1. Vesa Klumpp, Knowtion 2017-11-24
Applications of Intelligent Control in Industry and Adaption to Space Missions
Vesa Klumpp, Knowtion

2. About Knowtion
Founded in April 2011 Close contact to Karlsruhe Institute of Technology Customer-specific software and algorithm development Customer-oriented problem-solving approach with scientific background Highly qualified engineers and computer scientists with special knowledge in sensor fusion and data analysis
Karlsruhe

3. Core Competencies Sensor Fusion Automatic Data Analysis
Combination of different sensor data and information sources
Automatic Data Analysis
Realtime surveillance of sensor data and detection of anomalies

4. Extracted information
Core Competencies
Raw sensor data
Model / Algorithms
Information
Temperature
Pressure
Acceleration
Position …
Sensor Fusion (Bayes)
Classification
Machine Learning
Deep Learning Approaches …
Reduced systematic error
Filtered
Plausible
Data reduction …
Plausible data
Filtered data
Accuracy estimate
Extracted information 4

5. Air Traffic Control 1/3 Surveillance data processing system (tracker)
Wanted
Current air situation picture (position and velocity of each aircraft)
Given
Disturbed measurements of multiple radar stations
Different types of sensor technologies (PSR, SSR, ADS-B, MLAT)

6. Air Traffic Control 2/3 Solution Result Specialized algorithms for
Data association
Track update
Result
Plausible tracks with high accuracy
Simultaneous monitoring of sensors

7. Air Traffic Control - other projects 3/3
Medium term confliction detection
Warn the controller of potential conflict between flights in his area of responsibility
Departure and arrival flow management
Optimization of departure and arrival sequences under several time and spatial constraints
Proposal of actions for air traffic controllers

8. Hot Water Tank Wanted Solution Temperature and energy profile
Detection of warm water draw off
Supply of hot water on demand
Solution
Algorithm for temperature profile reconstruction and energy estimation
Learning of user behavior over time to provide heat in advance and reduce energy consumption
Limited main memory: 5 kB

9. Extrusion Foaming Process
Production of construction material for thermal insulation (Extruded polysterene foam, XPS)
Health monitoring of production process
Prediction of final production quality
Optimization and control of extruder and machine parameters
Control Algorithms
Prediction of quality of final product
Process and
machine data
Sensor
data
Extruder
Intermediate
Product
Final Product

10. Flight Passenger Number Prediction
Wanted
Prediction of PAX for each flight within the next 2 months
Given
Historic passenger data of the last years
Solution
Application of of multiple machine learning algorithms (Ensemble)
Achieved performace similar to human expert

11. Anominer - Overview
Automatic monitoring of data for changes, outliers, and anomalies
Learning and analysis of process data in real-time
Machine learning and deep learning algorithms
For automated inspection of process data, customer-specific adaption possible
Outlier detection
Detection of anomalies

12. Anominer - Filling Plant
Monitoring of a bottle filling process for detection of leakages and errors
1st step “Learning”: Anominer accepts input data streams and learns normal behavior of data in real-time
2nd step “Evaluation”: Anominer monitors input data streams for changes
Result is an anomaly indicator that describes the deviation of the learned normal behavior
Fast response: Result is returned in real-time

13. Anominer – DC Motor Monitoring of the health of a DC motor
Change of input data is very subtle when defect is present (e.g. change in friction, inductivity, or electrical resistance of rotor)
Same setup and configuration of Anominer as in filling plant use case
Anominer can also be employed to detect very subtle changes in input data

14. High Altitude Test and Validation Service employing SpaceFish Vehicle
Successor of GSTP 4 activity “Integrated Vehicle Health Management System Demonstrator (iHMSD)”
High-altitude re-entry demonstrator space vehicle
Glider is equipped with payload, e. g. measurement equipment
Glider is dropped from balloon and shall return to specific location autonomously
Challenges:
Unknown payload changes flight behavior
Intelligent monitoring of system

15. Intelligent Flight Control Algorithm
General Requirements:
Online / Real-time learning and evaluation
Relatively low processing power available
Low communication bandwidth and high latency to base station
 Offloading of processing to base station probably not feasible

16. Intelligent Flight Control Algorithm
Sensors measure current flight path
Model predicts behavior of glider
Based on equation of motion or learned
Current flight path is evaluated/scored
Smoothness, deviation from planned trajectory, Intensity of control
On-line learning of flight characteristics
Adaption of control strategy

17. Approaches for Complexity Reduction
Pre-trained models
Offline learning from recordings and online update
Reduced state space for lower computational complexity
Lower-dimensional parameterization of model
Conditional models
Decomposition of problem
Use set of motion equations for different flight maneuvers or learn different parts of trajectory
Learning is re-weighting of single models

18. System/Health Monitoring
Sensor faults
On-line estimation of calibration
Flight dynamics, mechanical structure, vibrations
Find specific signals or features in sensor data
Controller performance
Evaluate settling time, extent of control variables
Decreased performance may indicate problem

19. System/Health Monitoring
Detection of deviations from normal behavior
Employ pre-trained model or learn online during flight
On-line evaluation
Potential Processing Pipeline
Onboard pre-processing and data reduction
Detection of faults with machine learning algorithms in base station