connecting physics based and data driven models: … · changing role of testing in the design and...

Connecting physics based and data driven models: The best of two worldsHerman Van der AuweraerSiemens PLM SoftwareAcknowledging Peter Mas, Cameron Sobie, Bram Cornelis

Unrestricted © Siemens AG 20182018.03.06 Siemens PLM Software

Overview

1 Setting the Scene for the Digital Twin

3 The power of the Digital Twin

4 …in interaction with the Real World

Enter Data Analytics and Deep Learning5

Where to go…6

2 Where do we come from


Product and Production EngineeringAddressing tomorrow’s industry challenges …

From mechanical components to

Smart systems integrating mechanical, electrical,

controls

From defined options

to mass customization and personalization

From known material and production methods

to mixed materials, novel production methods

From internet connectivity

to system of systems and internet of things


Addressing of today’s Product and Production challenges …requires a systematic adoption of the Digital Twin paradigm

The Digital Twin • integrates all data, models (engineering, data-based,

simulation), and otherwise structured information • of a product, plant, or infrastructure system• generated during engineering, commissioning,

operation or service• and that can leverage existing and create new

business opportunities

Simulation is the execution of a model of a real world system to study its behavior, e.g. • 3D CAE models with attached physics• Models of logical behavior and effect-based models

„(1D)“ of the system • Test-based models, data driven models• System simulation as a mixture of all

Simulation and Digital Twin: Definitions Digital Twin: Origin

The Digital Twin has developed from concepts at USAF (modeling with high fidelity the as-built system) and NASA:The Digital Twin, integrates ultra-high fidelity simulation with the vehicle's on-board integrated vehicle health management system, maintenance history and all available historical and fleet data to mirror the life of its flying twin and enable unprecedented levels of safety and reliability.

NASA (2012): The digital twin paradigm for future NASA and U.S. air force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC

Since then it has developed to widely adopted term but often with widely differing interpretations.It has an entry in Wikipedia, it is a Gartner trend…

https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120008178.pdf


Digital Twin provides a huge knowledge base for new applications by coupling data driven and engineered models

Digital Product Twin

Push forward knowledge from engineered models

Digital Production Twin Digital Performance Twin

feed back insights to continuously optimize product and production


Overview





Where to go…6



Changing role of Testing in the design and engineering phaseFrom Test-based design iteration to advanced troubleshooting

Troubleshoot

Concept & Planning

Design & Development

Verification

Production

Design Build

ValidateAssemble


V-cycle for product developmentMultiple disciplines, multiple levels

J


Advanced Testing SolutionsIndustrialising Acoustic Analysis


Advanced Testing SolutionsIndustrialising Modal Analysis

44434241

34333231

24232221

14131211

HHHHHHHHHHHHHHHH

InputsR

espo

nses

0.00

0.10

Log

((m/s2 )/N

)

0.00 80.00Hz-180.00

180.00

Phas

e°

*

*1

( )n

k k

k k k

A AHj j=

ω = +ω−λ ω−λ∑


“Shaking things up – Airbus speeds up ground vibration testing for the A350-1000” Airbus Newsroom, 13/02/2017

Verification and integration testingSmarter and shorter ground vibration testing

Ground vibration testdrastically reduced:• A380 1 Month• A400M 3 Weeks• A350-900 9 Days • A350-1000 2 Days

Source Youtube https://www.youtube.com/watch?v=L8WMohu83zo

https://www.youtube.com/watch?v=L8WMohu83zo


Major breakthrough due to computing power and simulationThe Virtual Prototyping Approach

Troubleshoot

Concept & PlanningDesign & Development

VerificationProduction

Design Analyze Build Validate

Optimize

Propose Design Alternatives

Capture Knowledge in Rules

Acquire Knowledge


Simulation is evolving from a trouble shooting tool to addingcustomer value in the form of digital twins

Next-generation digital twin

Key challenges‒ Keep real and digital

worlds ‘in sync’ easily‒ Close the data loop from

operations back to design‒ Generate knowledge from

distributed models‒ Overcome expertise-

limited scalability of use‒ Apply novel simulation

technologies and convergence with data analytics and IoT

CAxSunriseModel

Pioneers

PLMPervasion

DigitalTwin Era

‒ Scientific experts use models

‒ Understanding of phenomena

‒ Computer technology aid in product design and engineering

‒ Model-basedSystems Engineering

‒ Key for communication

‒ Combining the virtual and physical world

‒ Bridge value chains

~1985 ~2000 ~2015Timeline


SimcenterTM: Simulation and Test SolutionsEnabling “Digital Twin” for Closed Loop Performance Engineering

