Designing Stochastic Cell Formation Problem Using Queuing Theory

This paper presents a new nonlinear mathematical model to solve a cell formation problem which assumes that processing time and inter-arrival time of parts are random variables. In this research, cells are defined as a queue system which will be optimized via queuing theory. In this queue system, each machine is assumed as a server and each part as a customer. The grouping of machines and parts are optimized based on the mean waiting time. For solving exactly, the proposed model is linearized. Since the cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating of initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Also, full factorial and Taguchi methods are used to set the crucial parameters in the solutions procedures. Numerical experiments are used to evaluate the performance of the proposed algorithms. The results of the study show that the proposed algorithms are capable of generating better quality solutions in much less time. Finally, a statistical method is used which confirmed that the MPSO algorithm generates higher quality solutions in comparison with the genetic algorithm (GA)

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

Reliability Consideration in the Design of Cellular Manufacturing Systems using Genetic Algorithm

Note: appendices for this title available . This thesis proposes a multi-objective, mixed integer, non-linear programming model of cellular manufacturing systems (CMS) design to maximize the system reliability and minimize the total system cost simultaneously. The model involves multiple machine types, multiple machines for each machine type, multiple part types, and alternative process routes for each part type. Each process route consists of a sequence of operations. System reliability associated with machines along process routes can be improved by increasing the number of parallel machines subject to acceptable cost. Assuming machine reliability to follow a lognormal distribution, the CMS design problem is to optimally decide the number of each machine type, assign machines to cells, and select, for each part type, the process route with the highest overall system reliability while minimizing the total cost. Genetic algorithm is applied to solve this practical-sized CMS design problem. It finds a heuristic solution within a reasonable computational time

Addressing Complexity and Intelligence in Systems Dependability Evaluation

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. The first aspect of complexity is the dependability modelling of large systems with many interconnected components and dynamic behaviours such as Priority, Sequencing and Repairs. To address this, the thesis proposes a novel hierarchical solution to dynamic fault tree analysis using Semi-Markov Processes. A second aspect of complexity is the environmental conditions that may impact dependability and their modelling. For instance, weather and logistics can influence maintenance actions and hence dependability of an offshore wind farm. The thesis proposes a semi-Markov-based maintenance model called “Butterfly Maintenance Model (BMM)” to model this complexity and accommodate it in dependability evaluation. A third aspect of complexity is the open nature of system of systems like swarms of drones which makes complete design-time dependability analysis infeasible. To address this aspect, the thesis proposes a dynamic dependability evaluation method using Fault Trees and Markov-Models at runtime.The challenge of “intelligence” arises because Machine Learning (ML) components do not exhibit programmed behaviour; their behaviour is learned from data. However, in traditional dependability analysis, systems are assumed to be programmed or designed. When a system has learned from data, then a distributional shift of operational data from training data may cause ML to behave incorrectly, e.g., misclassify objects. To address this, a new approach called SafeML is developed that uses statistical distance measures for monitoring the performance of ML against such distributional shifts. The thesis develops the proposed models, and evaluates them on case studies, highlighting improvements to the state-of-the-art, limitations and future work

Computational based automated pipeline corrosion data assessment

Corrosion is a complex process influenced by the surrounding environment and operational systems which cannot be interpreted by deterministic approach as in the industry codes and standards. The advancement of structural inspection technologies and tools has produced a huge amount of corrosion data. Unfortunately, available corrosion data are still under-utilized. Complicated assessment code, and manual analysis which is tedious and error prone has overburdened pipeline operators. Moreover, the current practices produce a negative corrosion growth data defying the nature of corrosion progress, and consuming a lot of computational time during the reliability assessment. Therefore, this research proposes a computational based automated pipeline corrosion data assessment that provides complete assessment in terms of statistical and computational. The purpose is to improve the quality of corrosion data as well as performance of reliability simulation. To accomplish this, .Net framework and Hypertext Preprocessor (PHP) language is used for an automated matching procedure. The alleviation of deterministic value in corrosion data is gained by using statistical analysis. The corrosion growth rate prediction and comparison is utilized using an Artificial Neural Network (ANN) and Support Vector Machine (SVM) model. Artificial Chemical Reaction Optimization Algorithm (ACROA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) model is used to improve the reliability simulation based on the matched and predicted corrosion data. A computational based automated pipeline corrosion data assessment is successfully experimented using multiple In-Line Inspection (ILI) data from the same pipeline structure. The corrosion data sampling produced by the automated matching is consistent compared to manual sampling with the advantage of timeliness and elimination of tedious process. The computational corrosion growth prediction manages to reduce uncertainties and negative rate in corrosion data with SVM prediction is superior compared to A ^N . The performance value of reliability simulation by ACROA outperformed the PSO and DE models which show an applicability of computational optimization models in pipeline reliability assessment. Contributions from this research are a step forward in the realization of computational structural reliability assessment

