Prediction of poor health in small ruminants and companion animals with accelerometers and machine learning

Global warming is one of the biggest challenge of our times, and significant efforts are being undertaken by academics, industries and other actors to tackle the problem. In the agricultural field precision farming is part of the solution to environmental sustainability and has been researched increasingly in recent years. Indeed, it has the potential to effectively increase livestock yield and decrease production carbon footprint while maintaining welfare. The thesis begins with a review of developments in automated animal monitoring and then moves on to a case study of a health monitoring system for small-ruminant in South Africa. As a demonstration and validation of the potential use case of the system, the method we propose is then applied to another study which aims to study feline health. Lower and Middle Income countries will be strongly affected by the changing climate and its impacts. We devise our method based on two South African small scale sheep and goat farms where assessment of the health status of individual animals is a key step in the timely and targeted treatment of infections, which is critical in the fight against anthelmintic and antimicrobial resistance. The FAMACHA scoring system has been used successfully to detect anaemia caused by infection with the parasitic nematode Haemonchus contortus in small ruminants and is an effective way to identify individuals in need of treatment. However, assessing FAMACHA is labour-intensive and costly as individuals must be manually examined at frequent intervals. Here, we used accelerometers to measure the individual activity of extensively grazed small ruminants exposed to natural Haemonchus contortus worm infection in southern Africa over long time scales (13+ months). When combined with machine learning for missing data imputation and classification, we find that this activity data can predict poorer health as well as those individuals that respond to treatment, with precision up to 80%. We demonstrate that these classifiers remain robust over time. Interpretation of trained classifiers reveals that poorer health can be predicted mainly by the night-time activity levels in the sheep. Our study reveals behavioural patterns across two small ruminant species, which low-cost biologgers can exploit to detect subtle changes in animal health and enable timely and targeted intervention. This has real potential to improve economic outcomes and animal welfare as well as limit the use of anthelmintic drugs and diminish pressures on anthelmintic resistance in both commercial and resource-poor communal farming. The validation of the proposed techniques with a different study group will be discussed in the latter part of the thesis. We used the accelerometry data of indoor cats equipped with wearable accelerometers in conjunction with their health status to detect signs of degenerative joint disease, and adapted our machine-learning pipeline to analyse bursts of high activity in the cats. We were able to classify high-activity events with precision up to 70% despite the relatively small dataset adding further evidence to the viability of animal health monitoring with accelerometers

