Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data

With the support of Internet of Things (IoT) devices, it is possible to acquire data from degradation phenomena and design data-driven models to perform anomaly detection in industrial equipment. This approach not only identifies potential anomalies but can also serve as a first step toward building predictive maintenance policies. In this work, we demonstrate a novel anomaly detection system on induction motors used in pumps, compressors, fans, and other industrial machines. This work evaluates a combination of pre-processing techniques and machine learning (ML) models with a low computational cost. We use a combination of pre-processing techniques such as Fast Fourier Transform (FFT), Wavelet Transform (WT), and binning, which are well-known approaches for extracting features from raw data. We also aim to guarantee an optimal balance between multiple conflicting parameters, such as anomaly detection rate, false positive rate, and inference speed of the solution. To this end, multiobjective optimization and analysis are performed on the evaluated models. Pareto-optimal solutions are presented to select which models have the best results regarding classification metrics and computational effort. Differently from most works in this field that use publicly available datasets to validate their models, we propose an end-to-end solution combining low-cost and readily available IoT sensors. The approach is validated by acquiring a custom dataset from induction motors. Also, we fuse vibration, temperature, and noise data from these sensors as the input to the proposed ML model. Therefore, we aim to propose a methodology general enough to be applied in different industrial contexts in the future

Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping

Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection. However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, the current systematic mapping studies on Anomaly Detection primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, these studies do not cover the challenges involved in using ML for Anomaly Detection in industrial machinery within the context of the IoT ecosystems. This paper presents a systematic mapping study on Anomaly Detection for industrial machinery using IoT devices and ML algorithms to address this gap. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of Anomaly Detection research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities

Detecção de Escherichia coli e coliformes totais e termotolerantes em sushis comercializados nos shoppings da cidade de Belém

In the metropolitan region of Belém, the consumption of foods from Japanese cuisine has been growing, this is evident from the growth in points selling this product in the city's shopping malls. This has awakened the need to find viable quality control methodologies for this product, in order to avoid outbreaks of food poisoning. The fact that sushi preparation involves extensive manual manipulation and is consumed without heating, presents a high risk of contamination by microorganisms that indicate microbiological contamination. Therefore, the objective of the present work was to detect the presence of Escherichia coli and total and thermotolerant coliforms in sushi sold in shopping malls in the city of Belém. The COLItest® commercial kit was used and it was concluded that 100% of the samples analyzed were if contaminated with total and thermotolerant coliforms.Na região metropolitana de Belém o consumo de alimentos oriundos da culinária japonesa vem crescendo, isso fica evidente pelo crescimento dos pontos de comercialização desse produto pelos shoppings da cidade. Isso tem despertado a necessidade de se encontrar metodologias viáveis de controle de qualidade desse produto, a fim de evitar surtos de toxinfecção alimentar. O fato de o preparo do sushi envolver elevada manipulação manual e ser consumido sem aquecimento, apresenta alto risco de contaminação por micro-organismos indicadores de contaminação microbiológica. Dessa forma, o objetivo do presente trabalho foi detectar a presença de Escherichia coli e coliformes totais e termotolerantes em sushis comercializados nos shoppings da cidade de Belém. Utilizou-se o kit comercial COLItest® e conclui-se que 100% das amostras analisadas encontram-se contaminadas com coliformes totais e termotolerantes

A Mapping Study of Machine Learning Methods for Remaining Useful Life Estimation of Lead-Acid Batteries

Energy storage solutions play an increasingly important role in modern infrastructure and lead-acid batteries are among the most commonly used in the rechargeable category. Due to normal degradation over time, correctly determining the battery's State of Health (SoH) and Remaining Useful Life (RUL) contributes to enhancing predictive maintenance, reliability, and longevity of battery systems. Besides improving the cost savings, correct estimation of the SoH can lead to reduced pollution though reuse of retired batteries. This paper presents a mapping study of the state-of-the-art in machine learning methods for estimating the SoH and RUL of lead-acid batteries. These two indicators are critical in the battery management systems of electric vehicles, renewable energy systems, and other applications that rely heavily on this battery technology. In this study, we analyzed the types of machine learning algorithms employed for estimating SoH and RUL, and evaluated their performance in terms of accuracy and inference time. Additionally, this mapping identifies and analyzes the most commonly used combinations of sensors in specific applications, such as vehicular batteries. The mapping concludes by highlighting potential gaps and opportunities for future research, which lays the foundation for further advancements in the field

Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks

Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain

Dinâmica espacial dos parâmetros físicos e químicos da água em viveiros de piscicultura

