Enhanced Fault Detection Research Articles

Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels

Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumvent specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporated denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. The method was evaluated using cross-validation on simulated satellite data comprising 1000 time series samples and across different fault scenarios, such as stiction and resonance at varying intensities (90%, 50%, and 30%). The results confirm achieving performance metrics such as Mean Squared Error for accurate fault identification. This research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions.

Enhanced Fault Detection in Photovoltaic Panels Using CNN-Based Classification with PyQt5 Implementation.

Solar photovoltaic systems have increasingly become essential for harvesting renewable energy. However, as these systems grow in prevalence, the issue of the end of life of modules is also increasing. Regular maintenance and inspection are vital to extend the lifespan of these systems, minimize energy losses, and protect the environment. This paper presents an innovative explainable AI model for detecting anomalies in solar photovoltaic panels using an enhanced convolutional neural network (CNN) and the VGG16 architecture. The model effectively identifies physical and electrical changes, such as dust and bird droppings, and is implemented using the PyQt5 Python tool to create a user-friendly interface that facilitates decision-making for users. Key processes included dataset balancing through oversampling and data augmentation to expand the dataset. The model achieved impressive performance metrics: 91.46% accuracy, 98.29% specificity, and an F1 score of 91.67%. Overall, it enhances power generation efficiency and prolongs the lifespan of photovoltaic systems, while minimizing environmental risks.

Research on Fault Diagnosis Method for Photovoltaic Array Based on XGBoost Algorithm

INTRODUCTION: Photovoltaic (PV) energy sources frequently experience issues, including fragmentation, open-circuit, short-circuiting, and other common and hazardous problems. The current focus of PV research is on fault detection within solar arrays. Traditional models encounter challenges in identifying errors due to uncertainties in panel settings and the complex nature of the actual PV structure.OBJECTIVES: This study aims to introduce a novel Extreme Gradient Boosting (XGBoost) approach for fault diagnosis in PV arrays.METHODS: The XGBoost algorithm is trained using collected PV array defect data samples. Data preprocessing is performed to manage missing values and remove noisy data. Feature extraction is conducted using Linear Discriminant Analysis (LDA) to improve detection accuracy. To further enhance XGBoost’s performance, the World Cup Optimization (WCO) approach is applied to select optimal features from the extracted data. Fault detection is then conducted using the XGBoost algorithm on the processed data. Various indicators are utilized for performance assessment within the Python environment.RESULTS: The comparative analysis demonstrates that this research improves fault detection efficiency in PV arrays compared to existing methodologies.CONCLUSION: The study presents an effective method for enhancing fault detection in PV systems, showcasing the advantages of the XGBoost and WCO-based approach over conventional methods.

Enhancing fault detection and predictive maintenance of rotating machinery with Fiber Bragg Grating sensor and machine learning techniques

Enhancing fault detection and predictive maintenance of rotating machinery with Fiber Bragg Grating sensor and machine learning techniques

Enhancing Fault Detection and Location Identification using GIS-based Techniques: A Case Study of Arba Minch Distribution System

ABSTRACT Fault detection and location in distribution systems are critical for maintaining reliable electric power supply and minimizing outage time. The Arba Minch District distribution department of Ethiopian Electric Utility (EEU) currently employs a manual trial-and-error approach for fault detection and location, which is inefficient, time-consuming, and ineffective in resolving faults. This study evaluates the current approach and proposes an enhanced system utilizing Geographic Information System (GIS) technology to improve fault detection and location in the Arba Minch distribution system, specifically the Shecha feeder. The implementation of GIS-based fault detection and location aims to address the limitations of the manual method and mitigate economic losses associated with feeder faults. ETAP GIS simulations successfully detected and located faults, achieving effective reduction in fault detection and location time compared to the conventional method. By optimizing fault detection and location time, the System Average Interruption Duration Index (SAIDI) for the feeder was significantly reduced. Consequently, the GIS system highly minimizes the economic losses associated with energy not supplied. The results demonstrate the potential of GIS technology in enhancing fault detection and location, ultimately improving the reliability and efficiency of the distribution system.