Scalable 1D - 3D Simulation Incl. Real-TimeVirtual to Physical

1D – BehavioralSimulation

LMS Imagine.Lab

LMS Amesim

J

Virtualto

Physical

LMS Test.Lab

Real-Time Simulation

3D / CFDSimulation

Simcenter 3DNX CAE, LMS Virtual.Lab,

LMS Samtech SuiteNX Nastran

CD-adapcoSTAR-CCM+


Overview





Where to go…6



Delivering “Digital Twin” ExperiencePredict Gas Turbine Blade Shape

Challenge

Gas Turbines operate in an extreme environment, causing the blades to significantly deform. It impacts the overall efficiency, and life of the blade

Objective

Predict aerodynamic performance, cooling flow rates, blade loading and solid temperatures, stress and blade deflection. Deform the analytic CAD from the as-manufactured shape to the as-operated and feed it back for updated flow & thermal prediction

Solution• CAD – NX CAD• CAE

• FE – Simcenter 3D• CFD – Star-CCM+


Delivering “Digital Twin” ExperienceOptimize NVH of Electrical Vehicle

Challenge

Optimize NVH (Noise & Vibration) from e-powertrain integration in vehicle reliability

ObjectivePredict structural and acoustic loading of e-powertrain in electric vehicle.Predict vehicle NVH from e-powertrain optimization.Enable combined optimization acrosse-powertrain and vehicle design.

Vehicle NVH – e-Powertrain Integration

e-Powertrain Structural and Acoustic Loads

Solution• EM – Star-CCM+, SPEED• FE – Simcenter 3D• 1D – Simcenter 1D


Delivering the “Digital Twin” Experience:Virtual Thermal Aircraft

EC – Project TOICANSR 2030+ Trade-Off studies

Virtual Integrated Aircraft - VIA

Mission & Environment

Propulsion

Structural-Thermal Pneumatic

Electric

Hydraulic

Fuel


Digital continuity to verification and integration testing “Shift Left” through virtualization of iron bird

Real-TimeMiL – SiL – HiL

Sub-system BenchesPilot-in-the-Loop

(Physical) Iron Bird Flight Testing

FF -3 Year FF -1 Year First Flight Certification

System Verification Models

Virtual Iron Bird

System Development Models


Delivering “Digital Twin” ExperienceOptimize Range and Thermal Comfort of Electrical Vehicle

Challenge

Extend electrical drive vehicle range while ensuring optimal passenger thermal comfort

ObjectiveBalance battery performance, to HVAC and control systems. Full system simulation with Battery and Electric Motors coupled with detailed 3D cell model to predict cell temperature variation, and optimize battery pack

Solution• CAE

• FE/System - Simcenter 1D, 3D• CFD – Star-CCM+

• Optimization

AC systemBattery Coolingsystem

Vehicle

ElectricPowertrain

Cabin

Control


Delivering “Digital Twin” ExperienceOptimize Efficiency of Excavator for Realistic Soil Loading

Challenge

Predict fuel consumption, structure integrity and durability for different soil conditions

Objective

Full co-simulation, loads coming from hydraulic pumps/cylinder are “piloting” the 3D / CFD multi-body model. Provide arms and bucket motion, predict load and stresses based on DEM based soil particle analysis, alter the motion, and feed it back into the system model

Solution• CAE - Simcenter 1D & 3D Motion• Soil models - STAR-CCM+


Delivering “Digital Twin” ExperienceEnabling V&V of Systems for Autonomous Driving …

“14.2 billion miles of testing is needed”Akio Toyoda, CEO of ToyotaParis Auto Show 2016

“Design validation will be a major – if not the largest – cost component”Roland Berger, “Autonomous Driving” 2014

RealTime

MassiveHPC

Environment, Traffic, Sensors, Vehicle, Controls, Occupant…


Addressing New Challenges in Controls Engineering…Model Based Optimal Control – Model Predictive Control

Similar to human driving Dynamic constraints Input constraints

2. Control Objective

Smooth trajectory Time-optimal driving Safe vehicle cornering

3. Dynamic Trajectory Optimization Find the optimal trajectory over a receding horizon Online optimization Visually inform the driver about reference path and vehicle velocity

