Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This paper conducts a comprehensive literature review of ML-based RT-FDD. Out of 805 documents, 29 studies were identified as noteworthy for presenting innovative methods that address the complexities and challenges associated with fault detection. While ML-based RT-FDD offers different benefits, including fault prediction accuracy, it faces challenges in data quality, model interpretability, and integration complexities. This review identifies a gap in industrial implementation outcomes that opens new research opportunities. Future Fault Detection and Diagnosis (FDD) research may prioritize standardized datasets to ensure reproducibility and facilitate comparative evaluations. Furthermore, there is a pressing need to refine techniques for handling unbalanced datasets and improving feature extraction for temporal series data. Implementing Explainable Artificial Intelligence (AI) (XAI) tailored to industrial fault detection is imperative for enhancing interpretability and trustworthiness. Subsequent studies must emphasize comprehensive comparative evaluations, reducing reliance on specialized expertise, documenting real-world outcomes, addressing data challenges, and bolstering real-time capabilities and integration. By addressing these avenues, the field can propel the advancement of ML-based RT-FDD methodologies, ensuring their effectiveness and relevance in industrial contexts.

Anomaly detection is a critical issue across several academic fields and real-world applications. Artificial neural networks have been proposed to detect anomalies from different input types, but there is no clear guide to deciding which model to use in a specific case. Therefore, this study examines the most relevant Neural Network Outlier Detection algorithms in the literature, compares their benefits and drawbacks in some application scenarios, and displays their outcomes in benchmark datasets. The initial search revealed 1422 papers on projects completed between 2017 and 2021. These papers were further narrowed based on title, abstract, quality assessment, inclusion, and exclusion criteria, remaining 76 articles. Finally, we reviewed these publications and verified that Autoencoder Neural Network, Convolutional Neural Network, Recurrent Neural Network, and Generative Adversarial Network have promisor outcomes for outlier detection, the advantages of these neural networks for outlier detection, and the significant challenges of outlier detection strategies.

This work presents a novel Automated Machine Learning (AutoML) approach for Real-Time Fault Detection and Diagnosis (RT-FDD). The approach’s particular characteristics are: it uses only data that are commonly available in industrial automation systems; it automates all ML processes without human intervention; a non-ML expert can deploy it; and it considers the behavior of cyclic sequential machines, combining discrete timed events and continuous variables as features. The capacity for fault detection is analyzed in two case studies, using data from a 3D machine simulation system with faulty and non-faulty conditions. The enhancement of the RT-FDD performance when the proposed approach is applied is proved with the Feature Importance, Confusion Matrix, and F1 Score analysis, reaching mean values of 85% and 100% in each case study. Finally, considering that faults are rare events, the sensitivity of the models to the number of faulty samples is analyzed.

Increasing wind generation insertion levels on electrical grids through power converters may cause instabilities in the AC grid due to the intermittent wind nature. Integrating a Battery Electric Energy Storage System (BESS) in wind generation can smooth the power injection at the Common Coupling Point (PCC), contributing to the power system voltage and frequency stability. In this article, it is proposed to analyze the operation of a lithium-ion battery technology based 1 MW/1.29 MWh BESS connected in parallel with wind generation with a capacity of 50.4 MW. The main characteristics investigated are power smoothing and power factor correction. Experimental results show that BESS contributes to smoothing the active power and correcting the power factor of wind generation, improving the quality of electrical energy at the PCC.

This paper presents an analysis using hierarchical grouping method (ward grouping) and the non-hierarchical grouping method (k-means) to analyze the participation levels in activities and interactions in a virtual forum. Data came from a MOOC and it was on grammatical rules of Brazilian Portuguese. About 5100 participants integrated the course. It lasted about three months and the activities developed by means of the Openredu learning platform. We analyzed data of collaboration, interaction, and discussions in forums, access data and activity on the Openredu. The results pointed to three distinct engagement strategies. Those categories oriented the proposition of an interface design guidelines for MOOC to conceive adaptive strategies that permit to increase engagement and favor an improved learning experience.

The search for charging electric vehicles using renewable energy sources and ensuring the stability of the electrical system has been growing. This has led to the development of charging stations that integrate photovoltaic systems, energy storage systems in batteries, and charging stations. The storage system allows for applications that increase the reliability of the electrical network. The objective of this work is to study, based on real operational scenarios, the impacts, and criteria of energy quality of a charging solution containing a photovoltaic system, a storage system, and a charging station. In the solution, the batteries are lead-carbon type and photovoltaics have priority in energy supply, using excess generation to charge the batteries. In this aspect, the work presents relevant contributions to the study of energy quality. An improvement in energy quality is pointed out with the use of batteries, proving that even in the most diverse operating situations, such as partial shading of the photovoltaic modules, with 20% shading relative to the nominal power, charging is not impacted and reductions in distortions are achieved, with a 50% reduction in voltage imbalance, when the storage system is a voltage reference and reference to the microgrid.