A Unified Graph Formulation for Spatio-Temporal Wind Forecasting

publishedVersio

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

The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

Wind Turbine Fault Detection: an Unsupervised vs Semi-Supervised Approach

The need for renewable energy has been growing in recent years for the reasons we all know, wind power is no exception. Wind turbines are complex and expensive structures and the need for maintenance exists. Conditioning Monitoring Systems that make use of supervised machine learning techniques have been recently studied and the results are quite promising. Though, such systems still require the physical presence of professionals but with the advantage of gaining insight of the operating state of the machine in use, to decide upon maintenance interventions beforehand. The wind turbine failure is not an abrupt process but a gradual one. The main goal of this dissertation is: to compare semi-supervised methods to at tack the problem of automatic recognition of anomalies in wind turbines; to develop an approach combining the Mahalanobis Taguchi System (MTS) with two popular fuzzy partitional clustering algorithms like the fuzzy c-means and archetypal analysis, for the purpose of anomaly detection; and finally to develop an experimental protocol to com paratively study the two types of algorithms. In this work, the algorithms Local Outlier Factor (LOF), Connectivity-based Outlier Factor (COF), Cluster-based Local Outlier Factor (CBLOF), Histogram-based Outlier Score (HBOS), k-nearest-neighbours (k-NN), Subspace Outlier Detection (SOD), Fuzzy c-means (FCM), Archetypal Analysis (AA) and Local Minimum Spanning Tree (LoMST) were explored. The data used consisted of SCADA data sets regarding turbine sensorial data, 8 to tal, from a wind farm in the North of Portugal. Each data set comprises between 1070 and 1096 data cases and characterized by 5 features, for the years 2011, 2012 and 2013. The analysis of the results using 7 different validity measures show that, the CBLOF al gorithm got the best results in the semi-supervised approach while LoMST won in the unsupervised scenario. The extension of both FCM and AA got promissing results.A necessidade de produzir energia renovável tem vindo a crescer nos últimos anos pelas razões que todos sabemos, a energia eólica não é excepção. As turbinas eólicas são es truturas complexas e caras e a necessidade de manutenção existe. Sistemas de Condição Monitorizada utilizando técnicas de aprendizagem supervisionada têm vindo a ser estu dados recentemente e os resultados são bastante promissores. No entanto, estes sistemas ainda exigem a presença física de profissionais, mas com a vantagem de obter informa ções sobre o estado operacional da máquina em uso, para decidir sobre intervenções de manutenção antemão. O principal objetivo desta dissertação é: comparar métodos semi-supervisionados para atacar o problema de reconhecimento automático de anomalias em turbinas eólicas; desenvolver um método que combina o Mahalanobis Taguchi System (MTS) com dois mé todos de agrupamento difuso bem conhecidos como fuzzy c-means e archetypal analysis, no âmbito de deteção de anomalias; e finalmente desenvolver um protocolo experimental onde é possível o estudo comparativo entre os dois diferentes tipos de algoritmos. Neste trabalho, os algoritmos Local Outlier Factor (LOF), Connectivity-based Outlier Factor (COF), Cluster-based Local Outlier Factor (CBLOF), Histogram-based Outlier Score (HBOS), k-nearest-neighbours (k-NN), Subspace Outlier Detection (SOD), Fuzzy c-means (FCM), Archetypal Analysis (AA) and Local Minimum Spanning Tree (LoMST) foram explorados. Os conjuntos de dados utilizados provêm do sistema SCADA, referentes a dados sen soriais de turbinas, 8 no total, com origem num parque eólico no Norte de Portugal. Cada um está compreendendido entre 1070 e 1096 observações e caracterizados por 5 caracte rísticas, para os anos 2011, 2012 e 2013. A ánalise dos resultados através de 7 métricas de validação diferentes mostraram que, o algoritmo CBLOF obteve os melhores resultados na abordagem semi-supervisionada enquanto que o LoMST ganhou na abordagem não supervisionada. A extensão do FCM e do AA originou resultados promissores

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

On extending process monitoring and diagnosis to the electrical and mechanical utilities: an advanced signal analysis approach

This thesis is concerned with extending process monitoring and diagnosis to electrical and mechanical utilities. The motivation is that the reliability, safety and energy efficiency of industrial processes increasingly depend on the condition of the electrical supply and the electrical and mechanical equipment in the process. To enable the integration of electrical and mechanical measurements in the analysis of process disturbances, this thesis develops four new signal analysis methods for transient disturbances, and for measurements with different sampling rates. Transient disturbances are considered because the electrical utility is mostly affected by events of a transient nature. Different sampling rates are considered because process measurements are commonly sampled at intervals in the order of seconds, while electrical and mechanical measurements are commonly sampled with millisecond intervals. Three of the methods detect transient disturbances. Each method progressively improves or extends the applicability of the previous method. Specifically, the first detection method does univariate analysis, the second method extends the analysis to a multivariate data set, and the third method extends the multivariate analysis to measurements with different sampling rates. The fourth method developed removes the transient disturbances from the time series of oscillatory measurements. The motivation is that the analysis of oscillatory disturbances can be affected by transient disturbances. The methods were developed and tested on experimental and industrial data sets obtained during industrial placements with ABB Corporate Research Center, Kraków, Poland and ABB Oil, Gas and Petrochemicals, Oslo, Norway. The concluding chapters of the thesis discuss the merits and limitations of each method, and present three directions for future research. The ideas should contribute further to the extension of process monitoring and diagnosis to the electrical and mechanical utilities. The ideas are exemplified on the case studies and shown to be promising directions for future research.Open Acces