Operational expenditure optimisation utilising condition monitoring for offshore wind parks

There is a strong desire to increase the penetration of renewable energy sources inthe UK electricity market. Offshore wind energy could be a method to achieve this. However, there are still issues, both technical and economical, that hinder the development and exploitation of this energy source.A condition based maintenance plan that relies on fully integrating the input from condition monitoring and structural health monitoring systems could be the method to solve many of these issues. Improved maintenance scheduling has the potential to reduce maintenance costs, increase energy production and reduce the overall cost of energy. While condition monitoring systems for gearboxes, generators and main bearings have become common place over the last few years, the deployment of other monitoring systems has been slower. This could be due to the expense and complication of monitoring an entire wind farm. Wind park operators, correctly, would like to see proof that their investment will be prudent.To assist wind park operators and owners with this decision, an offshore wind operations and maintenance model that attempts to model the impacts of using monitoring systems has been developed. The development of the model is shown in this analysis: multiple methodologies are used to capture deterioration and the abilities of monitoring systems. At each stage benchmarks are shown against other models and available data. This analysis has a breadth and scope not currently addressed in literature and attempts to give insight to industry that was previously unavailable.There is a strong desire to increase the penetration of renewable energy sources inthe UK electricity market. Offshore wind energy could be a method to achieve this. However, there are still issues, both technical and economical, that hinder the development and exploitation of this energy source.A condition based maintenance plan that relies on fully integrating the input from condition monitoring and structural health monitoring systems could be the method to solve many of these issues. Improved maintenance scheduling has the potential to reduce maintenance costs, increase energy production and reduce the overall cost of energy. While condition monitoring systems for gearboxes, generators and main bearings have become common place over the last few years, the deployment of other monitoring systems has been slower. This could be due to the expense and complication of monitoring an entire wind farm. Wind park operators, correctly, would like to see proof that their investment will be prudent.To assist wind park operators and owners with this decision, an offshore wind operations and maintenance model that attempts to model the impacts of using monitoring systems has been developed. The development of the model is shown in this analysis: multiple methodologies are used to capture deterioration and the abilities of monitoring systems. At each stage benchmarks are shown against other models and available data. This analysis has a breadth and scope not currently addressed in literature and attempts to give insight to industry that was previously unavailable

The effect of overloading on reliability of wheel loader structural components

This research attempts to provide a fundamental understanding into the relationship between the productivity of material handling equipment, specifically wheel loaders, and their ability to operate reliably when subjected to high overload conditions. The overall aim is to determine the effect of overloading the bucket on wheel loader reliability. The specific objectives of the research are to: 1) evaluate the effect of overloading the bucket on wheel loader productivity; 2) examine the effect of overloading the bucket on hydraulic pressures in the hoist cylinders (used as a proxy for forces on a wheel loader); and 3) investigate the effect of overloading the bucket on the reliability of structural components of a wheel loader. To achieve these objectives, the research used data from on-board equipment monitors from the global fleet of ultra-class wheel loaders for a specific original equipment manufacturer to test the various research hypotheses. The data included production data, failure and repair data, and hydraulic cylinder pressures, which were used as a proxy for stresses on structural components. ANOVA and Pearson and Spearman correlations tests were performed on data samples to test the hypotheses. Duty-cycle relationships were established using linear life stress relationships ratios for the wheel loaders structural components. The research showed that, while higher bucket loads increase productivity, there is evidence that they slow down the loading cycle, may be detrimental to productivity. The hoist cylinder pressure increased with increasing payload weight. The reliability of the structural components was similar in both the standard and duty-cycle cases; although, the accuracy of the reliability models increased when the models accounted for duty-cycles --Abstract, page iii

