Intelligent Fault Detection and Identification System for Analog Electronic Circuits Based on Fuzzy Logic Classifier

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works

Analysis on transformer vibration signal recognition based on convolutional neural network

In order to study the relationship between the transformer vibration and the operation state, the wavelet analysis method and the convolutional neural network method were used to analyze the transformer vibration signal. This paper proposes a transformer based on convolution neural network-based surface vibration signal feature extraction method. The result show that the convolution of neural network in different station transformer surface vibration signal classification has a lot of advantage, as the integration of feature extraction and classification recognition process together can effectively classify vibration signal recognition processing. This method is feasible for classification and identification by providing an accuracy value of 92.74 %. The future perspective of this research will focus on a generalized network model and parameters through experimentation for further investigation of accuracy and efficiency of this method

A review on deep learning applications in prognostics and health management

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain

Training autoencoders for state estimation in smart grids

Desde a sua implementação massiva nos centros de controlo dos sistemas eléctricos de energia, o estimador de estado apresenta-se como uma ferramenta de capital importância uma vez que permite a obtenção de um ponto de funcionamento do sistema com elevada precisão. Com efeito, uma das etapas essenciais à definição desse ponto de funcionamento é a determinação da topologia, isto é, o estado aberto ou fechado dos dispositivos de corte e seccionamento que a rede em questão possui.Assim, uma miríade de metodologias foi proposta com o firme intuito de endereçar o problema da identificação de topologia. Os primeiros passos foram dados em meados dos anos 80 sendo apresentadas abordagens baseadas em métodos puramente matemáticos e na análise dos seus resultados para a detecção de eventuais erros de topologia. Todavia, através da crescente popularidade e avanços nas áreas da inteligência artificial e da ciência da computação, foram desenvolvidos modelos baseados em redes neuronais, evidenciando resultados convincentes com pesos computacionais mais reduzidos e sendo, simultaneamente, livre dos problemas de convergência dos métodos estritamente analíticos.Tendo isto em consideração, nesta tese é ilustrada uma nova perspectiva da aplicação de redes neuronais, deep learning e Teoria de Informação (ITL) ao problema da determinação de topologia e, em última instância, à estimação de estados. Através da conjunção destes conceitos, pretende-se identificar a topologia de um dado sistema eléctrico de energia recorrendo ao reconhecimento dos padrões das medidas analógicas providenciadas, apontando, ao mesmo tempo, metodologias alternativas ao nível do treino e arquitectura de redes neuronais bem como de ferramentas computacionais que conduzam a uma maior eficiência desse processo. Particularizando, serão exploradas duas filosofias diferentes com elementos de treino supervisionado e não supervisionado e com arquitecturas típicas de autoencoders e redes neuronais feedforward aplicadas à descoberta da topologia de um sistema IEEE RTS 24. Para além do propósito de contrastar a eficácia de cada um dos métodos, serão igualmente demonstradas comparações no que concerne o peso e rapidez computacional através da utilização de CPU e GPU de modo a atestar as potencialidades desta última técnica em aplicações concretas nos sistemas eléctricos de energia.Since its massive deploy in power systems' control centres, the state estimator is regarded as a tool of critical importance due to its capability of determining a system operating point with remarkable precision. Indeed, one of the essential stages to obtain that operating point is identifying the system topology, i.e., the open or closed status of the grid's switches and breakers.As a result, a considerable amount of methodologies was proposed with the purpose of detecting the correct topology. First efforts related to this subject date back to the 80's and preconized the use of strict mathematical equations and, subsequently, the analysis of their results to evaluate possible topology errors. However, other techniques showed the advantages of the application of artificial neural networks to this particular problem; among them, figure the fast and reliable results and immunity to convergence problems of mathematical, analytic methods.Hence, this thesis presents a novel perspective of the application of neural networks, deep learning and Information Theoretic Learning (ITL) to the problem of topology determination and, ultimately, to state estimation. Blending these concepts, the main objective is to predict the topology of a given power system based on pattern recognition of analog measurements, pointing, at the same time, alternative approaches on neural networks' training and architecture as well as computational tools capable of introducing enhanced efficiency to that process.With more detail, two different applications with elements of supervised and unsupervised training as well as with autoencoder architectures and typical feedforward neural networks will be explored to discover the topology of an IEEE RTS 24 test case. Apart from comparisons between their efficacy, other major point will be the contrasts between running times achieved by CPU and GPU computation, showing that the large spectrum of application of this last technique comprises also power systems

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks