Search Results (468)

This paper proposes a PI-Dual-STGCN fault diagnosis model based on a SHAP-LLM joint explanation framework to address issues such as the lack of transparency in the diagnostic process of deep learning models and the weak interpretability of diagnostic results. PI-Dual-STGCN enhances the interpretability of graph data by introducing physical constraints and constructs a dual-graph architecture based on physical topology graphs and signal similarity graphs. The experimental results show that the dual-graph complementary architecture enhances diagnostic accuracy to 99.22%. Second, a general-purpose SHAP-LLM explanation framework is designed: Explainable AI (XAI) technology is used to analyze the decision logic of the diagnostic model and generate visual explanations, establishing a hierarchical knowledge base that includes performance metrics, explanation reliability, and fault experience. Retrieval-Augmented Generation (RAG) technology is innovatively combined to integrate model performance and Shapley Additive Explanations (SHAP) reliability assessment through the main report prompt, while the sub-report prompt enables detailed fault analysis and repair decision generation. Finally, experiments demonstrate that this approach avoids the uncertainty of directly using large models for fault diagnosis: we delegate all fault diagnosis tasks and core explainability tasks to more mature deep learning algorithms and XAI technology and only leverage the powerful textual reasoning capabilities of large models to process pre-quantified, fact-based textual information (e.g., model performance metrics, SHAP explanation results). This method enhances diagnostic transparency through XAI-generated visual and quantitative explanations of model decision logic while reducing the risk of large model hallucinations by restricting large models to reasoning over grounded, fact-based textual content rather than direct fault diagnosis, providing verifiable intelligent decision support for industrial fault diagnosis. Full article

Fault diagnosis is a core technology ensuring the safe and efficient operation of industrial systems. A paradigm shift has been observed wherein traditional signal analysis has been replaced by intelligent, algorithm-driven approaches. In recent years, large language models, digital twins, and knowledge graphs have been introduced. A new stage of intelligent integration has been reached that is characterized by data-driven methods, knowledge guidance, and physical–virtual fusion. In the present paper, the evolutionary context of fault diagnosis technologies was systematically reviewed, with a focus on the theoretical methods and application practices of traditional machine learning, digital twins, knowledge graphs, and large language models. First, the research background, core objectives, and development history of fault diagnosis were described. Second, the principles, industrial applications, and limitations of supervised and unsupervised learning were analyzed. Third, innovative uses were examined involving physical–virtual mapping in digital twins, knowledge modeling in knowledge graphs, and feature learning in large language models. Subsequently, a multi-dimensional comparison framework was constructed to analyze the performance indicators, applicable scenarios, and collaborative potential of different technologies. Finally, the key challenges faced in the current fault diagnosis field were summarized. These included data quality, model generalization, and knowledge reuse. Future directions driven by the fusion of large language models, digital twins, and knowledge graphs were also outlined. A comprehensive technical map was established for fault diagnosis researchers, as well as an up-to-date reference. Theoretical innovation and engineering deployment of intelligent fault diagnosis are intended to be supported. Full article

This paper explores simulation application functions for the computer monitoring system of a hydro–wind–solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the “dual carbon” backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture. Intelligent operation and maintenance with fault diagnosis—powered by big data and deep learning—enables equipment condition prediction, and emergency drill platforms boost response capacity via 3D visualization and scriptless modeling. Current hurdles include absent multi-energy modeling standards, poor extreme-condition adaptability, and inadequate knowledge transfer mechanisms. Future research should prioritize hybrid physical–data-driven approaches, multi-dimensional robust scheduling, federated learning-based diagnostics, and integrated digital twin, edge computing, and decentralized ledger technologies. These advances will drive simulation platforms toward greater intelligence, interoperability, and reliability, laying the technical foundation for unified hydro–wind–solar control centers. Full article

