Reliability Goodness of Fit for Oil Spills in the Gulf of Mexico

Eschenbach and Harper (2006) analyzed offshore oil spills in the Gulf of Mexico with extensions to the northern seas of Alaska. This involved multiple methods including assessing what statistical distribution adequately fits the data. Empirical distribution function (EDF) statistical procedures are powerful goodness of fit tests and also provide for good visual assessments. The most powerful of the current EDF methods is the Anderson-Darling test. This paper focuses on the Anderson-Darling EDF goodness of fit procedure for both the two and three parameter Weibull distribution that is often used in reliability analysis. Excel VBA code has been developed to compute this test statistic and also the associated p-values to allow statistical significance tests. The Excel routines are available free at http://faculty.otterbein.edu/WHarper/. The functions are illustrated with Gulf of Mexico oil spill data

Reliability Confidence Intervals for Oil Spills in the Gulf of Mexico

An extensive study [Eschenbach and Harper (2006)] of offshore oil spills in the Gulf of Mexico with extensions to the northern seas of Alaska involved the estimation of the likelihood of oil spill volumes in the Gulf of Mexico for both pipeline and platform spills. This paper develops both maximum likelihood based reliability and percentile confidence intervals for the 3- parameter Weibull distribution. The statistical aspects are discussed along with applications of developed Excel VBA functions. The Excel routines are available free on the web at http://faculty.otterbein.edu/WHarper/. The functions are illustrated with Gulf of Mexico oil spill data

Maximum Likelihood Estimation Methodology Comparison for the Three-Parameter Weibull Distribution with Applications to Offshore Oil Spills in the Gulf of Mexico

Maximum Likelihood estimation of the two-parameter Weibull distribution is straightforward; however, there are multiple methods for maximum likelihood estimation of the three-parameter Weibull. This paper presents an evaluation of these methods using four data sets including oil spill data from the Gulf of Mexico. Highlighted are fairly major differences in the estimated parameters between nine statistical packages. A VBA routine has been developed allowing practitioners to implement three-parameter Weibull maximum likelihood estimate within Excel. The code and support documentation are available free at http:faculty.otterbein.edu/WHarper

Powertrain modelling for engine stop-start dynamics and control of micro/mild hybrid construction machines

Engine stop-start control is considered as the key technology for micro/mild hybridisation of vehicles and machines. To utilize this concept, especially for construction machines, the engine is desired to be started in such a way that the operator discomfort can be minimized. To address this issue, this paper aims to develop a simple powertrain modelling approach for engine stop-start dynamic analysis and an advanced engine start control scheme newly applicable for micro/mild hybrid construction machines. First, a powertrain model of a generic construction machine is mathematically developed in a general form which allows to investigate the transient responses of the system during the engine cranking process. Second, a simple parameterisation procedure with a minimum set of data required to characterise the dynamic model is presented. Third, a model- based adaptive controller is designed for the starter to crank the engine quickly and smoothly without the need of fuel injection while the critical problems of machine noise, vibration and harshness can be eliminated. Finally, the advantages and effectiveness of the proposed modelling and control approaches have been validated through numerical simulations. The results imply that with the limited data set for training, the developed model works better than a high fidelity model built in AMESim while the adaptive controller can guarantee the desired cranking performance

Challenges of micro/mild hybridisation for construction machinery and applicability in UK

In recent years, micro/mild hybridisation (MMH) is known as a feasible solution for powertrain development with high fuel efficiency, less energy use and emission and, especially, low cost and simple installation. This paper focuses on the challenges of MMH for construction machines and then, pays attention to its applicability to UK construction machinery. First, hybrid electric configurations are briefly reviewed; and technological challenges towards MMH in construction sector are clearly stated. Second, the current development of construction machinery in UK is analysed to point out the potential for MMH implementation. Thousands of machines manufactured in UK have been sampled for the further study. Third, a methodology for big data capturing, compression and mining is provided for a capable of managing and analysing effectively performances of various construction machine types. By using this method, 96% of data memory can be reduced to store the huge machine data without lacking the necessary information. Forth, an advanced decision tool is built using a fuzzy cognitive map based on the big data mining and knowledge from experts to enables users to define a target machine for MMH utilization. The numerical study with this tool on the sampled machines has been done and finally realized that one class of heavy excavators is the most suitable to apply MMH technology

Powertrain modelling and engine start control of construction machines

This paper aims to develop an engine start control approach for a micro/mild hybrid machine for a capable of cranking the engine without injection. First, the powertrain is physically modelled using a co-simulation platform. Second, experiment data of the traditional machine is acquired to optimize the model. Third, a model-based adaptive controller is designed for the starter to crank the engine quickly and smoothly to minimize the operator discomfort. The effectiveness of the proposed approach is validated through numerical simulations with the established model