Integrating Deep Learning and Energy Management Standards for Enhanced Solar–Hydrogen Systems: A Study Using MobileNetV2, InceptionV3, and ISO 50001:2018

This study addresses the growing need for effective energy management solutions in university settings, with particular emphasis on solar–hydrogen systems. The study’s purpose is to explore the integration of deep learning models, specifically MobileNetV2 and InceptionV3, in enhancing fault detection capabilities in AIoT-based environments, while also customizing ISO 50001:2018 standards to align with the unique energy management needs of academic institutions. Our research employs comparative analysis of the two deep learning models in terms of their performance in detecting solar panel defects and assessing accuracy, loss values, and computational efficiency. The findings reveal that MobileNetV2 achieves 80% accuracy, making it suitable for resource-constrained environments, while InceptionV3 demonstrates superior accuracy of 90% but requires more computational resources. The study concludes that both models offer distinct advantages based on application scenarios, emphasizing the importance of balancing accuracy and efficiency when selecting appropriate models for solar–hydrogen system management. This research highlights the critical role of continuous improvement and leadership commitment in the successful implementation of energy management standards in universities.

Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems.

Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model

Enhanced Fault Detection in Solar Photovoltaic Modules Using VMD-LSTM Model

A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults

Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

Enhanced fault detection in energy systems using individual contextual forgetting factors in recursive principal component analysis

Improving maintenance strategies is an important component in reducing wastes of energy in industrial systems and especially energy systems, as they represent a large proportion of our total energy use. The use of fault detection methods can help transition to proactive maintenance methods and automate the detection of faults using data from the systems themselves. However, the presence of normal operational changes must be accounted for to avoid false alarms, as static fault detection methods cannot handle them. A recursive fault detection method, specifically recursive principal component analysis (RPCA), is applied to real building data in this paper to aid in addressing this challenge. Additionally, improvements to the aspect of updating forgetting factors, which are integral to RPCA's functionality, is also proposed which allows for enhanced adaptability. The results show that the improvement increases adaptability when setpoints are changed and provides similar performance otherwise. Lastly, the application of this was shown to significantly reduce false alarms in the building application, while still detecting the known faults.

Deep learning-based proactive fault detection method for enhanced quadrotor safety

The early detection of faults in advanced technological systems is imperative for ensuring operational reliability and safety. While there is a growing interest in using artificial intelligence for fault detection, current methodologies often exhibit limitations in utilizing comprehensive system information and sensor data. Hidden faults within collected data further highlight the need for advanced analysis techniques. This study introduces a novel deep learning-based framework designed to predict faults and extract insights from complex system datasets. The model, consisting of LSTM-autoencoder and BiLSTM classification components, effectively reduces feature dimensions, thereby enhancing fault detection accuracy. The autoencoder’s latent layer identifies prominent features across various dimensions, while BiLSTM classification conducts bidirectional analysis using these features from both healthy and faulty states, facilitating early fault detection. Experimental results demonstrate the model’s efficacy, achieving an accuracy of 79.48% in predicting incipient faults 30 seconds before a serious malfunction occurs. This underscores the significant potential of the proposed framework in enhancing operational safety and reliability in complex systems. Moreover, the study emphasizes the importance of leveraging comprehensive data and advanced analysis techniques for early fault detection.

Research on Multi-Sensor Fusion Fault Diagnosis Method Based on Spatiotemporal Attention Mechanism

As industrial systems become increasingly complex, real-time monitoring and intelligent management of industrial equipment have become imperative. However, the limitations in coverage and accuracy of single sensors make it challenging to comprehensively characterize the operational state of equipment, leading to reduced system reliability and increased pressures on data transmission and storage. To address these challenges, this study presents a novel fault diagnosis method based on multi-sensor fusion using a spatio-temporal attention mechanism. Initially, one-dimensional convolutional neural networks (1D-CNN) are employed to extract features from raw signals, effectively capturing local characteristics and ensuring the integrity and validity of fault signals. Subsequently, the spatiotemporal attention mechanism adjusts the feature weights based on the temporal and spatial correlations of different sensors, as well as their respective importance, thereby capturing the spatio-temporal dependencies across multiple sensors and enhancing the efficacy of information fusion. Finally, the proposed method is validated through experiments on a nickel flash smelting furnace system. The results demonstrate that the method achieves a fault diagnosis accuracy exceeding 97.78%, significantly enhancing fault detection and decision-making performance.

Mathematical Analysis of Novel Method to Solve Protection Issues Pertaining To Solar PV Integration

The integration of Solar Photovoltaic (PV) systems into modern power grids presents protection challenges, such as voltage fluctuations, fault detection complexities, and bidirectional power flow issues. These challenges compromise grid reliability, requiring advanced methods to address them. This study introduces a novel mathematical analysis approach to solve protection issues in solar PV integration, focusing on developing an adaptive protection scheme using advanced mathematical models. The proposed approach utilizes differential equations, optimization techniques, and machine learning algorithms to create dynamic protection settings responsive to varying grid conditions. The method integrates fault detection and classification algorithms based on wavelet transform and support vector machines (SVM), allowing for rapid and accurate identification of fault types and locations. The findings demonstrate that the proposed adaptive protection scheme significantly enhances fault detection accuracy, reducing false trip rates by over 30% compared to conventional protection systems. Moreover, the method efficiently distinguishes between transient and permanent faults, ensuring swift isolation and minimizing disruption to solar PV operations. The developed model also exhibits robustness in handling variations in solar irradiance and load fluctuations, making it suitable for real-world grid applications. This research provides a substantial contribution to the field of solar PV integration by offering a mathematically grounded, adaptive protection solution that ensures improved reliability and resilience in power systems. The proposed method paves the way for the development of more advanced protection schemes, ensuring the seamless integration of renewable energy sources into modern grids while maintaining system stability and security.

An Analytical Review on Predictive Fault Tolerance for Industrial IoT Using Advanced Learning Techniques

This article presents a thorough examination of hybrid machine learning methodologies employed to improve fault tolerance in Industrial Internet of Things (IIoT) systems. These hybrid models enhance fault detection precision and resilience by integrating various machine learning techniques, surpassing conventional methods. The research commences with a comprehensive literature review of contemporary fault tolerance techniques, subsequently presenting a comparison of various datasets and a proposal for hybrid predictive algorithms. The efficacy of these algorithms is subsequently assessed in comparison to cutting-edge techniques. Principal findings underscore the enhanced precision of hybrid models in real-time applications, their diminished computing complexity, and their scalability in dynamic industrial settings. Nonetheless, issues pertaining to integration, data reliance, and real-time implementation are also addressed. The article concluded by outlining prospective research methods, encompassing sophisticated integration techniques, automated model optimisation, adaptive self-learning frameworks, and methodologies to safeguard data privacy. This paper outlines a framework for future advancements in fault tolerance, with considerable implications for both academia and industry.

Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with LSTM Autoencoder for Enhanced Fault Detection

Machinery Fault Detection (MFD) is an important process in contemporary industrial systems, where it predicts possible physical failures before they lead to a serious problem. This uses multiple technologies to monitor machine statuses (algorithms, data gathering systems and sensors) Using a servo-motor driven actuator for deployment, the Locking Mechanism is pre-assembled into an OEM ATE and will enable predictive failure mode identification (via monitoring and warnings of operational parameters i.e., vibration, temperature or auditory signals in-built to MFD systems) leading to Prophylactic maintenance before critical bottlenecks can occur. The dataset we used in our study was collected from Kaggle and it is called the SpectraQuest Machinery Fault Simulator (MFS) Alignment-Balance-Vibration (ABVT). We used LSTM Autoencoder, KNN, SVM and DNN to analyzed the data. Our LSTM Autoencoder model was very accurate and achieved a precision, recall, accuracy and F-score of 99%. We worked on very large scale datasets. It will help the system detect faults and predict their evolution over time, so you save maintenance costs and increase production in your factory. More research on the practical efficiency of these models in real-time across different industrial settings can create a path towards improved and scalable MFD solutions.

IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New EnergyConservation

An IoT-enabled motor drive vehicle integrates Internet of Things (IoT) technology with traditional vehicle systems to enhance control, monitoring, and automation. Sensors and connected devices within the vehicle collect real-time data on parameters such as speed, battery status, motor performance, and environmental conditions. This data is transmitted to cloud-based platforms for analysis, enabling remote diagnostics, predictive maintenance, and optimization of vehicle performance. IoT integration also facilitates features like vehicle tracking, smart navigation, and user-specific adjustments, improving overall efficiency, safety, and user experience. This paper investigates the utilization of IoT enabled Programmable Logic Controller (PLC) technology to enhance fault detection in new energy vehicle (NEV) motor drive systems. With the increasing adoption of electric vehicles, ensuring the safety and reliability of motor drive systems becomes paramount. The study begins with a comprehensive review of existing literature, examining various fault detection methodologies and technologies. Subsequently, simulation analyses are conducted to evaluate the performance of IOT enabled PLC-based fault detection algorithms under different operating conditions. This paper presents the numerical results of fault detection in new energy vehicle (NEV) motor drive systems using Programmable Logic Controller (IOT enabled PLC) technology. Through comprehensive simulations and experimental validations, the IOT enabled PLC-based fault detection algorithms achieved an average detection accuracy of 93% across various fault scenarios. The response time of the fault detection system.

