1st Workshop on Digitalization and Blockchain – Managerial and Organizational Implications University of South-Eastern Norway, Campus Vestfold.

1. Data Driven Industrial Digitalization
through Reverse Engineering of Systems
Lokukaluge Prasad Perera
September 12th, 2019
1st Workshop on Digitalization and Blockchain – Managerial and Organizational Implications
University of South-Eastern Norway, Campus Vestfold.

2. Outline
• Industrial Digitalization.
• Data Analytics.
• From Newton's Law to Deep Neural Networks
: Autoencoders.
• Shipping Industrial Example.
• Conclusions.

3. Industrial Digitalization in Shipping
" Poor data quality costs…
the US economy around US$ 3.1 trillions per year"*
"1 in 3 Business Leaders don't trust…
the information they use to make decisions "*
"27% of the respondents in a survey…
were unsure of how much of their data sets are inaccurate"*
*IBM, The four V's of Big Data, URL:http://www.ibmbigdatahub.com/infographic/four-vs-big-data, 2017.

4. Why Data Analytics ..?
• Conventional Models
– Various Conventional Models in the shipping industry for various applications.
– Some challenges in handling Big Data : erroneous data conditions, system-model
uncertainty, estimation algorithm failures, data visualization challenges and high
computational power.
• Machine Learning & Statistical Analysis
– Machine Learning (ML) & Statistical Analysis will play an important role in
analyzing such data sets.
– Statistical Analysis will guide ML Algorithms.
– Towards Data Driven Models: Digital Models.
– It is Geometrical…
• Domain Knowledge
– Ship Dynamics & Hydrodynamics.
– Automation & Navigation Systems.

5. Data Analytics Types
– Digital model consists of data driven relationships.
– Descriptive analytics identifies various data anomalies.
– Diagnostic analytics recovers/removes such data anomalies.
– Predictive analytics forecasts vessel and ship system behavior.
– Visual analytics visualizes the same information.
– The information creates Advanced Knowledge and that will lead to Industrial Intelligence.
– Both advanced knowledge and industrial intelligence support Decision Analytics.
– Decision analytics may consist of appropriate Key Performance Indicators (i.e. KPIs)

6. Newton's Laws of Motion
– "A body at rest will remain at rest, and a body
in motion will remain in motion unless it is
acted upon by an external force."
– "The force acting on an object is equal to the
mass of that object times its acceleration.“
F = ma
where F is force, m is mass, and a is acceleration.
– "For every action, there is an equal and
opposite reaction."
Source: https://www.livescience.com

7. Newton's Laws of Motion
F = ma
F is force,
m is mass
a is acceleration.

8. Sensor Measurements
F = ma
F is force,
m is mass
a is acceleration.

9. Singular Values and Vectors
F = ma => Z1 and Z2 => sensor noise
F is force,
m is mass
a is acceleration.
Z1,2 are singular vectors

10. Data Projection
F = ma => Z1
F is force,
m is mass
a is acceleration.
Z1,2 are singular vectors
Least Squares Fitting

11. Reinvention of Newton's Laws of Motion
F = ma
F is force,
m is mass
a is acceleration.
Z1,2 are singular vectors

12. Reverse Engineering of Systems
F = ma
F is force,
m is mass
a is acceleration
Z1,2 are singular vectors

13. Deep Neural Networks: Autoencoders
F = ma
F is force,
m is mass
a is acceleration
F
a
Z 1,2
R

14. Advanced System Models
F = ma + Fm
where
F is force,
Fm is mean force,
m is mass,
a is acceleration

15. Digital Models in High Dimensions

16. Singular Values & Vectors
– The structure of each data cluster is denoted by several vectors: singular vectors
(i.e. associated with the respective singular values)
– Singular values and vectors represent the building blocks of electrical and
mechanical systems
– System behavior, i.e. system information, can be accommodated into the same.

17. Digital Models
– Three-dimensional vector space with the right-hand coordinate system.
– Three data clusters, i.e. system states, with the respective mean vectors.
– Each data cluster consists of local operational information of the respective
system.
– The structure of each data cluster is denoted by several vectors: singular vectors
(i.e. associated with the respective singular values).
– Each singular vector consists of local operational information of the respective
systems.
– Each cluster is a linear model, i.e. Piecewise linearization: the best approximation
of a nonlinear function as a piecewise linear function.
– The system can jump from one state to another state in a high dimensional data
space.
– Some data clusters may relate to data anomalies or system abnormal events.

18. Model Complexity
– From Components to System of Systems
– Various Model Levels
– High dimensional Digital Models
– From Big Data to Low Level Models

19. Ship Performance & Navigation Data Set
Parameter Mini. Max.
1. Avg. draft (m) 0 15
2. STW (Knots)- Speed through water 3 20
3. Engine power (kW) 1000 8000
4. Shaft speed (rpm) 20 120
5. Engine fuel cons. (Tons/day) 1 40
6. SOG (Knots) – Speed over ground 0 20
7. Trim (m) -2 6
8. Rel. wind speed (m/s) 0 25
9. Rel. wind direction (deg) 2 360
10. Aux. engine fuel cons. (Tons/day) 0 8

20. Ship Engine Data
in Histograms
– The vessel is a bulk carrier with ship
length: 225 (m) and beam: 32.29 (m)
– Three parameters: engine speed,
power and fuel consumption
– Engine data are clustered around
three Gaussian type distributions
– Three engine modes of this vessel
– Ship performance and navigation
data sets are often clustered in a
high dimensional space
– Those clusters relate to vessel
navigation and ship system
operational conditions

21. Ship Engine Data
– Two parameters: engine speed and
power.
– Combined kernel density estimation
(multivariate KDE) with the
respective univariate KDEs.
– Engine data are clustered around
three Gaussian type distributions.
– Three engine modes of this vessel.
– Ship performance and navigation
data sets are often clustered in a
high dimensional space.
– That introduce the discreteness (i.e.
digital-ness) into the proposed
models.

