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

The strategic impacts of Intelligent Automation for knowledge and service work : An interdisciplinary review

We would like to thank Professor Jarvenpaa and the review team for all the constructive comments and suggestions that were most helpful in revising the paper and in offering a stronger contribution. We would also like to thank Professor Guy Fitzgerald for his constructive comments on earlier versions of the paper. This study was funded by the Chartered Institute of Professional Development (CIPD). The views expressed are those of the authors and not necessarily those of the CIPD.Peer reviewedPublisher PD

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

A novel framework for enhancing marine dual fuel engines environmental and safety performance via digital twins

The Internet of Things (IoT) advent and digitalisation has enabled the effective application of the digital twins (DT) in various industries, including shipping, with expected benefits on the systems safety, efficiency and environmental footprint. The present research study establishes a novel framework that aims to optimise the marine DF engines performance-emissions trade-offs and enhance their safety, whilst delineating the involved interactions and their effect on the performance and safety. The framework employs a DT, which integrates a thermodynamic engine model along with control function and safety systems modelling. The DT was developed in GT-ISE© environment. Both the gas and diesel operating modes are investigated under steady state and transient conditions. The engine layout is modified to include Exhaust Gas Recirculation (EGR) and Air Bypass (ABP) systems for ensuring compliance with ‘Tier III’ emissions requirements. The optimal DF engine settings as well as the EGR/ABP systems settings for optimal engine efficiency and reduced emissions are identified in both gas and diesel modes, by employing a combination of optimisation techniques including multi-objective genetic algorithms (MOGA) and Design of Experiments (DoE) parametric runs. This study addresses safety by developing an intelligent engine monitoring and advanced faults/failure diagnostics systems, which evaluates the sensors measurements uncertainty. A Failure Mode Effects and Analysis (FMEA) is employed to identify the engine safety critical components, which are used to specify operating scenarios for detailed investigation with the developed DT. The integrated DT is further expanded, by establishing a Faulty Operation Simulator (FOS) to simulate the FMEA scenarios and assess the engine safety implications. Furthermore, an Engine Diagnostics System (EDS) is developed, which offers intelligent engine monitoring, advanced diagnostics and profound corrective actions. This is accomplished by developing and employing a Data-Driven (DD) model based on Neural Networks (NN), along with logic controls, all incorporated in the EDS. Lastly, the manufacturer’s and proposed engine control systems are combined to form an innovative Unified Digital System (UDS), which is also included in the DT. The analysis of marine (DF) engines with the use of an innovative DT, as presented herein, is paving the way towards smart shipping.The Internet of Things (IoT) advent and digitalisation has enabled the effective application of the digital twins (DT) in various industries, including shipping, with expected benefits on the systems safety, efficiency and environmental footprint. The present research study establishes a novel framework that aims to optimise the marine DF engines performance-emissions trade-offs and enhance their safety, whilst delineating the involved interactions and their effect on the performance and safety. The framework employs a DT, which integrates a thermodynamic engine model along with control function and safety systems modelling. The DT was developed in GT-ISE© environment. Both the gas and diesel operating modes are investigated under steady state and transient conditions. The engine layout is modified to include Exhaust Gas Recirculation (EGR) and Air Bypass (ABP) systems for ensuring compliance with ‘Tier III’ emissions requirements. The optimal DF engine settings as well as the EGR/ABP systems settings for optimal engine efficiency and reduced emissions are identified in both gas and diesel modes, by employing a combination of optimisation techniques including multi-objective genetic algorithms (MOGA) and Design of Experiments (DoE) parametric runs. This study addresses safety by developing an intelligent engine monitoring and advanced faults/failure diagnostics systems, which evaluates the sensors measurements uncertainty. A Failure Mode Effects and Analysis (FMEA) is employed to identify the engine safety critical components, which are used to specify operating scenarios for detailed investigation with the developed DT. The integrated DT is further expanded, by establishing a Faulty Operation Simulator (FOS) to simulate the FMEA scenarios and assess the engine safety implications. Furthermore, an Engine Diagnostics System (EDS) is developed, which offers intelligent engine monitoring, advanced diagnostics and profound corrective actions. This is accomplished by developing and employing a Data-Driven (DD) model based on Neural Networks (NN), along with logic controls, all incorporated in the EDS. Lastly, the manufacturer’s and proposed engine control systems are combined to form an innovative Unified Digital System (UDS), which is also included in the DT. The analysis of marine (DF) engines with the use of an innovative DT, as presented herein, is paving the way towards smart shipping