1. Model for Prediction


Addressing New Challenges in Controls Engineering…Model Based Optimal Control – Active Suspension Case

Innovative active damper designModel Based Control design and rapid prototypingBuilding into commercial vehicle (Ford S-Max)Validation on test trackFlemish research cooperation project Flanders Make


Addressing New Challenges in ManufacturingAdditive Manufacturing demands rethinking product engineering

Major research efforts needed

Seamless AM CAE approach Topology optimization Virtual/materials testing... Performance of AM components Strength Fatigue of AM components Relating AM process conditions to dynamic

performance of AM parts

3D printing is revolutionizing the manufacturing world Classical design constraints disappear Unprecedented shapes become possible But performance of the resulting products inot understood Nor the effects of manufacturing.... CAE is critical success factor for quality products

A disruptive technology


Addressing New Challenges in ManufacturingDeploying robotics & virtual commissioning

Digital Twin of SIMATIC S7-

1500


Overview





Where to go…6



Test data adding value to SimulationAircraft Model Validation in the critical path

CertificationFirst Flight

Virtual Ground Vibration Test

1 Physical prototype available Ground Vibration Test (GVT)

2 EngineeringInsights

3

Ground Vibration Testing Campaign

Pre-Test& De-Risking

Prepare Instrumentation

Instrument AircraftValidate Set-up

GVTIdentify Modes

& Validate Modes

Correlate GO/NOGOFirst Flight

UpdateRefineModels

Feasibility Concept Definition Development Test & validation In service

GVT


Test data adding value to SimulationAircraft GVT Model Validation and Updating: A330 MRRT Case

Bart Peeters et al, Modern solutions for Ground Vibration Testing of large aircraft. In Proceedings of IMAC 26, the International Modal Analysis Conference, Orlando (FL), USA, 4-7 Febr. 2008.

Aircraft GVT based model validation and updating is in the critical path of the Aircraft development process

https://www.youtube.com/watch?v=egDWh7jnNic

https://www.youtube.com/watch?v=egDWh7jnNic


Test data adding value to SimulationLoad Identification and Response Prediction

Critical to performance prediction Retrieve operational loading:

Force sensors Dedicated test benches Indirect: Operational response + FRFs

Calculate forced response Match response with targets


Connecting the Twins – Model Based System TestingCreating a unified testing framework from virtual testing to field testing

INTE

RFAC

E

Model Based System Testing IN

TERF

ACE

Virtual testing

Fieldtesting

Virtual testing

Conventionalbenchtesting

Conventionalbenchtesting

Enable attribute-specific evaluation throughout the development cycle, using virtual models, virtual-physical models and physical prototypes)

• same metrics and post-processing tools from 100% simulated to 100% real

• model-in-the-loop enables system-level testing for real components/subsystems

• common toolset and same user interface


Connecting the Twins – Model Based System TestingIntegration Validation of Components and Controllers - HiL Test Bench

CVT

Speed-controlled

IM

Speed-controlled

IM

Torque-controlled

IM

Invertors

Actuators

Vehiclemodel

Componentundertest

Sensors

Testing components in view of vehicle integration using real-time vehicle models

Active load

Running real-time models

To test CVT, controllers…


Connecting the Twins – Model Based System TestingBringing the Human in the Loop

Experience the Digital Twin Feed human commands Create human experience Example: Research on

Designing Advanced Safety Systems Including technology from:

3D 1D TEST Environment

Building blocks for several human factor studies


Simulation adding value to TestingVirtual Testing – Optimal Test Definition – Virtual Sensing

Simulation brings Testing to a new level Define optimal measurement set-up Execute virtual tests:

Reduce test setup costs Validate results

Virtual sensing: Measure unmeasurable quantities Enables new application fields

Sensor data Model

FUSION

“Virtual” sensor data

+

Fx

Fy

Fz


Augmenting Test with Simulation – Virtual SensingVehicle driving behavior assessment

Wheel AnglesSpeed/PositionStrain/ Accel. …

Sensor data

Cornering ForcesAt Body

Connections

Virtual sensor

Measurement update∆ (Measured value - predicted )

Kalman Filter based on Simulation (Real-time)Input maneuver

Objective evaluation of driving performance by estimating body cornering forces at suspension to body connection points usingTest - 1D/3D modeling

