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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