O Brasil apresenta características que podem fazer do seu território o grande celeiro mundial para produção de organismos piscícolas cultivados. No entanto, para isso, é fundamental estudar os parâmetros físico e químico da água das áreas de cultivo, para que se possam buscar estratégias para evitar erros de manejos. Com isso, objetivou-se avaliar a dinâmica espacial dos parâmetros físicos e químicos da água de viveiros de piscicultura e sua relação de dependência. O estudo foi realizado em um viveiro escavado em terreno natural, localizado em uma propriedade, adjacente à fazenda experimental da escola de Medicina Veterinária e Zootecnia, da Universidade Federal do Tocantins, no município de Araguaína. Os pontos para coleta no grid foram obtidos através de GPS, considerando as dimensões da área total. Em cada ponto georreferenciado, foram realizadas coletas em duas profundidades diferentes, uma a 20 cm da superfície da água (profundidade 1), e outra a 20 cm do fundo do tanque (profundidade 2), totalizando 108 amostras. Ocorreu grande variabilidade entre as profundidades para maioria das variáveis. Na estatística descritiva foi observado coeficiente de variação de grau moderado apenas para oxigênio dissolvido. Na análise dos semivariogramas, apenas a variável condutividade elétrica demonstrou efeito pepita puro. Foi verificada dependência espacial entre as variáveis estudadas nas duas profundidades. Através da geoestatística, foi possível evidenciar alteração na distribuição espacial dos parâmetros físicos e químicos: oxigênio dissolvido, temperatura da água e pH, mas não foi útil para condutividade elétrica, dada sua baixa variabilidade espacial. Os parâmetros físicos e químicos da água dos tanques de piscicultura são influenciados pelas práticas de manejo, sendo facilmente detectado através dos mapas de isolinhas. Spatial dynamics of the physical and chemical parameters of water in fish farms Abstract: Brazil presents, characteristics that can make of its territory, the world's great barn for the production of cultivated fish organisms. However, for this it is fundamental to study the physical and chemical parameters of the water in the growing areas, so that strategies can be sought to avoid handling errors. The aim of this study was to evaluate the spatial dynamics of the physical and chemical parameters of fish pond water and their relationship of dependence. The study was carried out in a nursery excavated on natural land, located on a property, adjacent to the experimental farm of the Veterinary Medicine and Animal Science School, Federal University of Tocantins, in the municipality of Araguaína. The points for collection on the grid were obtained by GPS, considering the size of the total area. In each georeferenced point samplings were performed in two different depths, one 20 cm from the tank bottom (depth first) and the other at 20 cm from the water surface (depth 2) totaling 108 samples. There was major variation between depths for most variables. Descriptive statistics was observed moderate variation coefficient only for dissolved oxygen. In the analysis of semivariograms, only the variable electrical conductivity showed pure nugget effect. spatial dependence was found between the variables at both depths. The mesh used was effective in showing the spatial distribution of physical - chemical parameters: dissolved oxygen, water temperature and pH, but it was not useful for electrical conductivity, given its low spatial variability. The physical and chemical parameters of the water of the fish ponds are influenced by the management practices, being easily detected through the isoline maps

Dinâmica espacial dos parâmetros físicos e químicos da água em viveiros de piscicultura

Brazil presents, characteristics that can make of its territory, the world's great barn for the production of cultivated fish organisms. However, for this it is fundamental to study the physical and chemical parameters of the water in the growing areas, so that strategies can be sought to avoid handling errors. The aim of this study was to evaluate the spatial dynamics of the physical and chemical parameters of fish pond water and their relationship of dependence. The study was carried out in a nursery excavated on natural land, located on a property, adjacent to the experimental farm of the Veterinary Medicine and Animal Science School, Federal University of Tocantins, in the municipality of Araguaína. The points for collection on the grid were obtained by GPS, considering the size of the total area. In each georeferenced point samplings were performed in two different depths, one 20 cm from the tank bottom (depth first) and the other at 20 cm from the water surface (depth 2) totaling 108 samples. There was major variation between depths for most variables. Descriptive statistics was observed moderate variation coefficient only for dissolved oxygen. In the analysis of semivariograms, only the variable electrical conductivity showed pure nugget effect. spatial dependence was found between the variables at both depths. The mesh used was effective in showing the spatial distribution of physical - chemical parameters: dissolved oxygen, water temperature and pH, but it was not useful for electrical conductivity, given its low spatial variability. The physical and chemical parameters of the water of the fish ponds are influenced by the management practices, being easily detected through the isoline maps.O Brasil apresenta características que podem fazer do seu território o grande celeiro mundial para produção de organismos piscícolas cultivados. No entanto, para isso, é fundamental estudar os parâmetros físico e químico da água das áreas de cultivo, para que se possam buscar estratégias para evitar erros de manejos. Com isso, objetivou-se avaliar a dinâmica espacial dos parâmetros físicos e químicos da água de viveiros de piscicultura e sua relação de dependência. O estudo foi realizado em um viveiro escavado em terreno natural, localizado em uma propriedade, adjacente à fazenda experimental da escola de Medicina Veterinária e Zootecnia, da Universidade Federal do Tocantins, no município de Araguaína. Os pontos para coleta no grid foram obtidos através de GPS, considerando as dimensões da área total. Em cada ponto georreferenciado, foram realizadas coletas em duas profundidades diferentes, uma a 20 cm da superfície da água (profundidade 1), e outra a 20 cm do fundo do tanque (profundidade 2), totalizando 108 amostras. Ocorreu grande variabilidade entre as profundidades para maioria das variáveis. Na estatística descritiva foi observado coeficiente de variação de grau moderado apenas para oxigênio dissolvido. Na análise dos semivariogramas, apenas a variável condutividade elétrica demonstrou efeito pepita puro. Foi verificada dependência espacial entre as variáveis estudadas nas duas profundidades. Através da geoestatística, foi possível evidenciar alteração na distribuição espacial dos parâmetros físicos e químicos: oxigênio dissolvido, temperatura da água e pH, mas não foi útil para condutividade elétrica, dada sua baixa variabilidade espacial. Os parâmetros físicos e químicos da água dos tanques de piscicultura são influenciados pelas práticas de manejo, sendo facilmente detectado através dos mapas de isolinhas

Benchmarking machine learning models to assist in the prognosis of tuberculosis

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare Citation: Lino Ferreira da Silva Barros, M.H.; Oliveira Alves, G.; Morais Florêncio Souza, L.; da Silva Rocha, E.; Lorenzazto de Oliveira, J.F.; Lynn, T.; Sampaio, V.; Endo, P.T. Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis. Informatics 2021, 8, 27. https://doi.org/10.3390/ informatics8020027 Academic Editors: Renato Umeton and Gregory Antell Received: 8 March 2021 Accepted: 9 April 2021 Published: 15 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class