Ali Mousavi -- Event modeling
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Transcript of Ali Mousavi -- Event modeling
Event-Modelling An Engineering Solution for Control and Analysis of Complex Systems
(SERG Laboratory) Brunel University United Kingdom
www.brunel.ac.uk/~emstaam
1 Ali Mousavi (SERG)
Product Engineering, Testing & Validation Process
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Existing Methods
• Polynomials (Classical DoE): – Classical Linear models, – Mathematically well defined and expressed – Exposed to measurement anomalies – Limited in expressing complex conditions
• Neural Networks: – More complex systems can be described – Larger sets of parameters (known Parameters by experts) can be included – Also automatic recognition features (help from Anatoly) – Mathematically beautiful, but implementation and understanding of the solution itself is a
challenge – This leads to difficulty to apply calibration – Regular operator and expert interference – over-fitting problem! – Not suitable for real-time applications
• Machine Learning – Captures complexity very well – Little need for model calibration and parameterisation – Computationally heavy, rendering it difficult in real-time application – Reliance on statistical analysis throughout (some may consider it strength – some weakness)
A new book that I am reading in the subject area: http://www.deeplearningbook.org/ (MIT Press)
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Deals with Known -
Parameters
But finds patterns
But What about…
• The Unknowns – The things that we have not noticed but beam data and information to us. [some NN and Machine learning deal with it but is it effective/efficient]
• Do they impact my system?
• Is the model sufficient to describe internal and external changes that occur?
• How do I make the system works to specifications when it is in the field?
• Can I change and adapt the system as its internal and external environment change/evolve?
• Converging with Learning Machine and NN – may be an effective way to
accelerate learning… becoming a member of the family
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EventTracking and Clustering
If we have the ability to capture all the data possible with the sphere of the problem: • Is it possible to explore their impact on the state of the system one
by one and group by group?
• Is it rationally and computationally possible/feasible?
• What will we achieve by it?
• Will the results be in good time to help?
• Can I validate and verify them quickly?
• Can I put it into any good use?
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Typical Systems Modelling
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The aim is to reduce the project cycle time by 50% using the Event-Based modelling
Step 1 Event Tracking
• Real-Time many-to-one correlation analysis in real time.
• Provides a good indication of the impact of various events on performance indicators.
• Verifies the known relations and finds new unknown relations
𝑦 =
𝑎1
𝑎2
…𝑎𝑛
𝑥1
𝑥2
…𝑥𝑛
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Step 2 Event Clustering – The Scenario Builder
• Event Clustering, coincidence matrix of events at given times
• Identifies the group of input and output events that coincide
𝑂1 𝑂2 𝑂𝑛 𝑂1 𝑂5 𝑂𝑚 𝑂3 𝑂7 𝑂𝑜
𝑖1 𝑖2
𝑖𝑛
𝑖4 𝑖5
𝑖𝑚
𝑖9 𝑖11
𝑖𝑜
1 1 1 1 1 1
1 1 1 1 1 1 1 1
1 1 1 1 1 1 1
When these groups of outputs Change these group of inputs Change as well
T= the time that this Combination has occurred
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Step 3 Scenarios identification
Scenario 1 Scenario 2 … Scenario 4 Scenario 3
T
S
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Step 3 State Convergence Scenario 1 Scenario 2
…
Scenario n
• Compare the matrices of state and combine similar scenarios (distance analysis) • Euclidean distance is a good starter • Number of States is less than or equal number of scenarios
State 1 State 2
…
State n
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Step 4 Look up table for system setting
• Go back to the actual values of the parameters
• Find cases of interest (e.g. best system condition, worse condition, risk, bottleneck, stability, hazard, …)
• Create a lookup table.
Condition Representing Bottleneck
relevant output and inputs, with values and weights
𝑦1
𝑦2
…𝑦𝑛
=
𝑥1
𝑥2
…𝑥𝑛
𝑤1
𝑤2
…𝑤𝑛
The importance/sensitivity
Y
X
Look up table System setting 150
0.76 6.92
1 0.02 1720
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Step 5 Validation and verification
• Setting up experiments on the actual system or the simulator.
• Validate if the they are true solutions, following scenarios my apply: – Instantaneous solution (by setting the controllable parameters the expected
results are achieved) – solution with no delay (e.g. changing the angle of a gate to control flow)
– The settings are implemented but it takes a fixed amount of time to reach solution - solution with delay (e.g. starting the heating but the material reaches the ideal temperature in t time)
– Multiple occurrence – a specific state/scenario needs to be repeated several times for an output to be reached - repetition of a setting for a finite number of time (e.g. failures or breakdown)
– Conditional Process – a number of various setting aligned together in a sequence – Process-based solution, scenario 1 to n should align in a sequence for an output to be reached (e.g. completion of an assembly) – Markovian chain
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Output Event Related Input Event (RIE)
𝑡 ≈ 0 Instantaneous Deterministic Event (Current Event Tracker Model)
𝑡𝑥 𝑡𝑛 𝑡𝑛−1 …
Fixed Time Delayed Deterministic Event – Output event occurs after a fixed time when The relevant input occurs. This can also help us optimise scan rates.
