Gearing

Gearing technology in its modern form has a history of only 100 years. However, the earliest form of gearing can probably be traced back to fourth century B.C. Greece. Current gear practice and recent advances in the technology are drawn together. The history of gearing is reviewed briefly in the Introduction. Subsequent sections describe types of gearing and their geometry, processing, and manufacture. Both conventional and more recent methods of determining gear stress and deflections are considered. The subjects of life prediction and lubrication are additions to the literature. New and more complete methods of power loss predictions as well as an optimum design of spur gear meshes are described. Conventional and new types of power transmission systems are presented

An assessment of flywheel storage for efficient provision of reliable power for residential premises in islanded operation

Energy storage systems (ESS) are key devices for improving power quality, electrical system stability and system efficiency by contributing to the balance of supply and demand. They can enhance the flexibility of electrical systems by mitigating supply intermittency, which has recently become problematic due to the increased penetration of renewable generation. The subject of this thesis is flywheel energy storage system (FESS), a technology that is gathering great interest due to benefits offered over alternative energy storage solutions, including high cycle life, long calendar life, high round‐trip efficiency, high power density, operation at high ambient temperatures and low negative environmental impact. This thesis describes the modelling and assessment of small scale energy system incorporating FESS with solar photovoltaic (PV) and a diesel generator for use in islanded residential premises with highly intermittent or non‐existent grid infrastructure. In this application, incorporation of FESS is shown to be beneficial in comparison to a system without storage or one with the alternative storage technology, Li‐Ion batteries. The thesis begins with a description of flywheel storage systems configured for electrical storage which comprises of a mechanical part; flywheel rotor, bearings and containment, and an electric drive part; motor‐generator and associated power electronics. Each of these components is described in the thesis along with the equations and modelling, itself carried out in the MATLAB/Simulink environment. Finally, the flywheel model is combined with a model of an islanded residential power system incorporating a solar PV system with a diesel generator. Such a system would be particularly useful for offgrid applications or those with weak grids as occurs in developing countries

Latent Space Smoothing for Individually Fair Representations

Fair representation learning encodes user data to ensure fairness and utility, regardless of the downstream application. However, learning individually fair representations, i.e., guaranteeing that similar individuals are treated similarly, remains challenging in high-dimensional settings such as computer vision. In this work, we introduce LASSI, the first representation learning method for certifying individual fairness of high-dimensional data. Our key insight is to leverage recent advances in generative modeling to capture the set of similar individuals in the generative latent space. This allows learning individually fair representations where similar individuals are mapped close together, by using adversarial training to minimize the distance between their representations. Finally, we employ randomized smoothing to provably map similar individuals close together, in turn ensuring that local robustness verification of the downstream application results in end-to-end fairness certification. Our experimental evaluation on challenging real-world image data demonstrates that our method increases certified individual fairness by up to 60%, without significantly affecting task utility

Featured Anomaly Detection Methods and Applications

Anomaly detection is a fundamental research topic that has been widely investigated. From critical industrial systems, e.g., network intrusion detection systems, to people’s daily activities, e.g., mobile fraud detection, anomaly detection has become the very first vital resort to protect and secure public and personal properties. Although anomaly detection methods have been under consistent development over the years, the explosive growth of data volume and the continued dramatic variation of data patterns pose great challenges on the anomaly detection systems and are fuelling the great demand of introducing more intelligent anomaly detection methods with distinct characteristics to cope with various needs. To this end, this thesis starts with presenting a thorough review of existing anomaly detection strategies and methods. The advantageous and disadvantageous of the strategies and methods are elaborated. Afterward, four distinctive anomaly detection methods, especially for time series, are proposed in this work aiming at resolving specific needs of anomaly detection under different scenarios, e.g., enhanced accuracy, interpretable results, and self-evolving models. Experiments are presented and analysed to offer a better understanding of the performance of the methods and their distinct features. To be more specific, the abstracts of the key contents in this thesis are listed as follows: 1) Support Vector Data Description (SVDD) is investigated as a primary method to fulfill accurate anomaly detection. The applicability of SVDD over noisy time series datasets is carefully examined and it is demonstrated that relaxing the decision boundary of SVDD always results in better accuracy in network time series anomaly detection. Theoretical analysis of the parameter utilised in the model is also presented to ensure the validity of the relaxation of the decision boundary. 2) To support a clear explanation of the detected time series anomalies, i.e., anomaly interpretation, the periodic pattern of time series data is considered as the contextual information to be integrated into SVDD for anomaly detection. The formulation of SVDD with contextual information maintains multiple discriminants which help in distinguishing the root causes of the anomalies. 3) In an attempt to further analyse a dataset for anomaly detection and interpretation, Convex Hull Data Description (CHDD) is developed for realising one-class classification together with data clustering. CHDD approximates the convex hull of a given dataset with the extreme points which constitute a dictionary of data representatives. According to the dictionary, CHDD is capable of representing and clustering all the normal data instances so that anomaly detection is realised with certain interpretation. 4) Besides better anomaly detection accuracy and interpretability, better solutions for anomaly detection over streaming data with evolving patterns are also researched. Under the framework of Reinforcement Learning (RL), a time series anomaly detector that is consistently trained to cope with the evolving patterns is designed. Due to the fact that the anomaly detector is trained with labeled time series, it avoids the cumbersome work of threshold setting and the uncertain definitions of anomalies in time series anomaly detection tasks

Particle-scale numerical study on screening processes

The present study aimed to increase the understanding of the industrial screening process by using the discrete element method simulation (DEM) and machine learning modelling. Thus, the study focused on understanding the fundamentals of the complicated screening processes by investigating the process model with different controlling factors through particle-scale analysis. The particle-scale analysis was also linked to several macroscopic models and screening processes such as percolation of particles under vibration, the local passing of particles from the screen, choking of screening, non-spherical shaped particles contact detection and packing and machine learning modelling. The computational and theoretical analyses as well as machine leaning helped to clarify the use of particle-scale analysis and screening processes in several areas. The outcomes of this thesis include: (i) the percolation of particles under vibration and the machine learning modelling of percolation velocity to predict the size ratio threshold; (ii) a better understanding of screening process based on local passing of inclined and multi-deck screen and physics informed machine learning modelling to predict the particles passing; (iii) a logical model to predict the choking judgement of screen while combining the numerical results and machine learning and (iv) a novel contact force model for non-spherical particles by Fourier transformation and packing. The research in this thesis is useful for the fundamental understanding of the effect of particles’ contact force, operational conditions, particle properties, percolation and sieving on the screening process. Moreover, the novel process models based on artificial intelligence modelling, DEM simulation, and physics laws can help the design, control and optimisation of screening processes