Tire

sR

oad

Chassis/body

Susp

ensi

on


Overview





Where to go…6



Data mining and processing on large sets of automotive

measurement dataProviding statistical answers toengineering questions about

vehicle usage during driving conditions

Turning Data into KnoweldgeStatistics processing of on-road fuel economy, durability, vehicle usage, …

Generating Design insight Knowledge from Vehicle Usage Data

0,0%

5,0%

10,0%

15,0%

20,0%

25,0%

30,0%

35,0%

Central Europe China USA Central Europe China USA

B-Type C-Type

Relative Time v=0

Relative Time XSystemEngineOff var 1

Relative Time XSystemEngineOff var 2

On-road driving –fleet information

CAN

Sensor

…Data taken during realistic driving

conditions

Statistical processingon large data

quantities

Data Mining application delivered as services

http://www.udldigital.de/wp-content/uploads/2013/07/big_data.jpg

http://www.udldigital.de/wp-content/uploads/2013/07/big_data.jpg

http://news.truckstop.com/wp-content/uploads/2010/05/truck-driving-on-icy-road1.jpg

http://news.truckstop.com/wp-content/uploads/2010/05/truck-driving-on-icy-road1.jpg


Machine Learning

Standard Neural network

Recurrent Neural network

Basic idea For time series inputs

Supervised learning

For n-dimensional data

Convolutional Neural network


How could AI / Machine Learning support the Digital Twin?Some opportunities

Support the testing processData validation and correctionEasier, more robust and faster measurements, improved ease-of-use of the software,

Insight into the testing dataAdvanced post-processing to extract relevant informationIdentifying use and user profiles, capturing emotional response Anomaly detection, monitoring and diagnosis

Data-driven System ModellingBlack-box data-driven models for components for which CAE model not availableVirtual sensing

Models driving Machine LearningModel based detection and diagnosisModel based control


Machine Learning supporting the testing processAutomatic recognition of incorrect Road Load Data measurements

Train a machine learning algorithm to automatically recognize measurement errors As early as possible (already during the measurements) Various error types Invariance to vehicle speed, road roughness, … ? No tuning of parameters by user (no rule/threshold setting) If possible, also offer correction

wheel acceleration

Feature extraction

RMS max/min quantiles kurtosis pseudo damage …

Trained classifier

Decision tree Random Forest Neural Network Logistic regression SVM …

“There are spikes”

Training Dataset


Machine Learning providing insight in dataData Analytics for Customer Correlation

Trend: embedded sensors in vehicles, vehicle fleet becomes a driving IoTContains information on how the vehicle is “used” by the customers, which can be exploited to set relevant targets

Challenges: How to find the useful information in this data;

it may not be known a priori what will be the useful information (e.g. surprising outliers? surprising clusters?)

Data labeling is limited Measurement data quality is low

Example; Durability target setting


Machine Learning for Structural Health MonitoringExample Z24 bridge: structural damage identification

Resonance frequencies (over 1 year period) extracted from raw vibration dataFeatures sensitive to damage but also to temperature!

Winter period: structure stiffens

Bridge is gradually demolished

Investigation 1: time series prediction for residual generation Assume temperature sensor data available: input to predictive models Training using only Healthy observations

𝑓𝑓𝑛𝑛

Results for Random Forest

Investigation 2: Binary Classification (healthy vs. damaged) Assume no temperature data available Training using both Healthy and Damage observations

Summary of results: investigated classifiers: Logistic Regression, SVM, k-NN, RF classification accuracy above 90 % for most methods:

extracted eigenfrequencies seem to be good features

gap increases as damage is induced

Predicted vs. Actual(only healthy data - training)

Predicted vs. Actual(last part of data -test)


Machine Learning for fault detectionShaft Dynamics - Imbalances

• Shafts are the most common and most efficient mode of power transportation for rotating machinery.

• Goal: Train machine learning method using simulations to determine location and magnitude of imbalance. Large Drive (>10 MW) Generator

Measurement PlanesImbalance Planes

Deep recurrent networks offer a direct solution:• Input: raw time-series data (4 signals, 2 shaft rotation)• Output: predicted equivalent imbalance at each plane.• Features are automatically discovered during the end-

to-end training.• Research is ongoing - initial results show highly

accurate prediction of imbalance magnitude, and moderately accurate imbalance phase prediction.

60x4

…

60x10x4

RNN


Machine Learning for fault detectionHigh Frequency Bearing Model

Three DOF bearing model resulting in an inhomogeneous system of 3 ODE’s. Sassi et al. (2007)

LMS Amesim bearing model an outer race defect as a force impulse.

