Condition Monitoring and Fault Diagnosis of a Marine Diesel Engine with Machine Learning Techniques

A marine engine room is a complex system in which many different subsystems are interacting with each other. At the center of this system is the main diesel engine which produces the propulsion force. Many other components such as compressed air, cooling, heating, lubricating oil, fuel, and pumping systems act as auxiliary machines to the main engine. Automation of many functions in the engine room is starting to play an important role in new generation ships to provide better control using sensors monitoring the engine and its environment. Sensors exist in the current generation ships, but engineers evaluate the sensor data for the presence of any problems. Maintenance actions are taken based on these manual analyses or regular maintenance is carried out at times determined by manufacturers, whether such actions are needed or not. With machine learning, it is possible to develop an algorithm using past evaluations made by engineers. Recent studies show that highly accurate results can be obtained using machine learning methods when there is sufficient data. In this study, we develop new learning-based algorithms and evaluate them on data obtained from a realistic ship engine room simulator. Data for a predetermined set of parameters of a high-power diesel engine were collected and analyzed for their role in a set of fault situations. These fault conditions and the associated sensor data are used to train a set of classifiers achieving fault detection up to 99% accuracy. These are promising results in preventing future damage to the engine or its supporting components by predicting failures before they occur

A decision support system for the development of voyage and maintenance plans for ships

The waterborne sector faces nowadays significant challenges due to several environmental, financial and other concerns. Such challenges may be addressed, among others, by optimising voyage plans, and diagnosing as early as possible engine failures that may lead to performance degradation. These two issues are addressed by the Decision Support System (DSS) presented herein, which focuses on the operation of merchant ships. For the development of voyage plans, a multicriteria decision problem is developed and handled with the PROMETHE method, while a multivariable control chart is used for the fault diagnosis problem. A MATLAB-based software implementation of the DSS has been developed adopting a modular architecture, while, in order to provide a generic software solution, the required input data are retrieved from dedicated web-services, following specific communication and data exchange protocols

The shipboard office\u27 : the role of IT and multi-media systems in managing engineering operations and maintenance onboard ship

This dissertation is an investigation of the roles of information technology and multimedia systems in the management of engineering operations and maintenance onboard ships. It considers the implications and constraints of adopting these technology tools on the economies of ship owners or managements companies, Maritime Administrations, and the Maritime Education and Training Institutions, especially in the developing countries. The study reviews the various shipboard maintenance policies and management techniques currently being practised in the traditional shipboard office with a view of understanding its constraints and the needs of adopting the various technological tools and systems. It also examines critically, the developmental trends in communication between the ship at sea and the shore office and how this trend has affected the landscape of management of shipboard operations and maintenance of engineering systems and machineries. The dissertation also presents an analysis of the result of the survey conducted to ascertain the practicability or otherwise as well as the possible effect of adopting IT and Multi-media systems in the management of shipboard operation. This is followed by the summary of the findings of the research, and concludes by recommending measures that will assist small ship owners and management companies (especially inn developing countries) to take full advantage of the benefits of indulging in these new technology tools in managing their ships

Risk and reliability modelling for multi-vehicle marine domains

It is well-known that autonomous underwater vehicle (AUV) missions are a challenging, high-risk robotics application. With many parallels to Mars rovers, AUV missions involve operating a vehicle in an inherently uncertain environment of which our prior knowledge is often sparse or low-resolution. The lack of an accurate prior, coupled with poor situational awareness and potentially significant sensor noise, presents substantial engineering challenges in navigation, localisation, state estimation and control. When constructing missions and operating AUVs, it is important to consider the risks involved. Stakeholders need to be reassured that risks of vehicle loss or damage have been minimised where possible, and scientists need to be confident that the mission is likely to produce sufficient high-quality data to meet the aims of the deployment. In this paper, we consider the challenges associated with risk analysis methods and representations for multi-vehicle missions, reviewing the relevant literature and proposing a methodology

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Engine Data Interpretation System (EDIS), phase 2

A prototype of an expert system was developed which applies qualitative constraint-based reasoning to the task of post-test analysis of data resulting from a rocket engine firing. Data anomalies are detected and corresponding faults are diagnosed. Engine behavior is reconstructed using measured data and knowledge about engine behavior. Knowledge about common faults guides but does not restrict the search for the best explanation in terms of hypothesized faults. The system contains domain knowledge about the behavior of common rocket engine components and was configured for use with the Space Shuttle Main Engine (SSME). A graphical user interface allows an expert user to intimately interact with the system during diagnosis. The system was applied to data taken during actual SSME tests where data anomalies were observed

Oil and Gas flow Anomaly Detection on offshore naturally flowing wells using Deep Neural Networks

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Oil and Gas industry, as never before, faces multiple challenges. It is being impugned for being dirty, a pollutant, and hence the more demand for green alternatives. Nevertheless, the world still has to rely heavily on hydrocarbons, since it is the most traditional and stable source of energy, as opposed to extensively promoted hydro, solar or wind power. Major operators are challenged to produce the oil more efficiently, to counteract the newly arising energy sources, with less of a climate footprint, more scrutinized expenditure, thus facing high skepticism regarding its future. It has to become greener, and hence to act in a manner not required previously. While most of the tools used by the Hydrocarbon E&P industry is expensive and has been used for many years, it is paramount for the industry’s survival and prosperity to apply predictive maintenance technologies, that would foresee potential failures, making production safer, lowering downtime, increasing productivity and diminishing maintenance costs. Many efforts were applied in order to define the most accurate and effective predictive methods, however data scarcity affects the speed and capacity for further experimentations. Whilst it would be highly beneficial for the industry to invest in Artificial Intelligence, this research aims at exploring, in depth, the subject of Anomaly Detection, using the open public data from Petrobras, that was developed by experts. For this research the Deep Learning Neural Networks, such as Recurrent Neural Networks with LSTM and GRU backbones, were implemented for multi-class classification of undesirable events on naturally flowing wells. Further, several hyperparameter optimization tools were explored, mainly focusing on Genetic Algorithms as being the most advanced methods for such kind of tasks. The research concluded with the best performing algorithm with 2 stacked GRU and the following vector of hyperparameters weights: [1, 47, 40, 14], which stand for timestep 1, number of hidden units 47, number of epochs 40 and batch size 14, producing F1 equal to 0.97%. As the world faces many issues, one of which is the detrimental effect of heavy industries to the environment and as result adverse global climate change, this project is an attempt to contribute to the field of applying Artificial Intelligence in the Oil and Gas industry, with the intention to make it more efficient, transparent and sustainable