22. Digital Models
– Each cluster is a linear model, i.e. Piecewise linearization: the best
approximation of a nonlinear function as a piecewise linear function.
– The vessel & ship system can jump from one state to another state in a high
dimensional data space.
– Some data clusters may relate to data anomalies or system abnormal events.

23. – Digital models interact with the descriptive and diagnostic analytics to improve the data quality
– Data anomaly filter 1: missing data points and preliminary data anomalies (i.e. Min-Max values)
detected
– Data anomaly filter 2: Additional data anomalies (i.e. the outliers of digital models) detected
– Data anomalies send to separate groups where the data anomalies against known and unknown
sensor and DAQ faults and system abnormalities compared
– Data sets from anomaly group 1 and 2 transfer through the data recovery filter and digital models
– A considerable amount of data anomalies can be recovered by this step
Data Anomaly Detection and Recovery
Procedure

24. Data Anomalies in Newton's Laws of Motion
F = ma
F is force,
m is mass
a is acceleration.
Z1,2 are singular vectors

25. Data Anomaly Detection and
Recovery Procedure: Filter 2
– Digital models interact with the descriptive and diagnostic analytics to improve the data quality
– Data anomaly filter 1: missing data points and preliminary data anomalies (i.e. Min-Max values)
detected
– Data anomaly filter 2: Additional data anomalies (i.e. the outliers of digital models) detected
– Data anomalies send to separate groups where the data anomalies against known and unknown
sensor and DAQ faults and system abnormalities compared
– Data sets from anomaly group 1 and 2 transfer through the data recovery filter and digital models
– A considerable amount of data anomalies can be recovered by this step

26. Data Anomaly Detection and
Recovery Procedure: Filter 2
– Digital models interact with the descriptive and diagnostic analytics to improve the data quality
– Data anomaly filter 1: missing data points and preliminary data anomalies (i.e. Min-Max values)
detected
– Data anomaly filter 2: Additional data anomalies (i.e. the outliers of digital models) detected
– Data anomalies send to separate groups where the data anomalies against known and unknown
sensor and DAQ faults and system abnormalities compared
– Data sets from anomaly group 1 and 2 transfer through the data recovery filter and digital models
– A considerable amount of data anomalies can be recovered by this step

27. Data Anomaly Detection and
Recovery Procedure: Data Recovery
– Digital models interact with the descriptive and diagnostic analytics to improve the data quality
– Data anomaly filter 1: missing data points and preliminary data anomalies (i.e. Min-Max values)
detected
– Data anomaly filter 2: Additional data anomalies (i.e. the outliers of digital models) detected
– Data anomalies send to separate groups where the data anomalies against known and unknown
sensor and DAQ faults and system abnormalities compared
– Data sets from anomaly group 1 and 2 transfer through the data recovery filter and digital
models
– A considerable amount of data anomalies can be recovered by this step

28. Data Anomaly Detection &
Recovery Procedure: Filter 2

29. Data Visualization of Newton's Laws of Motion

30. Visual Analytics
– Digital models should be visualized to extract relevant parameter
relationships.
– Covariance values of the data sets are represented by singular vectors.
– Each vector will present correlation information among the respective
parameters.

31. Visual Analytics
– Digital models should be visualized to extract relevant parameter
relationships in a High Dimensional Space.
– Covariance values of the data sets are represented by singular vectors.
– Each vector will present correlation information among the respective
parameters.

32. Visual Analytics
– Digital models should be visualized to extract relevant parameter relationships
in a High Dimensional Space.
– Covariance values of the data sets are represented by singular vectors.
– Each vector will present correlation information among the respective
parameters.
– The top singular vector is presented in the outer circle.
– The bottom singular vector is presented in the inner circle.

33. Visual Analytics

34. Visual Analytics

35. Predictive Analytics
– The outputs of the predictive analytics are predicted vessel and ship
system behavior.
– The information creates advanced knowledge and facilitates towards
industrial intelligence

36. Deep Learning
Source: https://www.edureka.co/blog/what-is-deep-learning

37. Data Handling Framework

38. Conclusions
– Novel mathematical framework to support industrial digitization of shipping
is presented: i.e. from Industrial IoT to Predictive Analytics.
– Data analytics can…
• self-learn (i.e. the data structure can learn itself)
• self-clean (i.e. data anomalies can be detected, isolated and recovered by considering the
outliers of the data structure),
• self-compress and expend (i.e. the respective parameters in the data sets can be reduced
and expanded by considering the same data structure)
• self-visualize (i.e. the respective data structures can be used for both vessel and ship
system performance observations)
– That introduces Intelligent Analytics to any industry and also provides
important solutions to the big data challenges under the Industrial
Digitalization.

39. Any Questions?