Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management

This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears. A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL). Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions. These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Artificial neural network models: data selection and online adaptation

Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings have the biggest proportion in energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. Hence this PhD was intended towards managing the energy consumed by Heating, Ventilating and Air Conditioning (HVAC) systems in buildings benefiting from Model Predictive Control (MPC) technique. To achieve this goal, artificial intelligence models such as neural networks and Support Vector Machines (SVM) have been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not noise-free. In this PhD, Radial Basis Function Neural Networks (RBFNN) as a promising class of Artificial Neural Networks (ANN) were considered to model a sequence of time series processes where the RBFNN models were built using Multi Objective Genetic Algorithm (MOGA) as a design platform. Regarding the design of such models, two main challenges were tackled; data selection and model adaptation. Since RBFNNs are data driven models, the performance of such models relies, to a good extent, on selecting proper data throughout the design phase, covering the whole input-output range in which they will be employed. The convex hull algorithms can be applied as methods for data selection; however the use of conventional implementations of these methods in high dimensions, due to their high complexity, is not feasible. As the first phase of this PhD, a new randomized approximation convex hull algorithm called ApproxHull was proposed for high dimensions so that it can be used in an acceptable execution time, and with low memory requirements. Simulation results showed that applying ApproxHull as a filter data selection method (i.e., unsupervised data selection method) could improve the performance of the classification and regression models, in comparison with random data selection method. In addition, ApproxHull was employed in real applications in terms of three case studies. The first two were in association with applying predictive models for energy saving. The last case study was related to segmentation of lesion areas in brain Computed Tomography (CT) images. The evaluation results showed that applying ApproxHull in MOGA could result in models with an acceptable level of accuracy. Specifically, the results obtained from the third case study demonstrated that ApproxHull is capable of being applied on large size data sets in high dimensions. Besides the random selection method, it was also compared with an entropy based unsupervised data selection method and a hybrid method involving ApproxHull and the entropy based method. Based on the simulation results, for most cases, ApproxHull and the hybrid method achieved a better performance than the others. In the second phase of this PhD, a new convex-hull-based sliding window online adaptation method was proposed. The goal was to update the offline predictive RBFNN models used in HVAC MPC technique, where these models are applied to processes in which the data input-output range changes over time. The idea behind the proposed method is capturing a new arriving point at each time instant which reflects a new range of data by comparing the point with current convex hull presented via ApproxHull. In this situation the underlying model’s parameters are updated based on the new point and a sliding window of some past points. The simulation results showed that not only the proposed method could efficiently update the model while a good level of accuracy is kept but also it was comparable with other methods.Devido aos processos de industrialização e globalização o consumo de energia tem aumentado de forma contínua. A investigação sobre o consumo mostra que os edifícios consomem a maior fatia de energia. Por exemplo nos países da União Europeia essa fatia corresponde a cerca de 40% de toda a energia consumida. Assim, esta tese de Doutoramento tem um objetivo prático de contribuir para melhorar a gestão da energia consumida por sistemas Heating, Ventilating and Air Conditioning (HVAC) em edifícios, no âmbito de uma estratégia de controlo preditivo baseado em modelos. Neste contexto foram já propostos modelos baseados em redes neuronais artificiais e máquinas de vetores de suporte, para mencionar apenas alguns. Estas técnicas têm uma grande capacidade de modelar relações não-lineares entre entradas e saídas de sistemas, e são aplicáveis em ambientes de operação, que, como sabemos, estão sujeitos a várias formas de ruído. Nesta tese foram consideradas redes neuronais de função de base radial, uma técnica consolidada no contexto da modelação de séries temporais. Para desenhar essas redes foi utilizada uma ferramenta baseada num algoritmo genético multi-objectivo. Relativamente ao processo de desenho destes modelos, esta tese versa sobre dois aspetos menos estudados: a seleção de dados e a adaptação em linha dos modelos. Uma vez que as redes neuronais artificiais são modelos baseados em dados, a sua performance depende em boa medida da existência de dados apropriados e representativos do sistema/processo, que cubram toda a gama de valores que a representação entrada/saída do processo/sistema gera. Os algoritmos que determinam a figura geométrica que envolve todos os dados, denominados algoritmos convex hull, podem ser aplicados à tarefa de seleção de dados. Contudo a utilização das implementações convencionais destes algoritmos em problemas de grane dimensionalidade não é viável do ponto de vista prático. Numa primeira fase deste trabalho foi proposto um novo método randomizado de aproximação ao convex hull, cunhado com o nome ApproxHull, apropriado para conjuntos de dados de grande dimensão, de forma a ser viável do ponto de vista das aplicações práticas. Os resultados experimentais mostraram que a aplicação do ApproxHull como método de seleção de dados do tipo filtro, ou seja, não supervisionado, pode melhorar o desempenho de modelos em problemas de classificação e regressão, quando comparado com a seleção aleatória de dados. O ApproxHull foi também aplicado em três casos de estudo relativos a aplicações reais. Nos dois primeiros casos no contexto do desenvolvimento de modelos preditivos para sistemas na área da eficiência energética. O terceiro caso de estudo consiste no desenvolvimento de modelos de classificação para uma aplicação na área da segmentação de lesões em imagens de tomografia computorizada. Os resultados revelaram que da aplicação do método proposto resultaram modelos com uma precisão aceitável. Do ponto de vista da aplicabilidade do método, os resultados mostraram que o ApproxHull pode ser utilizado em conjuntos de dados grandes e com dados de grande dimensionalidade. Para além da comparação com a seleção aleatória de dados, o método foi também comparado com um método de seleção de dados baseado no conceito de entropia e com um método híbrido que resulta da combinação do ApproxHull com o método entrópico. Com base nos resultados experimentais apurou-se que na maioria dos casos estudados o método híbrido conseguiu melhor desempenho que os restantes. Numa segunda fase do trabalho foi proposto um novo método de adaptação em linha com base no algoritmo ApproxHull e numa janela deslizante no tempo. Uma vez que os processos e sistemas na envolvente do sistema HVAC são variantes no tempo e dinâmicos, o objetivo foi aplicar o método proposto para adaptar em linha os modelos que foram primeiramente obtidos fora de linha. A ideia base do método proposto consiste em comparar cada novo par entrada/saída com o convex hull conhecido, e determinar se o novo par tem dados situados fora da gama conhecida. Nessa situação os parâmetros dos modelos são atualizados com base nesse novo ponto e num conjunto de pontos numa determinada janela temporal deslizante. Os resultados experimentais demonstraram não só que o novo método é eficiente na atualização dos modelos e em mantê-los num bom nível de precisão, mas também que era comparável a outros métodos existentes