The working conditions of bearings, as a key component in electromechanical systems, are becoming increasingly complex with the rapid development of current intelligent manufacturing technology. Therefore, it is difficult to accurately identify the abnormal operating state of the bearing through a single signal. In addition, data-based bearing fault diagnosis methods insufficiently utilize bearing prior knowledge under complex working conditions. To address the above issues, this paper proposes a bearing fault diagnosis method based on multi-source information fusion with physical prior knowledge (MSIF-PPK). An information fusion module and a physical embedding module are designed: the former module fuses frequency-domain, time–frequency-domain, and working condition information through an attention mechanism, while the latter one embeds physical working condition data and features. The feasibility and the effectiveness of the modules are verified through comparative experiments and ablation experiments using the Southeast University (SEU) Bearing Dataset, the Mehran University of Engineering and Technology (MUET) Induction Motor Bearing Vibration Dataset, and the Harbin Institute of Technology (HIT) Aeroengine Bearing Dataset. Experimental results show that this method is feasible, reliable, and interpretable for bearing fault diagnosis under complex working conditions. Full article

Elevators are critical vertical transportation systems in modern urban infrastructure; however, their intricate mechanical and electrical configurations render them highly susceptible to safety-critical failures. Although various automated diagnostic techniques have been proposed, many data-driven approaches exhibit limited generalizability due to their insufficient consideration of physical fault mechanisms and strong dependence on facility-specific training data. To overcome these limitations, this study presents a rule-based automated diagnostic framework for elevator state recognition that prioritizes reliability, real-time performance, and interpretability. The proposed approach explicitly integrates physically meaningful fault characteristics and dominant frequency components into the diagnostic process, and employs predefined expert rules derived from established standards to classify fault states in an automated manner. The effectiveness of the proposed method is verified using real operational data collected from an in-service elevator, demonstrating improved diagnostic accuracy and computational efficiency compared to conventional manual inspection procedures. The proposed framework provides a practical and scalable solution for intelligent elevator condition monitoring and is expected to serve as a foundational technology for future smart maintenance and preventive safety systems. Full article

Civil aircraft that have obtained airworthiness certification—operating with complex structures under harsh service environments—are prone to abnormal states and potential failures. Aircraft health management, as a comprehensive integration of advanced technologies, embodies the overall engineering capability of civil aviation. The advent of big data has introduced new opportunities and challenges, driving the development of intelligent health management across the entire life cycle—from predictive strategies and real-time monitoring to anomaly detection and adaptive decision support. This paper reviews current applications and technological trends in big data-driven health management for all airworthiness-certified civil aviation aircraft, with a focus on real-time fault diagnosis, Remaining Useful Life (RUL) prediction, large-scale fault data analytics, and emerging approaches enabled by generative models. The analysis highlights the role, necessity, and future directions of these technologies in advancing sustainable and intelligent civil aviation. Full article

Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions from material defects with high sensitivity. However, mechanical background noise significantly corrupts AE signals, while optimal selection of gear health indicators remains challenging, critically impacting fault feature extraction accuracy. This study develops an adaptive feature extraction method for fault diagnosis using AE. Through gear fault simulation experiments, VMD analyzes mode number and penalty factor effects on signal decomposition. Correlation coefficient-based reconstruction optimization is implemented. For feature selection challenges, SVM-RFE enables adaptive parameter ranking. Finally, SVM with optimized kernel parameters achieves effective fault classification. Optimized VMD enhances signal decomposition, while SVM-RFE reduces feature dimensionality, addressing manual selection uncertainty and computational redundancy. Experimental results demonstrate superior accuracy in gear fault classification. This study proposes an AE-based adaptive feature extraction method with three innovations: (1) establishing VMD parameter–decomposition quality relationships; (2) developing an SVM-RFE feature selection framework; (3) achieving high-accuracy gear fault classification. The method provides a novel technical approach for rotating machinery diagnostics with significant engineering value. Full article

