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

Fault diagnostics for advanced cycle marine gas turbine using genetic algorithm

The major challenges faced by the gas turbine industry, for both the users and the manufacturers, is the reduction in life cycle costs , as well as the safe and efficient running of gas turbines. In view of the above, it would be advantageous to have a diagnostics system capable of reliably detecting component faults (even though limited to gas path components) in a quantitative marmer. V This thesis presents the development an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using advanced concepts in genetic algorithm. The diagnostics model operates in three distinct stages. The rst stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses the heuristics modification of genetics algorithm parameters through a master-slave type configuration. The third stage uses the elitist model concept in genetic algorithm to preserve the accuracy of the solution in the face of randomness. The above fault diagnostics model has been integrated with a nested neural network to form a hybrid diagnostics model. The nested neural network is employed as a pre- processor or lter to reduce the number of fault classes to be explored by the genetic algorithm based diagnostics model. The hybrid model improves the accuracy, reliability and consistency of the results obtained. In addition signicant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR2l engine has been used as the test engine for implementing the diagnostics model.SOE Prize winne

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

Data-driven modeling of energy-exergy in marine engines by supervised ANNs based on fuel type and injection angle classification

The application of artificial neural networks with the involvement of a modified homogeneity factor to predict exergetic terms from combustive and/or mixing dynamics in a marine engine is considered in this study. This is a significant step since the mathematical formulation of exergy in combustion is complicated and even unconvincing due to the turbulent and highly nonlinear nature of the combustion process. The computational simulations are carried out on a marine CI (compression ignition) engine and the respective data per different fuel types that are used for thermodynamic exergetic computations as well as energetic simulations. A new parameter namely the modified homogeneity factor derived by an artificial neural network (ANN) is considered for the mixing dynamics, i.e. as an input parameter for the availability and irreversibility predictions. This parameter is based on the standard deviation from an ideal air-fuel mixture formed within the combustion chamber of the marine engine. Furthermore, spray and injection quantities along with the combustion process and its heat transfer parameters are served to predict the exergetic terms for two study cases: (a) fuel type and (b) injection orientation. It is shown that using data analytics that consists of neural networks can provide an adequate approach in diesel engines for improving energy efficiency and reducing emissions

A novel method for safety analysis of Cyber-Physical Systems - Application to a ship exhaust gas scrubber system

Cyber-Physical Systems (CPSs) represent a systems category developed and promoted in the maritime industry to automate functions and system operations. In this study, a novel Combinatorial Approach for Safety Analysis is presented, which addresses the traditional safety methods’ limitations by integrating System Theoretic Process Analysis (STPA), Events Sequence Identification (ETI) and Fault Tree Analysis (FTA). The developed method results into the development of a detailed Fault Tree that captures the effects of both the physical components/subsystems and the software functions’ failures. The quantitative step of the method employs the components’ failure rates to calculate the top event failure rate along with criticality analysis metrics for identifying the most critical components/functions. This method is implemented for an exhaust gas open loop scrubber system safety analysis to estimate its failure rate and identify critical failures considering the baseline system configuration as well as various alternatives with advanced functions for monitoring and diagnostics. The results demonstrate that configurations with SOx sensor continuous monitoring or scrubber unit failure diagnosis/prognosis lead to significantly lower failure rate. Based on the analysis results, the advantages/disadvantages of the novel method are also discussed. This study also provides insights for better safety analysis of the CPSs

Multidimensional prognostics for rotating machinery: A review

open access articleDetermining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and avail- ability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidi- mensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and draw- backs and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment

A Research Agenda in Maritime Crew Resource Management.

This paper opens with a brief introduction to the development of Crew Resource Management (CRM) training in the international shipping industry, a concept that was first advanced through the use of simulators in maritime training colleges over 25 years ago. The paper charts the development of the shipping industry’s approach to the preparation of bridge and engine room teams for normal and abnormal operations, and critiques the current training regime in resource management. Two case studies are presented to highlight some of the CRM issues raised by recent maritime casualties, and the paper then proceeds to set out a research agenda for exploring some of these issues. The paper provides an overview of three research initiatives: the first is to gain a better theoretical understanding of the nature of shared situational awareness and mental models in "real world" maritime operations. A second initiative is to identify a set of behavioural markers for assessing the non-technical skills of crisis management. The third initiative is to explore the role of organisational factors in safe operation, in recognition of the limitations of operator training as a panacea to prevent the re-occurrence of accidents

Monitoring of the piston ring-pack and cylinder liner interface in diesel engines through acoustic emission measurements

Investigation of novel condition monitoring systems for diesel engines has received much recent attention due to the increasing demands placed upon engine components and the limitations of conventional techniques. This thesis documents experimental research conducted to assess the monitoring capabilities of Acoustic Emission (AE) analysis. In particular it focuses on the possibility of monitoring the piston ring-pack and cylinder liner interface, a critical engine sub-system for which there are currently few practical monitoring options. A series of experiments were performed on large, two-stroke and small, four-stroke diesel engines. Tests under normal operating conditions developed a detailed understanding of typical AE generation in terms of both the source mechanisms and the characteristics of the resulting activity. This was supplemented by specific tests to investigate possible AE generation at the ring-pack/liner interface. For instance, for the small engines measures were taken to remove known AE sources in order to accentuate any activity originating at the interface whilst for the large engines the interfacial conditions were purposely deteriorated through the removal of the lubricating oil supply to one cylinder. Interpretation of the results was based mainly upon comparisons with published work encompassing both the expected ring-pack behaviour and AE generation from tribological processes. This provided a strong indication that the source of the ring-pack/liner AE activity was the boundary frictional losses. The ability to monitor this process may be of significant benefit to engine operators as it enhances the diagnostic information currently available and may be incorporated into predictive maintenance strategies. A further diagnostic technique considered was the possibility of using AE parameters combined with information of crankshaft speed fluctuations to evaluate engine balance and identify underperforming cylinders.EU Competitive and Sustainable Growth Programme, Project no: GRD2-2001-5001

Dynamic predictive reliability assessment of ship systems

Recent research shows that maritime industry has adopted innovative and sophisticated inspection and maintenance practices. A flexible framework, applicable on complex machinery, is introduced towards ship maintenance. A holistic inspection and maintenance notion is implemented, introducing different strategies, methodologies, and tools, suitably selected, for each required ship system. The proposed framework enables predictive reliability assessment of ship machinery, while scheduling maintenance actions by enhancing safety and systems' availability. This paper presents the Probabilistic Machinery Reliability Assessment (PMRA) strategy, which achieves predictive reliability assessment and evaluation of different complex ship systems. The assessment takes place on system, subsystem and component level, while allowing data fusion of different data types from various input sources. In this respect, the combination of data mining method (k-means), manufacturers' alarm levels, dynamic state modelling (Markov Chains), probabilistic predictive reliability assessment (Dynamic Bayesian Belief Networks) and qualitative decision making (Failure Modes and Effects Analysis) is suggested. PMRA has been clearly demonstrated in a case study on selected ship machinery. The results identify the most unreliability systems, subsystems and components, while advising practical maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme