Research on consumption prediction of spare parts based on fuzzy C-means clustering algorithm and fractional order model

In order to achieve the non-stationary de-noising signal effectively, and to solve the prediction of less sample, a hybrid model composed of FCCA (Fuzzy C-means clustering algorithm) and FOM (Fractional Order Model) was constructed. The degree of each data point was determined by FCCA to de-noise and the p order cumulative matrix was extended to r fractional cumulative matrix, so that the fractional order cumulative grey model was established to make forecasting. The results of numerical example showed that the hybrid model can obtain better prediction accuracy

Rule-based integrated building management systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The introduction of building management systems in large buildings have improved the control of building services and provided energy savings. However, current building management systems are limited by the physical level of integration of the building's services and the lack of intelligence provided in the control algorithms. This thesis proposes a new approach to the design and operation of building management systems using rule-based artificial intelligence techniques. The main aim of is to manage the services in the building in a more co-ordinated and intelligent manner than is possible by conventional techniques. This approach also aims to reduce the operational cost of the building by automatically tuning the energy consumption in accordance with occupancy profile of the building. A rule-based design methodology is proposed for building management systems. The design adopts the integrated structure made possible by the introduction of a common communications network for building services. The 'intelligence' is coded in the form of rules in such a way that it is both independent of any specific building description and easy to facilitate subsequent modification and addition. This is achieved using an object-oriented approach and classifying the range of data available into defined classes. The rules are divided into two knowledge-bases which are concerned with the building's control and its facilities management respectively. A wide range of rule-based features are proposed to operate on this data structure and are classified in terms of the data classes on which they operate. The concepts presented in this thesis were evaluated using software simulations, mathematical analysis and some hardware implementation. The conclusions of this work are that a rule-based building management system could provide significant enhancements over existing systems in terms of energy savings and improvements for both the building's management staff and its occupants

Cooling Load Prediction for Different Building Types and Room Occupancy Detection Using Accelerometers

There are two parts in this thesis: the first part was conducted at UWM, and the second part was conducted at Johnson Controls using the knowledge and skills that I learned throughout my time in the Master’s Degree program. The primary purpose of my time at UWM was to compare different types of buildings with two popular machine learning regression algorithms, artificial neural network (ANN), supported vector machine regression (SVR) algorithms, and lastly to provide the results of my research to better help building managers make more informed decisions in regard to electrical utilities. The major objective is to use algorithms and neural networks to detect the occupancy of a room using real-time data from accelerometers. This data could then be used to enable HVAC systems to be more efficient and intelligent. My research at UWM consists of 6 chapters. The background and related research are shown first in chapter 1 and chapter 2. Chapter 3 focuses on analyzing different building types, which aims to provide an overlook in the feature of the data. The basic concepts of ANN and SVR are included in the Chapter 4. The last chapter is the summary of internship in Johnson Controls during the summer. The project goal, data analysis and results are presented with details. A brief occupancy detection review of the industry as well as the basic knowledge of Wavelet Transform and K-means++ algorithm are also mentioned in Chapter 7. The result of my research at UWM shows that it is necessary to apply different models for different building types if high accuracy is required. Compared to SVR, ANN is more accurate in all the building types. However, the difference of the accuracy depends on the building features. In a hospital, SVR and ANN both show high accuracy, but in restaurants, they are both underperforming. Additionally, using vibration magnitude measured from accelerometers to detect occupancy has proved to be feasible during the first stage. However, more complicated cases and patterns need to be considered and higher resolution sensors will need to be tested in the future work

Framework for spare parts management. Methods to improve decision making. (Marco de referencia para la gestión de repuestos. Métodos para la mejora del proceso de toma de decisiones)

Este trabajo surge en un marco de colaboración con la Universidad de Brescia, mediante el intercambio de información con uno de sus alumnos y el seguimiento en paralelo de los departamentos homo logos correspondientes. Este intercambio se facilita tanto por el uso del inglés para la realización del trabajo, como por los conocimientos de italiano adquiridos en el Politécnico de Milán durante una estancia anual donde el tema de investigación presentado aparece de forma recurrente. En el sector industrial, especialmente en aquellas empresas con activos que requieren grandes inversiones y alto grado de especialización, nos encontramos con el problema de la gestión de repuestos, cuya gestión tiene un notable impacto en el desempeño final de la organización. Cualquier fallo en la maquinaria conlleva un paro en la producción principal, y este paro n depende del tiempo de diagnóstico, reparación o cambio de pieza. Siendo piezas de alta especialización esto puede implicar fabricar el repuesto desde cero con sus implicaciones: Caí da del nivel de servicio, menores tasas de producción, roturas de stock, perdidas... Se podrí a pensar que la solución es mantener un nivel de stock de piezas de repuesto de manera que se reduzcan estos Lead Time, pero se debe tener en cuenta que mantener un stock de piezas de repuestos con riesgo de obsolescencia alto, costes de mantenimiento de inventario elevado y de alguna manera “secundarios” para la producción, puede significar unos costes excesivos. La clave está en buscar el balance que permita minimizar los costes conjuntos y encuentre el modo de proceder óptimo. En la literatura se estudia el problema de gestión de repuestos, pero se hace de manera demasiado focalizada, estudiando y actuando sobre cada pequen o proceso decisional en vez de tratar de atacar el todo. Este ha sido el objetivo de este trabajo, ofrecer una metodología completa, que englobe todos los procesos de gestión, que aporte herramientas y modelos, que encontrando su justificación en la literatura, proporcionen un respaldo y un soporte objetivo a los protocolos de acción a la hora de la toma de decisiones.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías Industriale

An efficient framework for short-term electricity price forecasting in deregulated power market

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. BSA as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information (MI) and SVM. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.©2021 Institute of Electrical and Electronics Engineers. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/This research has been supported by University of Vaasa under Profi4/WP2 project with the financial support provided by the Academy of Finland.fi=vertaisarvioitu|en=peerReviewed

Prognostic Algorithms for Condition Monitoring and Remaining Useful Life Estimation

To enable the benets of a truly condition-based maintenance philosophy to be realised, robust, accurate and reliable algorithms, which provide maintenance personnel with the necessary information to make informed maintenance decisions, will be key. This thesis focuses on the development of such algorithms, with a focus on semiconductor manufacturing and wind turbines. An introduction to condition-based maintenance is presented which reviews dierent types of maintenance philosophies and describes the potential benets which a condition- based maintenance philosophy will deliver to operators of critical plant and machinery. The issues and challenges involved in developing condition-based maintenance solutions are discussed and a review of previous approaches and techniques in fault diagnostics and prognostics is presented. The development of a condition monitoring system for dry vacuum pumps used in semi- conductor manufacturing is presented. A notable feature is that upstream process mea- surements from the wafer processing chamber were incorporated in the development of a solution. In general, semiconductor manufacturers do not make such information avail- able and this study identies the benets of information sharing in the development of condition monitoring solutions, within the semiconductor manufacturing domain. The developed solution provides maintenance personnel with the ability to identify, quantify, track and predict the remaining useful life of pumps suering from degradation caused by pumping large volumes of corrosive uorine gas. A comprehensive condition monitoring solution for thermal abatement systems is also presented. As part of this work, a multiple model particle ltering algorithm for prog- nostics is developed and tested. The capabilities of the proposed prognostic solution for addressing the uncertainty challenges in predicting the remaining useful life of abatement systems, subject to uncertain future operating loads and conditions, is demonstrated. Finally, a condition monitoring algorithm for the main bearing on large utility scale wind turbines is developed. The developed solution exploits data collected by onboard supervisory control and data acquisition (SCADA) systems in wind turbines. As a result, the developed solution can be integrated into existing monitoring systems, at no additional cost. The potential for the application of multiple model particle ltering algorithm to wind turbine prognostics is also demonstrated

Autonomously Reconfigurable Artificial Neural Network on a Chip

Artificial neural network (ANN), an established bio-inspired computing paradigm, has proved very effective in a variety of real-world problems and particularly useful for various emerging biomedical applications using specialized ANN hardware. Unfortunately, these ANN-based systems are increasingly vulnerable to both transient and permanent faults due to unrelenting advances in CMOS technology scaling, which sometimes can be catastrophic. The considerable resource and energy consumption and the lack of dynamic adaptability make conventional fault-tolerant techniques unsuitable for future portable medical solutions. Inspired by the self-healing and self-recovery mechanisms of human nervous system, this research seeks to address reliability issues of ANN-based hardware by proposing an Autonomously Reconfigurable Artificial Neural Network (ARANN) architectural framework. Leveraging the homogeneous structural characteristics of neural networks, ARANN is capable of adapting its structures and operations, both algorithmically and microarchitecturally, to react to unexpected neuron failures. Specifically, we propose three key techniques --- Distributed ANN, Decoupled Virtual-to-Physical Neuron Mapping, and Dual-Layer Synchronization --- to achieve cost-effective structural adaptation and ensure accurate system recovery. Moreover, an ARANN-enabled self-optimizing workflow is presented to adaptively explore a "Pareto-optimal" neural network structure for a given application, on the fly. Implemented and demonstrated on a Virtex-5 FPGA, ARANN can cover and adapt 93% chip area (neurons) with less than 1% chip overhead and O(n) reconfiguration latency. A detailed performance analysis has been completed based on various recovery scenarios

The influence of diet and metabolism on hippocampus and hypothalamus connectivity across the lifespan

The high prevalence of unhealthy dietary patterns, obesity, and related brain disorders such as dementia emphasise the importance of research that examines the effect of dietary and metabolic factors on brain health. Using magnetic resonance imaging (MRI) to assess brain grey matter functional connectivity (FC) and volumes, this thesis aimed to examine the relationship between measures of diet and metabolism and the brain over the adult lifespan. First, a systematic review was conducted, to examine the relationship between dietary and metabolic health in relation to a wide range of brain MRI markers. The reviewed evidence suggested that lower dietary and metabolic health quality was related to reduced brain volume and connectivity, especially in the default mode network and the frontal and temporal lobes, although there were contrasting trends for each of these associations. To address the gaps identified by the review, we examined the association between dietary and metabolic health in relation to the hippocampus and hypothalamus FC and volumes in the cross-sectional Human Connectome Project cohort of 400 younger adults and in the longitudinal Whitehall II cohort of 775 midlife-older aged adults. The Whitehall cohort had longitudinal measures of diet/metabolic markers collected every 5 years throughout their midlife (40-70 years old). First, we note that different dietary and metabolic markers have unique patterns of longitudinal trajectories from mid-to-old-age. Our findings supported the hypothesis that better dietary and metabolic health is associated with volumetric and FC differences of the hippocampus and the hypothalamus both in younger and older cohorts. Specifically, dietary and metabolic health was linked to (1) hippocampal FC with the frontal lobe, precentral gyrus, and occipital lobe and (2) hypothalamic FC with the brainstem and the basal forebrain. These findings contribute to a growing understanding of the brain networks associated with dietary and metabolic health. The thesis provides insights into when in life dietary and metabolic health measures are related to brain health. Our findings indicated that in order to promote brain health in older age, some metabolic factors may be better targeted in midlife (e.g., cholesterol, diet, abdominal fat), while other factors should be targeted as early as possible (blood pressure, body composition/BMI). This may have implications for preventative lifestyle interventions to reduce the risk of developing dementia and to maintain overall brain health

Efficient use of deep learning and machine learning for load forecasting in South African power distribution networks

Abstract: Load forecasting, which is the act of anticipating future loads, has been shown to be important in power system network planning, operations and maintenance. Artificial Intelligence (AI) techniques have been shown to be good tools for load forecasting. Load forecasting can assist power distribution utilities maximise their revenue through optimising maintenance planning. With the dawn of the smart grid, first world countries have moved past the customer’s point of supply and use smart meters to forecast customer loads. These recent studies also utilise recent state of the art AI techniques such as deep learning techniques. Weather parameters are such as temperature, humidity and rainfall are usually used as parameters in these studies. South African load forecasting studies are outdated and recent studies are limited. Most of these studies are from 2010, and dating backwards to 1999. Hence they do not use recent state of the art AI techniques. The studies do not focus at distribution level load forecasting for optimal maintenance planning. The impact of adjusting power consumption data when there are spikes and dips in the data was not investigated in all these South African studies. These studies did not investigate the impact of weather parameters on different South African loads and hence load forecasting performance...D.Phil. (Electrical and Electronic Management