Inter-basin water transfer projects are core infrastructure for achieving sustainable water resource allocation and addressing regional water scarcity, and pumping station units, as their critical energy-consuming and operation-controlling components, are vital to the projects’ sustainable performance. With the growing complexity and scale of these projects, pumping station units have become more intricate, leading to a gradual rise in failure rates. However, existing fault diagnosis methods are relatively backward, failing to promptly detect potential faults—this not only threatens operational safety but also undermines sustainable development goals: equipment failures cause excessive energy consumption (violating energy efficiency requirements for sustainability), unplanned downtime disrupts stable water supply (impairing reliable water resource access), and even leads to water waste or environmental risks. To address this sustainability-oriented challenge, this paper focuses on the fault characteristics of pumping station units and proposes a comprehensive and accurate fault diagnosis model, aiming to enhance the sustainability of water transfer projects through technical optimization. The model utilizes advanced algorithms and data processing technologies to accurately identify fault types, thereby laying a technical foundation for the low-energy, reliable, and sustainable operation of pumping stations. Firstly, continuous wavelet transform (CWT) converts one-dimensional time-domain signals into two-dimensional time-frequency graphs, visually displaying dynamic signal characteristics to capture early fault features that may cause energy waste. Next, the multi-head attention mechanism (MHA) segments the time-frequency graphs and correlates feature-location information via independent self-attention layers, accurately capturing the temporal correlation of fault evolution—this enables early fault warning to avoid prolonged inefficient operation and energy loss. Finally, the improved convolutional neural network (CNN) layer integrates feature information and temporal correlation, outputting predefined fault probabilities for accurate fault determination. Experimental results show the model effectively solves the difficulty of feature extraction in pumping station fault diagnosis, considers fault evolution timeliness, and significantly improves prediction accuracy and anti-noise performance. Comparative experiments with three existing methods verify its superiority. Critically, this model strengthens sustainability in three key ways: (1) early fault detection reduces unplanned downtime, ensuring stable water supply (a core sustainable water resource goal); (2) accurate fault localization cuts unnecessary maintenance energy consumption, aligning with energy-saving requirements; (3) reduced equipment failure risks minimize water waste and environmental impacts. Thus, it not only provides a new method for pumping station fault diagnosis but also offers technical support for the sustainable operation of water conservancy infrastructure, contributing to global sustainable development goals (SDGs) related to water and energy. Full article

In this paper, we propose a mechanical fault diagnosis technology for circuit breakers based on the NGO-VMD, aiming to improve the accuracy and efficiency of fault diagnosis. The circuit breaker is a key protection device in power systems, and its operational status is crucial to grid security. This paper introduces the NGO-VMD method to decompose its vibration signals, aiming to improve the accuracy and efficiency of fault diagnosis. Failure to detect and address mechanical faults in circuit breakers can lead to equipment damage, power outages, and even personal injury. Therefore, it is of great significance to develop efficient and accurate mechanical fault diagnosis technology for after converting the mechanical fault signal of the vacuum circuit breaker in the distribution network into the IMF form, the modal information of the vibration signal under different faults of the circuit breaker is effectively extracted, and the singular value decomposition of the IMF signal component is carried out to make the information characteristics contained more obvious, Finally, LSTM is used to achieve precise identification of circuit breaker faults. In this paper, the experimental test is carried out on the basis of the actual vacuum circuit breaker in the distribution network, and the feasibility of the design scheme is verified by comprehensive analysis. The comparison and analysis with other methods can be obtained, and the scheme has the advantages of higher efficiency, stronger stability and more accuracy. Full article