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.

A Deep Learning-Based Approach for Identifying Defects in Solar Panels

In the solar energy sector, the task of monitoring and maintaining large photovoltaic (PV) system portfolios is essential for ensuring optimal performance and reliability. Prominent solar energy companies face challenges with their current fault detection methods, which are inefficient and resource- intensive. This paper addresses the critical need for improved fault detection in solar PV systems to maximize uptime and minimize maintenance costs. We employed advanced data preprocessing and augmentation techniques using Roboflow and developed a YOLOv8 segmentation model in Google Colab with GPU. This model was then deployed using Streamlit, providing a robust solution for identifying faulty solar modules. The proposed approach significantly enhances fault detection accuracy, achieving a minimum accuracy rate of 85%, thus ensuring reliable operation of the PV systems. Additionally, the implementation of this model contributes to a 15% reduction in system downtime and a 10% reduction in maintenance costs. By leveraging advanced machine learning techniques, our solution transforms the maintenance process, making it more efficient and cost-effective. Consequently, this work not only improves the reliability and performance of solar PV systems but also supports the broader goal of sustainable energy through more efficient resource usage.

Enhanced Fault Diagnosis in IoT: Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network

One of the biggest challenges is figuring out when industrial IoT devices break. Notwithstanding these difficulties, one of the many advantages of using real-time sensor data from the Industrial Internet of Things (IIoT) is the ability to monitor events and respond promptly. The IIoT's sensor numbers may be analysed, combined with data from other sources, and applied quickly to make decisions. This manuscript proposes Enhanced Fault Diagnosis in IoT, Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network (FD-IoT-DMSFNN). Initially, input sensor data are taken from the CWRU Dataset. Then, sensor data is normalised using Multivariate Fast Iterative Filtering. Then, the normalised sensor data are extensively dispersed and diverse; hence, the data outlier detection is performed using the Deep isolation forest (DIF) approach. Therefore, this manuscript proposes a Mexican Axolotl Optimization (MAO) for tuning the weight parameter of DMSFNN, which exhibits an enhanced fault detection process. Python is used to implement the recommended strategy. The proposed method's performance yields a lower completion time of 6.5%, 1.8%, and 4.13%; higher prediction accuracy of 2.26%, 6.71%, and 8.32% compared with existing approaches such as towards deep domain adaptability training methods for industrial IoT edge device soft real-time fault diagnosis (FD-IoT-LSTM), Intelligent Fault Quantitative Identification for the Industrial Internet of Things (IIoT) utilising a particular deep dual reinforcement learning model with insufficient samples (FD-IoT-DRL) and IIoT- based fault identification model for industrial use (FD-IoT-ML-LSTM) respectively.

Smart Grid Technologies: Advancements and Applications in Nigeria

This study explores the impact of smart grid technologies on the modernization of power grids in response to evolving energy demands and the integration of renewable energy sources in Nigeria. It aims to empirically assess advancements in smart grid technologies, focusing on four key objectives: eval_uating the impact of smart meters on energy consumption and peak demand reduction, analyzing the effectiveness of Advanced Distribution Management Systems (ADMS) in enhancing grid reliability and efficiency, assessing the role of demand response programs in balancing supply and demand, and examining the integration and management of Distributed Energy Resources (DERs) within smart grids. The research employs a quantitative methodology, collecting and analyzing data from utility companies that have implemented smart grid technologies. Key findings reveal that smart meters significantly reduce energy consumption and peak demand by providing real-time monitoring, while ADMS improves grid reliability and operational efficiency through enhanced fault detection and automated control. Demand response programs effectively balance energy supply and demand, reducing peak loads and energy costs. The integration of DERs increases renewable energy utilization and grid stability but also presents challenges related to variability in power output and management complexity. The study concludes that smart grid technologies play a crucial role in achieving a more resilient and efficient power grid, though addressing challenges related to DER integration and system management is essential for maximizing their benefits.