Machine intelligence for energy efficient ships: A big data solution

Perera, Lokukaluge P.; Mo, Brage

doi:10.1201/b21890-21

Cited by 45 publications

(16 citation statements)

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“…There is a significant amount of data generated in navigation systems that consist of radar, electronic chart display and information system (ECDIS), auto-pilot system and other related sensors [22]. Moreover, special purpose vessels will require additional instrumentation relevant for their operations, such as wave radars, oil spill detectors and high accuracy inertial navigation sensors [24].…”

Section: Data Sources and Utilization Strategies / Izvori Podataka I mentioning

confidence: 99%

“…Perrera et al [22] propose the data flow chart as presented in Figure 3. The data are collected from various onboard sensors and data acquisition systems, preprocessed and transmitted to shore based data centers where they are stored and analyzed.…”

Section: Data Sources and Utilization Strategies / Izvori Podataka I mentioning

confidence: 99%

“…Ship performance and navigation information can be used to develop navigation strategies to improve ship energy efficiency [22]. Monitoring fuel consumption, various emissions, the use of lighting, heating and similar processes can result in insights that support decision-making.…”

Section: Energy Efficiency Improvement / Unapređenje Energetske Učinkmentioning

confidence: 99%

“…An overview of several machine learning techniques that can be used to detect anomalies from data gathered on vessel movement is presented in [20]. [22] In [4], an application of sensor-based anomaly detection in maritime transport is demonstrated. Sensor data, which include environmental and ship system information, are streamed from a ship to shore where they are analyzed through a Big Data analytics platform.…”

Section: Safety Improvement / Unapređenje Sigurnostimentioning

confidence: 99%

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Veliki skupovi podataka u pomorskoj industriji

Mirović¹,

Miličević²,

Obradovic³

2018

Naše more

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SummaryMaritime industry is a complex system that requires a quick adaptation to changing conditions and in which decision-making needs to take into account a large number of parameters. As navigation systems become more advanced, there is a significant amount of ship performance and navigation data generated. Big Data analytics tools make it possible to analyze these large quantities of data in order to gain the insight that supports decision-making. This paper gives an overview of the applications of Big Data in maritime industry, specifically in logistics optimization, safety and energy efficiency improvement, as well as the challenges that systems involving Big Data face. SažetakPomorska industrija je složeni sustav koji zahtijeva brzu prilagodbu u promjenjivim uvjetima u kojima je potrebno uzeti u obzir velik broj parametara prilikom donošenja odluka. Napretkom navigacijskih sustava, generira se znatna količina podataka o performansama broda i navigaciji. Analitički alati za velike skupove podataka omogućuju analizu tih podataka kako bi se dobilo razumijevanje potrebno za podršku donošenju odlukâ. Ovaj članak daje pregled primjene velikih skupova podataka u pomorstvu, posebno u optimizaciji logistike, sigurnosti i poboljšanju energetske učinkovitosti, kao i izazove s kojima se suočavaju sustavi koji koriste velike skupove podataka. KEY

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Section: Data Sources and Utilization Strategies / Izvori Podataka I mentioning

confidence: 99%

Section: Data Sources and Utilization Strategies / Izvori Podataka I mentioning

confidence: 99%

Section: Energy Efficiency Improvement / Unapređenje Energetske Učinkmentioning

confidence: 99%

Section: Safety Improvement / Unapređenje Sigurnostimentioning

confidence: 99%

See 2 more Smart Citations

Veliki skupovi podataka u pomorskoj industriji

Mirović¹,

Miličević²,

Obradovic³

2018

Naše more

View full text Add to dashboard Cite

show abstract

“…The results show that all sensor and DAQ fault situations are separated into the bottom PC. This is approach is illustrated to identify complex sensor and DAQ fault situations and these erroneous data points should be filtered to improve the quality of the respective data sets (Perera andMo, 2016b and2016c). However, the PCA capabilities not only detecting sensor and DAQ faults but also identifying the respective sensors are further investigated in this study.…”

Section: Sensor Fault Detectionmentioning

confidence: 99%

Statistical Filter based Sensor and DAQ Fault Detection for Onboard Ship Performance and Navigation Monitoring Systems

Perera

2016

IFAC-PapersOnLine

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Abstract:Statistical filter based sensor and data acquisition (DAQ) fault detection is presented in this study. The parameters of a large-scale data set of ship performance and navigation information are considered as statistical distributions and principal component analysis (PCA) is used to identify the hidden structure of the same data set. This data set relates to a specific operating region of the main engine, where ship performance and navigation conditions can be linearized. The structure derived under PCA is further investigated to identify the respective sensor and DAQ fault situations as the main contribution. That is done by projecting the same data set into the respective principal components, where a new set of ship performance and navigation parameters is derived. Then, the respective parameter variance values of the new data set are calculated and the thresholds that relate to the same variance values for detecting sensor and DAQ fault situations are derived. Finally, the data set of ship performance and navigation information is analyzed through these fault thresholds and the successful results on identifying complex fault situations are presented in this study. Hence, this approach can be used to develop advanced sensor and DAQ fault detection and isolation methodologies of ship performance and navigation monitoring systems.

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Big Data-Based Decision Support Systems

Lang,

Mao

2022

Encyclopedia of Ocean Engineering

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Machine intelligence for energy efficient ships: A big data solution

Cited by 45 publications

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Veliki skupovi podataka u pomorskoj industriji

Veliki skupovi podataka u pomorskoj industriji

Statistical Filter based Sensor and DAQ Fault Detection for Onboard Ship Performance and Navigation Monitoring Systems

Big Data-Based Decision Support Systems

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