Dynamiske posisjonerings- og integrerte automasjonssystemer som beslutningsstøtte.

Oppgavens hovedmål er å se på hvordan datateknologi har utviklet seg de siste årene og finne ut om denne gir beslutningsstøtte for operatører av sikkerhetskritiske systemer. Undersøkelse av empiri fra to slike systemer installert ombord i et moderne og avansert offshorefartøy dannet grunnlaget for å vurdere om det er tilstede beslutningsstøtte i den grad som kreves for å utføre en sikker krevende maritim operasjon. Ved å foreta et dokumentstudie, analysere og systematisere opplysninger om et Dynamisk Posisjoneringssystem og et Integrert Automasjonssystem, har en fått etablert en forståelse om temaet. Svar og resultater er presentert med tall i tabellform for å visualisere forskjellen på graden av støtte til operatørene. Fokuset har vært å se på hvordan mennesket får presentert informasjonen fra systemene og hvordan den kan implementeres for beslutningstaking. Kommunikasjonen mellom systemene og samhandling mellom operatørene har også blitt sett på. I prosessen ble det avdekket flere relevante funn som er av interesse og som blir diskutert med forankring i problemstillingen og teorier. For å forsøke å knytte funn og teorier sammen ble det brukt teorier og metoder som er kjente innen temaene situasjonsbevissthet, beslutningstaking, beslutnings-støtte og menneskelige faktorer med flere. Ut i fra det som presenteres i tilgjengelig materiale fra systemene, er det rimelig å stille kritiske spørsmål til utviklingen og graden av beslutningsstøtte til operatørene. Svarene rangerer fra negative til positive. Sett i lys av datateknologien kan en si at utviklingen er funnet ulik for de to systemene det er forsket på. Dette gir grunnlag for å studere temaet mer inngående og etablere videre forskning. Det er identifisert et behov for å øke og forbedre graden av beslutningsstøtten til mennesker som beslutningstakere i slike sikkerhetskritiske systemer

Hydrogen as a Maritime Fuel–Can Experiences with LNG Be Transferred to Hydrogen Systems?

As the use of fossil fuels becomes more and more restricted there is a need for alternative fuels also at sea. For short sea distance travel purposes, batteries may be a solution. However, for longer distances, when there is no possibility of recharging at sea, batteries do not have sufficient capacity yet. Several projects have demonstrated the use of compressed hydrogen (CH2) as a fuel for road transport. The experience with hydrogen as a maritime fuel is very limited. In this paper, the similarities and differences between liquefied hydrogen (LH2) and liquefied natural gas (LNG) as a maritime fuel will be discussed based on literature data of their properties and our system knowledge. The advantages and disadvantages of the two fuels will be examined with respect to use as a maritime fuel. Our objective is to discuss if and how hydrogen could replace fossil fuels on long distance sea voyages. Due to the low temperature of LH2 and wide flammability range in air these systems have more challenges related to storage and processing onboard than LNG. These factors result in higher investment costs. All this may also imply challenges for the LH2 supply chain

Fault Detection with LSTM-Based Variational Autoencoder for Maritime Components

Maintenance routines on ships today follow either a reactive maintenance (RM) or preventive maintenance (PvM) approach. RM can be regarded as post-failure repair, which might create large costs. PvM uses predetermined maintenance intervals, which often involves unnecessary maintenance. Recently, prognostics and health management (PHM) has emerged as a potential way to develop an ideal maintenance policy. PHM aims to provide optimal maintenance schedule through the use of sensor measurement for fault detection and fault prognostics, among which fault detection is the first and fundamental action. In this paper, a long-short term memory based variational autoencoder (LSTM-VAE) is proposed for fault detection of maritime components onboard. It is a semi-supervised approach that requires only fault-free data for training. Therefore, it is widely applicable in the maritime industry since operational data in normal conditions already exists. Real-world operation data collected from a diesel engine on the research vessel (RV) Gunnerus is used to validate the method. Results show that the LSTM-VAE can detect the fault accurately

