Search Results (198)

Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence. Full article

Large-scale battery datasets often contain anomalous data due to sensor noise, communication errors, and operational inconsistencies, which degrade the accuracy of data-driven prognostics. However, many existing studies overlook the impact of such anomalies or apply filtering heuristically without rigorous benchmarking, which can potentially introduce biases into training and evaluation pipelines. This study presents a deep learning framework that integrates autoencoder-based anomaly detection with a residual neural network (ResNet) to achieve state-of-the-art prediction of remaining useful life at the cycle level using only a single-cycle input. The framework systematically filters out anomalous samples using multiple variants of convolutional and sequence-to-sequence autoencoders, thereby enhancing data integrity before optimizing and training the ResNet-based models. Benchmarking against existing deep learning approaches demonstrates a significant performance improvement, with the best model achieving a mean absolute percentage error of 2.85% and a root mean square error of 40.87 cycles, surpassing prior studies. These results indicate that autoencoder-based anomaly filtering significantly enhances prediction accuracy, reinforcing the importance of systematic anomaly detection in battery prognostics. The proposed method provides a scalable and interpretable solution for intelligent battery management in electric vehicles and energy storage systems. Full article

Infrared thermography is a common approach used in building inspection for identifying building envelope thermal anomalies that cause energy loss and occupant thermal discomfort. Detecting these anomalies is essential to improve the thermal performance of energy-inefficient buildings through energy retrofit design and correspondingly reduce operational energy costs and environmental impacts. A thermal bridge is an unwanted conductive heat transfer. On the other hand, an infiltration/exfiltration anomaly is an uncontrollable convective heat transfer, typically happening around windows and doors, but it can also be due to a defect that comprises a building envelope’s integrity. While the existing literature underscores the significance of automatic thermal anomaly identification and offers insights into automated methodologies, there is a notable gap in addressing an automated workflow that leverages building envelope component segmentation for enhanced detection accuracy. Consequently, an automatic thermal anomaly identification workflow from visible and thermal images was developed to test it, utilizing segmented building envelope information compared to a workflow without any semantic segmentation. Therefore, building envelope images (e.g., walls and windows) were segmented based on a U-Net architecture compared to a more conventional semantic segmentation approach. The results were discussed to better understand the importance of the availability of training data and for scaling the workflow. Then, thermal anomaly thresholds for different target domains were detected using probability distributions. Finally, thermal anomaly masks of those domains were computed. This study conducted a comprehensive examination of a campus building in Syracuse, New York, utilizing a drone-based data collection approach. The case study successfully detected diverse thermal anomalies associated with various envelope components. The proposed approach offers the potential for immediate and accurate in situ thermal anomaly detection in building inspections. Full article

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

Today, coastal storms represent one of the most formidable environmental challenges, causing significant impacts on coastal communities. This situation underscores both the importance and urgency of studying storms and their characterization. This study proposes an innovative approach combining Principal Component Analysis (PCA) and machine learning (Classification and Regression Trees, CART) to characterize and distinguish damaging storms from non-damaging ones along the coast of Dakar, Senegal. The analysis revealed that among several hydrometeorological variables studied (wave height, period, direction, runup, wave energy, sea level anomaly, tide, etc.), the variables SLA and tide play a central role in the occurrence of damage, although they are weakly correlated with the others. By cross-analyzing these variables, critical thresholds were established, such as Tide > 0.53 m combined with SLA ≥ 0.061 m, Tide > 0.53 m and ECWL ≥ 1.3 m, as well as Runup ≤ 0.64 m associated with a high wave period (Tp), allowing accurate differentiation of potentially damaging storms. The CART method validated these results and identified three key combinations: (1) Tide–SLA, where no damage is observed if Tide < 0.53 m, and damage occurs beyond this threshold when SLA ≥ 0.061 m; (2) Tide–ECWL, where storms are damaging if Tide > 0.53 m and ECWL ≥ 1.3 m; (3) Runup–Tp, where storms are damaging if Runup ≤ 0.64 m or if Runup > 0.82 m with Tp ≥ 16 s. These results constitute the first application of machine learning for storm classification on the Senegalese coast, providing a novel quantitative foundation for better understanding the hydrodynamic conditions associated with damaging storms. The findings of this study could be valuable for risk management and the development of early warning systems Full article

Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies. Full article

The increasing deployment of wind energy systems, particularly offshore wind farms, necessitates advanced monitoring and maintenance strategies to ensure optimal performance and minimize downtime. Supervisory Control And Data Acquisition (SCADA) systems have become indispensable tools for monitoring the operational health of wind turbines, generating vast quantities of time series data from various sensors. Anomaly detection techniques applied to this data offer the potential to proactively identify deviations from normal behavior, providing early warning signals of potential component failures. Traditional model-based approaches for fault detection often struggle to capture the complexity and non-linear dynamics of wind turbine systems. This has led to a growing interest in data-driven methods, particularly those leveraging machine learning and deep learning, to address anomaly detection in wind energy applications. This study focuses on the development and application of a semi-supervised, multivariate anomaly detection model for horizontal axis wind turbines. The core of this study lies in Bidirectional Long Short-Term Memory (BI-LSTM) networks, specifically a BI-LSTM autoencoder architecture, to analyze time series data from a SCADA system and automatically detect anomalous behavior that could indicate potential component failures. Moreover, the approach is reinforced by the integration of the Isolation Forest algorithm, which operates in an unsupervised manner to further refine normal behavior by identifying and excluding additional anomalous points in the training set, beyond those already labeled by the data provider. The research utilizes a real-world dataset provided by EDP Renewables, encompassing two years of comprehensive SCADA records collected from a single offshore wind turbine operating in the Gulf of Guinea. Furthermore, the dataset contains the logs of failure events and recorded alarms triggered by the SCADA system across a wide range of subsystems. The paper proposes a multi-modal anomaly detection framework orchestrating an unsupervised module (i.e., decision tree method) with a supervised one (i.e., BI-LSTM AE). The results highlight the efficacy of the BI-LSTM autoencoder in accurately identifying anomalies within the SCADA data that exhibit strong temporal correlation with logged warnings and the actual failure events. The model’s performance is rigorously evaluated using standard machine learning metrics, including precision, recall, F1 Score, and accuracy, all of which demonstrate favorable results. Further analysis is conducted using Cumulative Sum (CUSUM) control charts to gain a deeper understanding of the identified anomalies’ behavior, particularly their persistence and timing leading up to the failures. Full article

This paper proposes a non-invasive, data-driven methodology for monitoring and optimising machine utilisation in manufacturing environments. By analysing high-resolution power consumption data, the system automatically classifies machine states (off, idling, and working, and segments operational periods into discrete production events. Unsupervised learning techniques enable the identification of production patterns, product typologies, and anomalies, supporting improvements in operational efficiency and quality control. The approach also estimates energy consumption and cost using time-of-use tariffs, offering insights into both performance and sustainability. Experimental evaluations across multiple industrial settings demonstrate the method’s robustness, with high agreement with production records and significant potential for reducing idle time, improving scheduling, and enhancing resource allocation. This work presents a scalable and interpretable analytics framework to support data-driven decision-making in modern manufacturing operations. Full article

The performance degradation of photovoltaic (PV) systems, comprising solar panels and DC-DC converters, is often caused by various anomalies related to manufacturing defects, operational conditions, or environmental factors. These faults significantly reduce energy output, preventing the system from reaching its nominal power and expected production levels. Given the demonstrated impact of such faults on PV system efficiency, an effective diagnostic method is essential for proactive maintenance and optimal performance. This paper presents a fault detection algorithm based on a Mamdani-type fuzzy logic approach. The proposed method utilizes three key inputs—panel current, panel voltage, and converter voltage—to assess system health. By computing the distortion ratios of these electrical parameters and processing them through a fuzzy logic controller, the algorithm accurately identifies fault conditions. Simulation results validate the effectiveness of this approach, demonstrating its capability to detect and classify 12 distinct faults in both the PV array and the DC-DC converter. The study highlights the potential of fuzzy logic-based diagnostics in enhancing the reliability and maintenance of photovoltaic systems. Full article

The growing reliance on natural gas in urban China has heightened the urgency of maintaining pipeline integrity, particularly in environments prone to disruption by nearby construction activities. In this study, we present a practical approach for the real-time classification of distributed fiber optic monitoring signals, leveraging a hybrid framework that combines the feature learning capacity of a one-dimensional convolutional neural network (1D-CNN) with the classification robustness of a support vector machine (SVM). The proposed method effectively distinguishes various pipeline-related events—such as minor leakage, theft attempts, and human movement—by automatically extracting their vibration patterns. Notably, it addresses the common shortcomings of softmax-based classifiers in small-sample scenarios. When tested on a real-world dataset collected via the DAS3000 system from Hangzhou Optosensing Co., Ltd., the model achieved a high classification accuracy of 99.92% across six event types, with an average inference latency of just 0.819 milliseconds per signal. These results demonstrate its strong potential for rapid anomaly detection in pipeline systems. Beyond technical performance, the method offers three practical benefits: it integrates well with current monitoring infrastructures, significantly reduces manual inspection workloads, and provides early warnings for potential pipeline threats. Overall, this work lays the groundwork for a scalable, machine learning-enhanced solution aimed at ensuring the operational safety of critical energy assets. Full article