Actuarial applications of survival analysis in healthcare

Healthcare actuaries are increasingly responsible for advising their employers and clients in areas of managed care. Managed care links traditional health actuarial financial work to areas of medical practice, to address the fundamental question: what works? These relatively new responsibilities have required an expansion of actuarial techniques into non-traditional areas, and, in particular, epidemiology and biostatistics. This study is about a specific area of statistics, survival analysis, a topic of great potential application in non-traditional managed care actuarial practice. Survival analysis is used frequently in biostatistics to evaluate the efficacy of treatments and to identify factors that contribute to patient survival. In this study, we illustrate three applications of survival models to solve real-world problems in areas of health actuarial practice: the estimation of survival of permanently disabled workers receiving lifetime benefits for occupational illness and injury, the rate at which seriously ill hospice patients, at risk of polypharmacy, are weaned from non-life sustaining drugs, and the ability to predict, using a model incorporating drug dosage information and specifically changes in dosage, changes in expected future lifetimes of hospice patients. All three case studies are examples of practical models that can be applied within a business context. The study will serve a more important purpose, if it shows health actuaries the potential value of the application of a non-traditional technique within their evolving practice

Improved wind turbine monitoring using operational data

With wind energy becoming a major source of energy, there is a pressing need to reduce all associated costs to be competitive in a market that might be fully subsidy-free in the near future. Before thousands of wind turbines were installed all over the world, research in e.g. understanding aerodynamics, developing new materials, designing better gearboxes, improving power electronics etc., helped to cut down wind turbine manufacturing costs. It might be assumed, that this would be sufficient to reduce the costs of wind energy as the resource, the wind itself, is free of costs. However, it has become clear that the operation and maintenance of wind turbines contributes significantly to the overall cost of energy. Harsh environmental conditions and the frequently remote locations of the turbines makes maintenance of wind turbines challenging. Just recently, the industry realised that a move from reactive and scheduled maintenance towards preventative or condition-based maintenance will be crucial to further reduce costs. Knowing the condition of the wind turbine is key for any optimisation of operation and maintenance. There are various possibilities to install advanced sensors and monitoring systems developed in recent years. However, these will inevitably incur new costs that need to be worthwhile and retro-fits to existing turbines might not always be feasible. In contrast, this work focuses on ways to use operational data as recorded by the turbine's Supervisory Control And Data Acquisition (SCADA) system, which is installed in all modern wind turbines for operating purposes -- without additional costs. SCADA data usually contain information about the environmental conditions (e.g. wind speed, ambient temperature), the operation of the turbine (power production, rotational speed, pitch angle) and potentially the system's health status (temperatures, vibration). These measurements are commonly recorded in ten-minutely averages and might be seen as indirect and top-level information about the turbine's condition. Firstly, this thesis discusses the use of operational data to monitor the power performance to assess the overall efficiency of wind turbines and to analyse and optimise maintenance. In a sensitivity study, the financial consequences of imperfect maintenance are evaluated based on case study data and compared with environmental effects such as blade icing. It is shown how decision-making of wind farm operators could be supported with detailed `what-if' scenario analyses. Secondly, model-based monitoring of SCADA temperatures is investigated. This approach tries to identify hidden changes in the load-dependent fluctuations of drivetrain temperatures that can potentially reveal increased degradation and possible imminent failure. A detailed comparison of machine learning regression techniques and model configurations is conducted based on data from four wind farms with varying properties. The results indicate that the detailed setup of the model is very important while the selection of the modelling technique might be less relevant than expected. Ways to establish reliable failure detection are discussed and a condition index is developed based on an ensemble of different models and anomaly measures. However, the findings also highlight that better documentation of maintenance is required to further improve data-driven condition monitoring approaches. In the next part, the capabilities of operational data are explored in a study with data from both the SCADA system and a Condition Monitoring System (CMS) based on drivetrain vibrations. Analyses of signal similarity and data clusters reveal signal relationships and potential for synergistic effects of the different data sources. An application of machine learning techniques demonstrates that the alarms of the commercial CMS can be predicted in certain cases with SCADA data alone. Finally, the benefits of having wind turbines in farms are investigated in the context of condition monitoring. Several approaches are developed to improve failure detection based on operational statistics, CMS vibrations or SCADA temperatures. It is demonstrated that utilising comparisons with neighbouring turbines might be beneficial to get earlier and more reliable warnings of imminent failures. This work has been part of the Advanced Wind Energy Systems Operation and Maintenance Expertise (AWESOME) project, a European consortium with companies, universities and research centres in the wind energy sector from Spain, Italy, Germany, Denmark, Norway and UK. Parts of this work were developed in collaboration with other fellows in the project (as marked and explained in footnotes)