neural network models.
Development
Maximize insights from series production vehicles
Minimize component cost, CO2 and weight
Testing
Maximize insights from development vehicles
Minimize sensor and testing costs
Operation
Maximize component lifetime
Minimize unplanned failures
Reuse
Maximize reuse quota
Minimize waste and replacement part costs
AI-based Virtual Sensors Platform for Development Fleets
Published in
Conference paper | CTI Symposium Dec. 2021
Application
Automotive
Keywords
Virtual sensors, vehicle data, EV development, data analytics
Content in a nutshell
A methodology how to use data science for fatigue and durability engineering in automotive R&D, obtained test results and a reflection on trust into AI-based methods.
AI-based end-to-end drivetrain monitoring and optimization
Published in
Conference paper | CTI Symposium Berlin 2020
Application
Automotive
Keywords
Automotive durability testing, virtual mileage accumulation, predicted reliability
Content in a nutshell
Our approach to enhance automotive durability testing with digitalization, using virtual sensors, a virtual fleet and virtual mileage accumulation.
Virtual Sensors based on CAN bus signals
Published in
Blog article | Medium.com
Application
Vehicles from 2-wheelers to 18-wheelers and other mechatronic equipment
Keywords
Virtual sensors basics, CAN bus signals, vehicle application example
Content in a nutshell
What are virtual sensors? Benefits of replacing physical sensors partly or completely by virtual sensors – reliability, cost and simplicity. How it works: sensor fusion based on CAN-bus signals.
Predicting and Forecasting Fatigue Damage of Automotive Components
Published in
Blog article | Medium.com
Application
Automotive
Keywords
Fatigue damage, Remaining Useful Life, RUL prediction, predicted reliability
Content in a nutshell
In-depth explanation about an improved remaining useful life (RUL) prediction of automotive components based on artificial yet realistic fleet data and neural network models.
AI-based Virtual Sensors Platform for Development Fleets
Published in
Conference paper | CTI Symposium Dec. 2021
Application
Automotive
Keywords
Virtual sensors, vehicle data, EV development, data analytics
Content in a nutshell
A methodology how to use data science for fatigue and durability engineering in automotive R&D, obtained test results and a reflection on trust into AI-based methods.
AI-based end-to-end drivetrain monitoring and optimization
Published in
Conference paper | CTI Symposium Berlin 2020
Application
Automotive
Keywords
Automotive durability testing, virtual mileage accumulation, predicted reliability
Content in a nutshell
Our approach to enhance automotive durability testing with digitalization, using virtual sensors, a virtual fleet and virtual mileage accumulation.
Virtual Sensors based on CAN bus signals
Published in
Blog article | Medium.com
Application
Vehicles from 2-wheelers to 18-wheelers and other mechatronic equipment
Keywords
Virtual sensors basics, CAN bus signals, vehicle application example
Content in a nutshell
What are virtual sensors? Benefits of replacing physical sensors partly or completely by virtual sensors – reliability, cost and simplicity. How it works: sensor fusion based on CAN-bus signals.
Predicting and Forecasting Fatigue Damage of Automotive Components
Published in
Blog article | Medium.com
Application
Automotive
Keywords
Fatigue damage, Remaining Useful Life, RUL prediction, predicted reliability
Content in a nutshell
In-depth explanation about an improved remaining useful life (RUL) prediction of automotive components based on artificial yet realistic fleet data and neural network models.
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