Development of Data Analytics in Shipping

Modern vessels are monitored by Onboard Internet of Things (IoT), sensors and data acquisition (DAQ), to observe ship performance and navigation conditions. Such IoT may create various shipping industrial challenges under large-scale data handling situations. These large-scale data handling issues are often categorized as “Big Data” challenges and this chapter discusses various solutions to overcome such challenges. That consists of a data-handling framework with various data analytics under onboard IoT. The basis for such data analytics is under data driven models presented and developed with engine‐propeller combinator diagrams of vessels. The respective results on data analytics of data classification, sensor faults detection, data compression and expansion, integrity verification and regression, and visualization and decision support, are presented along the proposed data handling framework of a selected vessel. Finally, the results are useful for energy efficiency and system reliability applications of shipping discussed.

[1]  Lokukaluge P. Perera,et al.  Marine Engine Operating Regions under Principal Component Analysis to evaluate Ship Performance and Navigation Behavior , 2016 .

[2]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[3]  Lokukaluge P. Perera,et al.  Evaluations on Ship Performance Under Varying Operational Conditions , 2015 .

[4]  Lokukaluge P. Perera,et al.  Statistical Filter based Sensor and DAQ Fault Detection for Onboard Ship Performance and Navigation Monitoring Systems , 2016 .

[5]  Nilanjan Dey,et al.  A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset , 2016, Comput. Methods Programs Biomed..

[6]  Shiwen Mao,et al.  CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.

[7]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[8]  Sofia Werner,et al.  A verification of the ITTC/ISO speed/power trials analysis , 2015 .

[9]  J. Edward Jackson,et al.  Principal Components and Factor Analysis: Part I - Principal Components , 1980 .

[10]  Lokukaluge P. Perera,et al.  Weather routing and safe ship handling in the future of shipping , 2017 .

[11]  Lokukaluge P. Perera,et al.  Marine Engine Centered Localized Models for Sensor Fault Detection under Ship Performance Monitoring , 2016 .

[12]  Lokukaluge P. Perera,et al.  Marine Engine-Centered Data Analytics for Ship Performance Monitoring , 2017 .

[13]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[14]  Lokukaluge P. Perera,et al.  Data Compression of Ship Performance and Navigation Information Under Deep Learning , 2016 .

[15]  Lokukaluge P. Perera,et al.  Data Analytics for Capturing Marine Engine Operating Regions for Ship Performance Monitoring , 2016 .

[16]  T. Moon The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..

[17]  Chintan Bhatt,et al.  Internet of Things in HealthCare , 2017 .