A novel mathematical framework to support industrial digitization of shipping is presented in this study. The framework supports a data flow path, i.e. from Industrial IoT (i.e. with Big Data) to Predictive Analytics, where digital models with advanced data analytics are introduced. The digital models are derived from ship performance and navigation data sets and a combination of such models facilitates towards the proposed Predictive Analytics. Since the respective data sets are used to derive the Predictive Analytics, this mathematical framework is also categorized as a reverse engineering approach. Furthermore, a data anomaly detection and recover procedure that is associated with the same framework to improve the respective data quality are also described in this study.

1. Industrial IoT to Predictive Analytics:
A Reverse Engineering Approach from Shipping.
Lokukaluge Prasad Perera
SINTEF Ocean, Trondheim, Norway.
3rd Norwegian Big Data Symposium (NOBIDS) 2017,
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2. 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.

3. Ship Engine Data
• The vessel is a bulk carrier with ship length: 225
(m) and beam: 32.29 (m)
• 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
• Those clusters relate to vessel navigation and
ship system operational conditions
• That introduce the discreteness (i.e. digital-
ness) into the proposed models

4. Digital Models
• Three data clusters with the respective mean vectors
• Three-dimensional vector space with the right-hand coordinate system
• Each data cluster consists of local navigation and operational information of the vessel and ship systems
• The structure of each data cluster is denoted by several vectors: singular vectors (i.e. associated
with the respective singular values)
4
Data Structure

5. Advanced Data Analytics
• 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 consists of appropriate Key Performance Indicators (i.e. KPIs)

6. Data Anomaly Detection and Recovery Procedure
• 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

7. Predictive Analytics
• Digital models connect with several observers and that create the respective predictive analytics.
• Each model represents a local linear model of vessel navigation and ship system operation behavior.
• A global nonlinear model that represents the vessel and ship system behavior.
• The outputs of the predictive analytics are predicted vessel and ship system behavior.
• That behavior is converted to vessel navigation and ship system operation information by visual analytics.
• The information creates advanced knowledge and facilitates towards industrial intelligence
• Advanced knowledge develops appropriate decision analytics

8. Conclusions
• A novel mathematical framework to support industrial digitization of shipping is
presented: a data flow path, i.e. from Industrial IoT to Predictive Analytics.
• The proposed 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)
• Since this framework is developed from ship performance and navigation data sets,
this process can also be a reverse engineering approach of the vessel and ship
systems.
• That introduces Intelligent Analytics to the shipping industry and also provides
important solutions to the big data challenges under Industrial Digitalization.