Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals

Gear reducer motors play an important role in industry due to their robustness and simplicity of construction. However, the appearance of faults in these systems can affect the quality of the product and lead to significant financial losses. Therefore, it is necessary to perform Prognostics and Health Management (PHM) for these systems. This paper aims to develop a practical and effective method allowing an early fault detection and diagnostic for critical components of the gear reducer, in particular gear and bearing defects. This method is based on a new indicator extracted from electrical signals. It allows characterizing different states of the gear reducer, such as healthy state, bearing faults, gear faults, and combined faults. The diagnostic of these states is done by the Adaptive Neuro-Fuzzy Inference System (ANFIS). The efficiency and the robustness of the proposed method are highlighted through numerous experimental tests with different levels of loads and speeds

Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning

Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper-parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained

Control of Energy Storage

Energy storage can provide numerous beneficial services and cost savings within the electricity grid, especially when facing future challenges like renewable and electric vehicle (EV) integration. Public bodies, private companies and individuals are deploying storage facilities for several purposes, including arbitrage, grid support, renewable generation, and demand-side management. Storage deployment can therefore yield benefits like reduced frequency fluctuation, better asset utilisation and more predictable power profiles. Such uses of energy storage can reduce the cost of energy, reduce the strain on the grid, reduce the environmental impact of energy use, and prepare the network for future challenges. This Special Issue of Energies explore the latest developments in the control of energy storage in support of the wider energy network, and focus on the control of storage rather than the storage technology itself

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Economic and Policy Challenges of the Energy Transition in CEE Countries

With the announcement of the European Green Deal, which defines a set of policy initiatives aimed at achieving a 50–55% reduction in carbon emissions by 2030 and making Europe climate neutral in 2050, the challenge of energy transition becomes even more critical. The transformation of national energy systems towards sustainability is progressing throughout all Central and Eastern European (CEE) countries, yet the goals and results are different. Most EU Member States have made substantial progress towards meeting their long-term commitments of emissions reductions. However, some bloc members have struggled to meet their obligations. An effective energy transition requires the introduction of appropriately designed policy instruments and of robust economic analyses that ensure the best possible outcomes at the lowest costs for society. In this context, this Special Issue aims to bring into the discussion the challenges that CEE countries have to face and overcome while undergoing energy transition

Recent Advances and Perspectives in Deoxynivalenol Research

Mycotoxins are secondary metabolites produced by molds. Although the primary role of these toxins is thought to be related to the colonisation of the environment by the fungi—mycotoxins are able to kill other micro-organisms (antimicrobial effect) and/or plant cells (mycotoxin-producing fungi being necrophagic)—the exposure of animals and humans to mycotoxins through the consumption of mycotoxin-contaminated food and feeds leads to diseases and death. Among the different mycotoxins described (more than 350 mycotoxins have been identified), deoxynivalenol (DON or vomitoxin) produced by Fusarium species has attracted the most attention due to its prevalence and toxicity. DON is part of a family of mycotoxins called trichothecenes that are small sesquiterpenoids with an epoxide group at positions 12–13 allowing their binding to ribosomes causing the so-called ribosome stress response, characterized by the activation of various protein kinases that lead to alterations in gene expression and cellular toxicity in animals, humans and plants. Here, we compiled very recent findings regarding DON and its derivatives: i) their prevalence in human food; ii) the estimation of the exposure of humans to them using biological markers; iii) their roles during plant–fungi interaction; iv) the alteration caused by them in animals and humans, particularly at low doses that are close to those observed in farm animals and human consumers; v) possible strategies to decrease their presence in food and feeds. Overall, this book will give the reader a clear and global view on this important mycotoxin produced by Fusarium species which is responsible for huge economic loss and health issues

Active Materials

What is an active material? This book aims to redefine perceptions of the materials that respond to their environment. Through the theory of the structure and functionality of materials found in nature a scientific approach to active materials is first identified. Further interviews with experts from the natural sciences and humanities then seeks to question and redefine this view of materials to create a new definition of active materials