Ensuring the safe, reliable, and energy-efficient provision of electricity is a complex task for smart grid (SG) management applications. Internet of Things (IoT) and edge computing-based SG applications have been proposed for time-responsive monitoring and controlling tasks related to power systems. Recent studies have provided valuable insights into the potential of machine learning algorithms in SGs, covering areas such as generation, distribution, microgrids, consumer energy market, and cyber security. Integrated IoT devices directly exchange data with the SG cloud, which increases the vulnerability and security threats to the energy system. The review aims to provide a comprehensive analysis of privacy-preserving machine learning (PPML) applications in IoT-Integrated SGs, focusing on non-intrusive load monitoring, fault detection, demand forecasting, generation forecasting, energy-management systems, anomaly detection, and energy trading. The study also highlights the importance of data privacy and security when integrating these applications to enable intelligent decision-making in smart grid domains. Furthermore, the review addresses performance issues (e.g., accuracy, latency, and resource constraints) associated with PPML techniques, which may impact the security and overall performance of IoT-integrated SGs. The insights of this study will provide essential guidelines for in-depth research in the field of IoT-integrated smart grid privacy and security in the future. Full article

The advent of Tiny Machine Learning (TinyML) has unlocked the potential to deploy machine learning models on resource-constrained edge devices, revolutionizing real-time monitoring in Internet of Medical Things (IoMT) applications. This study introduces a novel approach to real-time electrocardiogram (ECG) anomaly detection by integrating TinyML with edge Artificial Intelligence (AI) on low-power embedded systems. We demonstrate the feasibility and effectiveness of deploying optimized models on edge devices, such as the Raspberry Pi and Arduino, to detect ECG anomalies, including arrhythmias. The proposed workflow encompasses data preprocessing, feature extraction, and model inference, all executed directly on the edge device, eliminating the need for cloud resources. To address the constraints of memory and power consumption in wearable devices, we applied advanced optimization techniques, including model pruning and quantization, achieving an optimal balance between accuracy and resource utilization. The optimized model achieved an accuracy of 92.3% while reducing the power consumption to 0.024 mW, enabling continuous, long-term health monitoring with minimal energy requirements. This work highlights the potential of TinyML to advance edge AI for real-time medical applications. Full article

The rapid proliferation of IoT devices necessitates robust security measures to protect against malicious traffic. Anomaly detection, primarily through traffic classification supported by artificial intelligence and machine learning techniques, has emerged as a practical approach to enhancing IoT network security. Effective traffic classification requires efficient feature selection, which is critical for resource-constrained IoT devices with limited computational power, memory, and energy. This study proposes Statistical Moments Difference Thresholding, a feature selection method leveraging statistical central moments to identify significant features distinguishing between legitimate and malicious traffic. The aim is to reduce feature dimensionality while maintaining high detection accuracy. Validated on the MQTTset dataset through binary and multiclass classification using seven ML algorithms, the results highlight its ability to enhance computational efficiency without compromising performance, showcasing its potential in real-world IoT security applications. Full article

The Yinchuan Basin harbors significant geothermal resource potential and could be a clean energy source critical for transitioning to a low-carbon economy. However, the current research primarily focuses on the exploration and development of geothermal water in the sedimentary basins, with limited studies on the deep geothermal formation mechanisms and regional geothermal types. Although geophysical methods provide insights into the types and formation mechanisms of deep geothermal resources in the basin, there is still a lack of a connection between quantitative understanding and direct evidence. A series of algorithms based on thermal parameter characteristics can directly extract underground thermal features from raw geophysical signal data, offering a powerful tool for characterizing the structure and aggregation patterns of deep thermal sources. Therefore, this study employed a Bayesian thermal parameter inversion method based on interface information to obtain the spatial distribution of thermal conductivity, surface heat flow, and mantle heat parameters in the Ningxia Basin study area. Additionally, correlation analysis and global sensitivity analysis were conducted to further interpret the predicted results. A comprehensive analysis of the geophysical inversion results showed that the deep thermal anomalies in the basin are primarily controlled by fault activities and the lithospheres’ thermal structure, while shallow high-heat flow anomalies are closely related to convective circulation within faults and heat transfer from deep thermal sources. The established geothermal genesis mechanism and model of the Yinchuan Basin provide crucial support for sustainable regional geothermal development planning and the utilization of deep geothermal resources, contributing to energy security and emission reduction goals. Full article