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

Analysis of Disengagements in Semi-Autonomous Vehicles: Drivers’ Takeover Performance and Operational Implications

This report analyzes the reactions of human drivers placed in simulated Autonomous Technology disengagement scenarios. The study was executed in a human-in-the-loop setting, within a high-fidelity integrated car simulator capable of handling both manual and autonomous driving. A population of 40 individuals was tested, with metrics for control takeover quantification given by: i) response times (considering inputs of steering, throttle, and braking); ii) vehicle drift from the lane centerline after takeover as well as overall (integral) drift over an S-turn curve compared to a baseline obtained in manual driving; and iii) accuracy metrics to quantify human factors associated with the simulation experiment. Independent variables considered for the study were the age of the driver, the speed at the time of disengagement, and the time at which the disengagement occurred (i.e., how long automation was engaged for). The study shows that changes in the vehicle speed significantly affect all the variables investigated, pointing to the importance of setting up thresholds for maximum operational speed of vehicles driven in autonomous mode when the human driver serves as back-up. The results shows that the establishment of an operational threshold could reduce the maximum drift and lead to better control during takeover, perhaps warranting a lower speed limit than conventional vehicles. With regards to the age variable, neither the response times analysis nor the drift analysis provide support for any claim to limit the age of drivers of semi-autonomous vehicles

Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis

Systematic network monitoring can be the cornerstone for the dependable operation of safety-critical distributed systems. In this paper, we present our vision for informed anomaly detection through network monitoring and resilience measurements to increase the operators' visibility of ATM communication networks. We raise the question of how to determine the optimal level of automation in this safety-critical context, and we present a novel passive network monitoring system that can reveal network utilisation trends and traffic patterns in diverse timescales. Using network measurements, we derive resilience metrics and visualisations to enhance the operators' knowledge of the network and traffic behaviour, and allow for network planning and provisioning based on informed what-if analysis

Condition monitoring for enhanced inspection, maintenance and decision making in ship operations

This paper presents the INCASS (Inspection Capabilities for Enhanced Ship Safety) project which brings innovative solutions to the ship inspection regime by integrating robotic-automated platforms for on-line or on-demand ship inspection activities and selecting the software and hardware tools that can implement or facilitate specific inspection tasks, to provide in- put to the Decision Support System (DSS). Enhanced inspection of ships includes ship structures and machinery monitoring with real time information using ‘intelligent’ sensors and incorporating structural and machinery risk analysis, using in-house structural/hydrodynamics and machinery computational tools. Condition based inspection tools and methodologies, reliability and criticality based maintenance are introduced. An enhanced central database handles ship structures and machinery data. The development and implementation of the INCASS system is shown in the case of ship machinery systems. In this way the validation and testing of the INCASS framework will be achieved in realistic operational conditions

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

Forecasting summer-time overheating in UK homes using time series models

Heatwaves are projected to become more frequent, intense and long-lasting in the UK and the prevalence of overheating in dwellings is set to increase. As a result, occupants will experience increased levels of thermal discomfort, heat stress and heat-related morbidity and mortality. Since the use of mechanical air conditioning in dwellings is unsustainable, and not widely affordable, it is of utmost importance to understand when heat related health risks are anticipated in free-running dwellings. This is crucial for vulnerable occupants, such as the elderly, for whom the accurate detection of future heat risks could prepare them (or their carers) for timely mitigation, for example, through additional window ventilation or the use of shading. Many countries deploy Heat-Health Warning Systems (HHWS) to alert their populations, however, these generally apply to a wide area and are based exclusively on regional weather forecasts. Consequently, HHWSs are unable to identify where, when, or to what extent individual buildings (and their occupants) will be affected. Previous studies have investigated the use of time series forecasting models, with the majority considering the use of Model Predictive Control. There is, however, no rigorous scientific evidence to support the belief that such models can provide accurate predictions in free-running dwellings during heatwaves and over multi-day forecasting horizons. This thesis therefore examines the use of black-box forecasting models to provide reliable predictions of the impending indoor temperatures in UK homes. Having established the viability of this approach, the application of such models in the context of an indoor Heat-Health Warning System (iHHWS) has been explored. This research led to five main findings: (i) linear AutoRegressive forecasting models with eXogenous inputs (ARX), i.e. weather forecasts, can provide satisfactory accuracies during heatwaves for time horizons up to 72 h ahead; (ii) more complex semi-parametric Generalized Additive Models (GAMs) were not capable of significantly improving the forecasting accuracy at forecasting horizons over 6 h (iii) logistic GAMs can predict the window opening state with adequate discrimination, however, integration of the window state into forecasting models did not improve their accuracy; (iv) forecasting models could be usefully incorporated within an iHHWS, however, the warning lead-time should be constrained to less than 24 h in order to guarantee high confidence in such a system; (v) a weighted metric such as the Cumulative Heat Index (CHI) could further reduce the risks of false or missed warnings, increasing the dependability of the iHHWS.</div