Input (𝑡𝑥)𝑇=𝑡
𝑂𝑢𝑡𝑝𝑢𝑡(𝑥) Non related Input Events
𝑌
search
𝑡𝑥 𝑡𝑛 𝑡𝑛−1 …
Deterministic singular input multiple occurrence delay. In this case the same input event should repeat itself a number of times until the output event occurs.
𝑛 × 𝐼𝑛𝑝𝑢𝑡(𝑥)𝑇=𝑡
𝑂𝑢𝑡𝑝𝑢𝑡(𝑥)
𝑡𝑥 𝑡𝑛 𝑡𝑛−1 …
Deterministic sequence of different input events causing an output event (Deterministic Process). In this case a number of specific input event series results in a specific output. Conditional Chain
𝐼𝑛𝑝𝑢𝑡 𝑥 ˄ 𝐼𝑛𝑝𝑢𝑡(𝑦) ˄ 𝐼𝑛𝑝𝑢𝑡(𝑧) ˄ … 𝑇=𝑡
𝑂𝑢𝑡𝑝𝑢𝑡(𝑥)
𝑡𝑥
𝑡𝑛 𝑡𝑛−1
…
Search in various scenarios for common input event and find the alternative pathways, akin to a tree Petri-Net, Monte-Carlo Tree, …. In this case a sequence of alternative event in a pathway which proceed a common prior event to achieve the final output. The conditional probability of event x occurring at time 𝑡𝑥 and possible alternative pathways with their consecutive input events to reach Output Y at 𝑡𝑛 . This could be used for predictive or probabilistic pathways to output events
𝑌
𝑌
𝑌
𝑌
𝑡𝑛: time the Output Occurs
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Comparisons between other known techniques
• We are yet to stabilise and understand what we are trying to do – with limited resources, challenging well established techniques requires time and collaboration with colleagues
• Neural Network – It is underway in a project with JEV Power Plant in Malaysia to explore the optimisation of Harmonic Filters… we will soon release the outcome.
• Entropy Based Sensitivity Analysis – small comparisons through a PG dissertation.
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EventTracker Verses Entropy Based Sensitivity Analysis
Flight Control
• Tera bytes of aircraft flight Data
• EventTracker algorithm
Implementation
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Data Queuing System
Trigger / Event Detection
Two way Matching Score
Sensitivity Indexes
SummationNormalisation
Sensitivity Analysis Array
Generation
Search Slot
Analysis Span
Data Considered
Input Data Output Data
Aircraft velocity down (veld_gin) Altitude (alt_gin)
Aircraft velocity east (vele_gin) Pitch angle (ptch_gin)
Aircraft velocity north (veln_gin) Roll angle (roll_gin)
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These data were considered due to their the apparent relationship between the aircraft velocity and the altitude, pitch angle and roll angle. (simplified for proof of concept)
Why Entropy
• We had worked on the theory before. • Krzykacz-Hausmann1 uses Shannon’s Theory2 of communication to
measure uncertainty by. • The entropy is defined as the measurement of loss of information in a
system3 . • In the particular case of SIL and HIL, Entropy Based Method can be used as
a mechanism for Sensitivity Analysis (Variable Selection) to compare the expected entropy and the actual and possibly interpreted as (sensitivity index).
• An important characteristic about the Entropy Based Method is that it is computationally efficient and may be deployed in Real-Time Applications.
• As a Sampling based methodology, it requires a sampling generation to decide a verifiable number of observations.
• Krzykacz-Hausmann equations for the Sensitivity Analysis were used in this example
1. B. Krzykacz-Hausmann, "Epistemic sensitivity analysis based on the concept of entropy," Proceedings of SAMO, pp. 31-35, 2001. 2. C. E. Shannon, "A Mathematical Theory of Communication," The Bell System Technical Journal, vol. 27, pp. 379–423, 623–656, 1948. 3. B. A. &. B. Iooss, "Global sensitivity analysis based on entropy," Safety, Reliability and Risk Analysis: Theory, Methods and Applications, pp. 2107-2115, 2009.