Coupling of dynamic reaction forces of a heavy load crane and ship motion responses in waves

The conventional approach to dynamic analysis of ship motion response with shipboard operation equipment is usually done by establishing combined equations of motion of the multi-body system. The weakness of such methods is usually associated with the effectiveness of modelling and the simulation efficiency of computing the dynamic ship responses in waves in the time domain. In recent years, time domain simulation of nonlinear ship motion response in waves has become more and more popular. In this paper, we present coupled simulation of a heavy load crane with interactive ship motion responses in waves. The static gravitational forces of the crane system and dynamic excitation forces from the payload are applied to the ship as external forces on varying attack points of the hull during crane operations. Simulation of the crane operation is implemented in the digital twin ship platform and demonstrated meaningful physical behaviours

Online Fault Detection in Autonomous Ferries: Using fault-type in-dependent spectral anomaly detection

Enthusiasm for ship autonomy is flourishing in the maritime industry. In this context, data-driven Prognostics and Health Management (PHM) systems have emerged as the optimal way to improve operational reliability and system safety. However, further research is needed to enhance the essential actions relating to such a system. Fault detection is the first and most crucial action of any data-driven PHM system. In this study, we propose a fault-type independent spectral anomaly detection algorithm for marine diesel engine degradation in autonomous ferries. The benefits of the algorithm are verified on three fault-types where the nature of degradation differs. Both normal operation data and faulty degradation data have been collected from a marine diesel engine, using two different engine load profiles. These profiles aim to replicate real autonomous ferry crossing operations, environmental conditions the ferry may encounter. First, the data is subjected to a feature selection process to remove irrelevant and redundant features. Then, a multi-regime normalization method is performed on the data to merge the engine loads into one context. Finally, a variational autoencoder is trained to estimate velocity and acceleration calculations of the anomaly score. Generic and dynamic threshold limits are simultaneously established to detect the fault time step online. The algorithm achieved an accuracy of 97.66% in the final test when the acceleration was used as the fault detector. The results suggest that the algorithm is independent of faulttypes with different nature of degradation related to the marine diesel engine

A Step-wise Feature Selection Scheme for a Prognostics and Health Management System in Autonomous Ferry Crossing Operation

Developing a reliable algorithm to detect faults automatically within critical components in autonomous ferries is essential for safe and cost-beneficial maritime operations. Autonomous ferries are equipped with hundreds of sensors. Thus, in order to support the algorithm, the input data should be subjected to a feature selection process. This paper introduces a novel step-wise feature selection scheme for prognostics and health management (PHM) system in autonomous ferries. The scheme mainly consists of two steps. The first step is the Pearson correlation analysis to reduce the redundant information among sensors. In order to study the importance of the selected features obtained by correlation analysis and removal of irrelevant features, the second step is sensitivity analysis (SA) based feature selection. The proposed scheme is evaluated on real-operational marine diesel engine data. In the experiments, both fault classification and fault detection demonstrate the feasibility of the proposed approach

Automatic Fault Detection for Marine Diesel Engine Degradation in Autonomous Ferry Crossing Operation

The maritime industry generally anticipates having semi-autonomous ferries in commercial use on the west coast of Norway by the end of this decade. In order to schedule maintenance operations of critical components in a secure and cost-effective manner, a reliable prognostics and health management system is essential during autonomous operations. Any remaining useful life prediction obtained from such system should depend on an automatic fault detection algorithm. In this study, an unsupervised reconstruction-based fault detection algorithm is used to predict faults automatically in a simulated autonomous ferry crossing operation. The benefits of the algorithm are confirmed on data sets of real-operational data from a marine diesel engine collected from a hybrid power lab. During the ferry crossing operation, the engine is subjected to drastic changes in operational loads. This increases the difficulty of the algorithm to detect faults with high accuracy. Thus, to support the algorithm, three different feature selection processes on the input data is compared. The results suggest that the algorithm achieves the highest prediction accuracy when the input data is subjected to feature selection based on sensitivity analysis