Multiple-panel longwall top coal caving induced microseismicity: Monitoring and development of a statistical forecasting model for hazardous microseismicity

Continuous microseismic monitoring was carried out around 9 producing longwall top coal caving (LTCC) panels with concurrently recorded daily face advance rates at Coal Mine Velenje in Slovenia over a 27-month monitoring period. The monitoring results suggested that spatial and magnitude characteristics of microseismicity are dominated by those of underlying fractures, while microseismic event rate is under the combined effects of local natural fracture abundance and mining intensity. On this basis, a data-driven yet physics-based forecasting methodology was established for LTCC induced hazardous microseismicity, which is above a given threshold of energy magnitude and within a certain distance to the longwall face. Statistical analyses were first conducted to characterise temporal, magnitude and spatial characteristics of long-term recorded microseismicity, based on which a short-term forecasting model was developed to calculate the probability of hazardous microseismicity considering the three characteristics. The model developed was employed to forecast the likelihood of hazardous microseismicity at one of these LTCC panels, and the forecasted results were supported by the monitoring. This statistical model has important implications in the evaluation of mining-induced hazards, and it can be used to optimise longwall face advance rates to minimise the risk of hazardous microseismicity in burst-prone deep-level mining sites

An Automated Method of Predicting Clear-Air Turbulence

Clear-air turbulence (CAT) prediction is vitally important to military aviation and the successful completion of Department of Defense (DoD) operations such as air to air refueling and new national defensive weapon systems such as directed energy platforms. The unique mission requirements of military aircraft often require strict avoidance of turbulent regions. Traditionally, weather forecasters have found it difficult to accurately predict CAT. In order to forecast regions where CAT might occur, forecasters must first determine the location of breaking waves caused by either Kelvin-Helmholtz instabilities or topographically forced internal gravity waves (mountain waves) in the atmosphere. The United States Air Force (USAF) 15th Operational Weather Squadron (15th OWS) requested an updated method of predicting CAT and this request was ranked as one of the highest priority research needs by the HQ USAF Director of Weather, Deputy Chief of Staff for Air and Space Operations. A new method of forecasting turbulence was developed in this work and the operational model was delivered to the 15th OWS for immediate inclusion into their operations. This method combines output from the Knapp-Ellrod index and the Naval Research Laboratory s Mountain Wave Forecast Model (MWFM) onto a single chart. Displaying these tools together allows forecasters to view both causes of CAT simultaneously. Furthermore, a new visualization tool is developed that allows a forecaster to view several layers at the same time as well as a composite chart to greatly reduce the time required to produce turbulence charts by OWS forecasting centers worldwide. Tests of forecast accuracy, as determined by pilot reports (PIREPS), between charts currently produced by USAF OWSs and this new method were compared, with the new method producing far superior forecast results. This method revolutionize

Maine CDC Extreme Temperature Community Resilience Guidebook

The purpose of this guidebook is to help community leaders, public health departments, municipalities, and state and local emergency planning agencies prepare for extreme temperature events in Maine. The Environmental and Occupational Health Program at the Maine Center Disease Control and Prevention (CDC) developed this Extreme Temperature Community Resilience Guidebook using existing best practices and strategies to adapt to the changing climate and to address community needs during extreme temperature events. Because extreme temperature events are becoming more frequent, local leaders must work in collaboration with private and public community partners to prepare and respond to the impact of such public health events. Community partners should use this guidebook to advance health equity by addressing needs experienced by disadvantaged communities and vulnerable populations