543 research outputs found

    The education and training of marine engineers on an engine room simulator at the Vietnam Maritime University

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    Diesel-cng dual fuel combustion characterization using vibro-acoustic analysis and response surface methodology

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    Engine conversion process from any diesel vehicle to a diesel-CNG dual fuel system requires additional fuel management. The need for an engine monitoring is vital to ensure the dual fuel operation run smoothly without excessive knocking, which may shorten the life of the engine. Knock and air-fuel ratio (AFR) sensors are commonly used for engine monitoring during fuel management setup. However, the engine output characteristics has been overlooked during the monitoring process. This study is aimed to explore a statistical approach by predicting the relationship between fuel management and engine output characteristics of diesel-CNG dual fuel engine using Response Surface Methodology (RSM). Two inputs which are CNG substitution rate and engine speed were used to predict the engine output characteristics in terms of engine performance, exhaust emissions, combustion pattern and combustion stability. Within the investigation, a statistical method was proposed to analyse the vibro-acoustic signal generated by a knock sensor installed at the outer cylinder wall of the engine. The frequency distribution analysis was applied to interpret the high variability of the vibro-acoustic signal. The results were used as the input for combustion stability in RSM analysis. It also provided useful information with regards to the engine stability. The response surface analysis showed that the CNG substitution rate and its properties significantly influenced the engine output characteristics. This study also describes the methodology to determine the accuracy and the significance of the developed prediction models. The prediction models were validated using confirmation test and showed good predictability within 95% confidence interval. Thus, it is concluded that RSM provide models that predict the engine characteristics with significant accuracy, which contributes to the effectiveness of diesel-CNG dual fuel engine conversion process

    AUTOMATED DIESEL ENGINE CONDITION & PERFORMANCE MONITORING & THE APPLICATION OF NEURAL NETWORKS TO FAULT DIAGNOSIS

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    The overall aim of this research was to design, configure and validate a system which was capable of on-line performance monitoring and fault diagnosis of a diesel engine. This thesis details the development and evaluation of a comprehensive engine test facility and automated engine performance monitoring package. Results of a diesel engine fault study were used to ascertain commonly occurring faults and their realistic severities are discussed. The research shows how computer simulation and rig testing can be applied to validate the effects of faults on engine performance and quantify fault severities. A substantial amount of engine test work has been conducted to investigate the effects of various faults on high speed diesel engine performance. A detailed analysis of the engine test data has led to the development of explicit fault-symptom relationships and the identification of key sensors that may be fitted to a diesel engine for diagnostic purposes. The application of a neural network based approach to diesel engine fault diagnosis has been investigated. This work has included an assessment of neural network performance at engine torques and speeds where it was not trained, noisy engine data, faulty sensor data, varying fault severities and novel faults which were similar to those which the network had been trained on. The work has shown that diagnosis using raw neural network outputs under operational conditions would be inadequate. To overcome these inadequacies a new technique using an on-line diagnostic database incorporating 'weight adjusting' and 'confidence factor' algorithms has been developed and validated. The results show a neural network combined with an on-line diagnostic database can be successfully used for practical diesel engine fault diagnosis to offer a realistic alternative to current fault diagnosis techniques.The Ministry Of Defenc

    Comparative analysis and improvement of onboard and shore-based machinery maintenance in Sierra Leone

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    THE INVESTIGATION INTO THE CONDITION MONITORING OF TRIBOLOGICAL BEHAVIOUR BETWEEN PISTON RING AND CYLINDER LINER USING ACOUSTIC EMISSIONS

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    To improve engine operational performance and reliability, this study focuses on the investigation into the behaviour of tribological conjunction between the ring - liner based on a comprehensive analysis of non-intrusive acoustic emission (AE) measurement. Particularly, the study will provide more knowledge of using AE for online monitoring and diagnosing the performances of the conjunction. To fulfil this study, it integrates analytical predictions of the theoretical modelling for the AE generation mechanism with extensive experimental evaluations. Moreover, effective signal processing techniques are implemented with a combination of the model based AE predictions to extract the weak and nonstationary AE contents that correlate more with the tribological behaviour. Based on conventional tribological models, tribological AE is modelled to be due to two main dynamic effects: asperity-asperity collision (AAC) and fluid-asperity interaction (FAI), which allows measured AE signals from the tribological conjunction to be explained under different scenarios, especially under abnormal behaviours. FAI induced AE is more correlated with lubricants and velocity. It presents mainly in the middle of engine strokes but is much weaker and severely interfered with AEs from not only valve landings, combustion and fuel injection shocks but also the effect of considerable AACs due to direct contacts and solid particles in oils. To extract weak AEs for accurately diagnosing the tribological behaviours, wavelet transform analysis is applied to AE signals with three novel schemes: 1) hard threshold based wavelet coefficients selection in which the threshold value and wavelet analysis parameters are determined using a modified velocity of piston motion which has high dependence on the AE characteristics predicted by the two models; 2) Adaptive threshold wavelet coefficients selection in which the threshold is gradually updated to minimise the distance between the AE envelopes and the predicted dependence; and 3) wavelet packet transform (WPT) analysis is carried out by an optimised Daubechies wavelet through a novel approach based on minimising the time and frequency overlaps in WPT spectrum. Based on these optimal analyses, the local envelope amplitude (LEA) and the average residual wavelet coefficient (ARWC) are developed from AE signals as novel indicators to reflect the tribological behaviours.\ud Both the hard threshold based LEA and wavelet packet transform LEA values allow two different new lubricants to be diagnosed in accordance with model predictions whereas they produce less consistent results in differentiating the used oil under several operating conditions. Nevertheless, ARWC can separate the used oil successfully in that it can highlight the AAC effects of particle collisions in used oils. Similarly, LEA shows little impacts of two alternative fuels on the tribological behaviours. However, ARWC shows significantly higher amplitudes in several operating conditions when more particles can be produced due to unstable and incomplete combustions of both the biodiesel and FT diesel, compared with pure diesel, indicating they can cause light wear

    Establishment of a novel predictive reliability assessment strategy for ship machinery

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    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

    Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

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    With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring
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