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Comparison at 90msec intervals
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ptch_gin alt_gin roll_gin
0.19
0.623 0.565
0.347
0.712
0.17
0.347
0.569 0.672
EVENT TRACKER SENSITIVITY INDEX
veld_gin vele_gin veln_gin
ptch_gin alt_gin roll_gin
0.015
6.773
4.91
0.024
7.1726
0.007 0.0155
7.89
5.26
ENTROPY-BASED SENSITIVITY INDEX
veld_gin vele_gin veln_gin
Range 0-100 Range 0-10
Area of interest… Quite similar results…
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Performance indicator
(Sampling time=500ms)
Value
Number of slots 26
Execution time 253 seconds
Memory usage 317.10 KB
Performance indicator
(Sampling time=90ms)
Value
Number of slots 15
Execution time 47 seconds
Memory usage 317.19 KB
Table : Event-tracker performance for 90ms sampling time
Table: EventTracker performance for 500ms sampling time
Performance indicator
(Sampling time=90ms)
Value
Number of samples 1000
Execution time 101 seconds
Memory usage 321.58 KB
Performance indicator
(Sampling time=500ms)
Value
Number of samples 1000
Execution time 450 seconds
Memory usage 321.58 KB
Table: Entropy-based results for 500ms sampling time
Table: Entropy-based results for 90ms sampling
Longer interval a burst of execution
Shorter Interval Significant
reduction in execution time
EventTracker Verses Entropy
• Reaching similar results
Time could be much more critical factor than memory
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0
50
100
150
200
250
300
350
Event-tracker Entropy-based
Algorithm Performance
Time consumption (ms) Memory usage (KB)
Performance Comparison Time and Memory
Various applications
• Systems Modelling and Simulation of product Design
• Aviation Industry
• Manufacturing and Process Engineering
• Image Processing
• Automotive Industry
• Energy and Environmental Studies
• Automation
• Big Data Analytics
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Current Grants
1. Zero Defect Manufacturing €6.2M EU-H2020- FoF – first major investment on the approach – for Adaptive Control of Production Machines and Robots – Commencing November 2016.
2. Previously QMU applied the technique in Deep Drilling EU.
3. Recent Proposals submitted:
– Fast Screening and Scanning System for Airport Security (with Russia and UK) – Department of Transport UK
– Optimisation of Dairy Industry operations from table to stable (Farm, Production, Storage, Distribution, Retail, and Customer. (EU)
– Environmental Monitoring and Risk analysis of Fracking Industry (EU)
– Integration of Patient processing hospital and home care (saving an estimate of £5B a year on patient care in the UK NHS – (UK)
4. Working Application:
– Efficient Product Engineering, Testing & Validation Process (EPET) in Aerospace industry (UK)
– Event-Based motion simulator for people with limb amputation (NATO)
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Some sample relevant references from SERG
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1. Tavakoli S. and Mousavi A. and Peter Broomhead (2013), “Event Tracking for Real-Time Unaware Sensitivity Analysis (EvenTracker)”, IEEE Trans. On Knowledge and Data Engineering, Vol:25, No. 2, pp. 348-359, DOI: 10.1109/TKDE.2011.240.
2. Tavakoli S. and Mousavi A. and Peter Broomhead (2013), “Event Tracking for Real-Time Unaware Sensitivity Analysis (EvenTracker)”, IEEE Trans. On Knowledge and Data Engineering, Vol:25, No. 2, pp. 348-359, DOI: 10.1109/TKDE.2011.240.
3. Mousavi A., and Siervo, H.A. (2016), Automatic Translation of Plant Data into Management Performance Metrics: A Case for Real-Time and Predictive Production Control, International Journal of Production Research – in print.
4. Danishvar, M. Mousavi, A. and Broomhead P. (2016), Modelling the Eco-System of Causality: The Real-Time Unaware Event-Data Clustering (EventiC), submitted to IEEE Trans Systems, Man and Cybernetics – in print.
5. Komashie A. and Mousavi, A., Clarkson, J., and Young T. (2016), A Robust Healthcare Quality Index (HQI) Based on the Generalized Maximum Entropy Formulation, submitted to European Journal of Operational Research – under review.
Appendix 1. Accelerometer 2. Acceleration along the aircraft vertical axis (acld_gin) 3. Acceleration along the aircraft longitudinal axis (aclf_gin) 4. Acceleration along the aircraft transverse axis (acls_gin) 5. Altitude (alt_gin) 6. Angle of attack from the turbulence probe (aoa) 7. Angle of sideslip from the turbulence probe (aoss) 8. Groundspeed (gspd_gin) 9. Heading (hdg_gin) 10. Rate of change of GIN heading (hdgr_gin) 11. Indicated air speed from the aircraft RVSM (air data) system (ias_rvsm) 12. Latitude (lat_gin) 13. Longitude (lon_gin) 14. Rate of change pitch angle (pitr_gin) 15. Static pressure from the aircraft RVSM (air data) system (ps_rvsm) 16. Pitch angle (ptch_gin) 17. Roll angle (roll_gin) 18. Rate of change of rall angle (rolr_gin) 19. True air speed from the aircraft RVSM (air data) system and deiced temperature (tas_rvsm) 20. Track angle (trck_gin) 21. Eastward wind component from turbulence probe and GIN (u_c) 22. Northward wind component from turbulence probe and GIN (v_c) 23. Aircraft velocity down (veld_gin) 24. Aircraft velocity east (vele_gin) 25. Aircraft velocity north (veln_gin) 26. Vertical wind component from turbulence probe and GIN (w_c) 27. Timestamp
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