High-frequency resolution simulations synthesize long time signals

Synchronous averaging in the angle domain


Machine Learning for fault detectionHigh Frequency Bearing Model: Feature Based Learning

Goal: characterize signal with statistical measures capturing key differentiators.

𝑓𝑓1𝑓𝑓2𝑓𝑓3…𝑓𝑓𝑁𝑁

Skewness

Kurtosis

…WaveletEnergy

Logistic Regression

Random Forest

…MLP Neural

Network

Features used: skewness, kurtosis (ASA and spectral), crest factor, margin factor, wavelet decomposition energies.

Algorithms used: logistic regression, random forests, k-nearest neighbors, SVM-RBF, and neural networks.


Machine Learning for fault detectionHigh Frequency Bearing Model- CNN

15x10 filters

filter50%

dropout50 dense

units50%

dropout

Nominal

Faulty

Convolutional Neural Network (CNN)

• CNNs learn filters that exploit the spatial morphology of the data

• Applying this method to an angle-synchronous average is more logical than the power spectrum because of the spatial sensitivity of the power spectrum.

• A small, portable network is sufficient to reach high accuracy because of the significant preprocessing.

Flatten

128 samples


Machine Learning for fault detectionEvaluation on experimental data

The best statistical classifier (random forest) performs the best when in-service data for the same machine is available; otherwise, a CNN or NNDTW is preferable.

Simulation-driven machine learning methodology.


Machine Learning for fault detectionWind Turbine Inner Race Fault

00.20.40.60.8

1

05101520253035404550Days Until Failure

00.20.40.60.8

1

05101520253035404550Days Until Failure

RF CNN NNDTW

Pred

icte

d Fa

ult S

tate

Inne

r Rac

eO

uter

Rac

e

C. Sobie, C. Freitas, and M. Nicolai (2017) Simulation-driven machine learning: Bearing fault classification. Accepted to Mechanical Systems and Signal Processing.


Machine Learning for Virtual Sensing Improved workflow with Machine Learning

Simplified workflow

• Model reduction done automatically using Principal Component Analysis (PCA) and whitening

• LPV model replaced by a Recurrent Neural Network state-space model

Benefits

• Improved overall accuracy (loss of accuracy ÷2)• Total memory dropped from 100 Mb to less than 100 kB• Fast training from simulation results (~5 min)• Automatic model reduction thanks to PCA (here 20 states

+ 5 inputs reduced to 5 inputs for NN model)

States & control input data

𝒇𝒇,𝒉𝒉 data and Jacobian 𝑨𝑨,𝑩𝑩,𝑪𝑪,𝑫𝑫

data

Training/FittingNeural network

model simulation (NEDC, WLTC etc.)

Performance Evaluation

New workflow with Neural Network State-Space Model

Change settings (number of neurons, hidden layers etc.)

PCA (with whitening)

EKF synthesis


Virtual Sensing tool chainCombustion engine EGR ratio estimation


Towards automated control developmentUsage of Model Predictive control

MPC ControllerInternal model

Objective function

Constraints

Optimal

control solver

This methodology creates a controller automatically and reducesthe cumbersome calibration on the bench to a strict minumum


SimcenterTM: Simulation and Test SolutionsConnected Assets help “Digital Twin” driven Engineering

Ref.: Demonstrated at Hannover Messe, 2016


Overview





Where to go…6



A Brief Outlook

The Digital Twin is a cornerstone for product and production engineering, realization and use. Challenges are Model complexity -> smart calculation paradigms Multiphysics, multiscale model of complex systems Interoperability of tools, data, models

Sensing and Measuring Everywhere Ubiquitous & embedded sensors Internet of Things, Internet of Everything Create value in digital Twin context

Deep Learning knows a major revival and gets to maturity; Plethora of techniques with specific application strengths Data Driven models versus First Principles models Explore value creation in Digital Twin context

δ 1

x2

x1

hid1 hid2

δ 2

x2

grou

nd

m 1c 1

k 1

f1(t)

m 2 m n grou

nd

k n+1k 2

c 2 c n+1

f 2(t) f n(t)

x1(t) x2(t) xn(t)


Thank you for your attention

Questions ?Herman Van der Auweraer

Siemens Industry Software NVSiemens DF PL STS

E-mail: [email protected]

Realize innovation.

connecting physics based and data driven models: … · changing role of testing in the design and...

Documents