Power transformers are critical components of power grids, and their operational status characterization and fault diagnosis are crucial for power system reliability. Oil quality assessment is a crucial method for determining transformer status, and the detection of impurity particles in oil has historically been a key approach. However, recent field tests have revealed the presence of numerous impurity particles less than 5 μm in transformer oil. Current power standards do not address these micron-sized particles, and their sources and mechanisms of action are largely unresolved. Therefore, this paper designed a localized overheating experiment, incorporating microflow imaging technology, to investigate the generation patterns of impurity particles under localized overheating and their quantitative correlation with heat. Field oil samples were also collected and tested to further explore the potential application of these micron-sized particles in transformer overheating assessment. The research results show that insulating oil can decompose and produce impurity particles at temperatures as low as 140 °C. When the temperature is below 140 °C, the number of particles at different heat levels is not significantly different from that of the non-overheated oil sample. However, when the temperature exceeds 140 °C, the number of particles increases significantly with increasing heat. Among the generated particles, particles with a diameter of less than 5 μm account for over 50% of the total number, and their number increases significantly with increasing heat. Their morphology is characterized by a smooth, regular, and spherical shape. Field test results of overheated oil samples are consistent with laboratory tests. Micron-sized particles are highly sensitive to changes in overheating conditions and have the potential to be used as a new characteristic parameter of transformer overheating conditions. In summary, this paper reveals the formation mechanism of impurity particles in insulating oil under localized overheating conditions. It was found that insulating oil can also decompose and generate impurity particles at 140 °C, with the pyrolysis products mainly consisting of particles smaller than 5 μm in diameter, which are not currently considered a concern in existing standards. Further research indicates that these micron-sized particles exhibit high sensitivity to changes in overheating conditions, demonstrating potential application value as a novel characteristic parameter of transformer overheating. Full article

Brushless DC (BLDC) motors are widely used in many engineering fields such as transportation, industrial automation, pumping systems, household devices, and renewable energy applications. Their popularity mainly arises from advantages like high power density, low noise, long service life, and high efficiency. This study contributes to the literature by comprehensively addressing the types, applications, faults, and diagnostic methods of BLDC motors. This review systematically examines recent studies to identify and classify common mechanical, electrical, magnetic, thermal, and sensor-related faults. Diagnostic approaches reported in these studies are then analyzed and compared. The methods are grouped into several categories, including signal processing, model-based, data driven, artificial intelligence-supported, and thermal or magnetic monitoring techniques. The review results show that hybrid and intelligent diagnostic strategies, which combine different analysis methods, significantly improve the accuracy of fault detection and enable earlier fault identification. These improvements also contribute to higher reliability and safer operation of BLDC systems. In the discussion, attention is given to the growing use of artificial intelligence and data fusion in fault diagnosis. These trends are likely to guide the next generation of condition monitoring systems for BLDC motors. Overall, this study emphasizes the importance of developing reliable and sustainable diagnostic frameworks to enhance energy efficiency and system performance. The results can provide a useful reference for researchers and engineers working on BLDC motor technologies. Full article

Electric linear or rotary actuators are the ultimate power-dense execution units in modern industrial and transportation systems, yet their dependability is directly governed by the health of the driving electric motor. To guarantee fail-safe operation of the electromechanical actuator chain, condition monitoring and fault diagnosis of the embedded motor have become indispensable. The motor fault diagnosis process can be comprehensively summarized into four key steps: signal acquisition, feature extraction, condition monitoring, and fault identification. Based on the data obtained by signal acquisition, machine learning methods can be effectively integrated into the latter three steps. Feature extraction techniques primarily revolve around autoencoders. In terms of condition monitoring technology, in-depth research has been conducted on image recognition, including the identification of two-dimensional and three-dimensional images. In terms of fault identification, various machine learning methods have been applied, such as convolutional neural networks, autoencoders, transfer learning, long short-term memory networks, and support vector machines. Finally, the potential application of the Large Language Model in motor fault diagnosis was explored. Full article

High-voltage (HV) bushings are critical hub components in power systems, whose operational reliability is paramount to the safety and stability of transmission and distribution infrastructure. Conventional management paradigms are hampered by challenges such as information silos, reactive maintenance, and imprecise condition assessment, rendering them in-adequate for the evolving demands of modern power systems. Digital twin technology, by creating a high-fidelity, re-al-time interplay between physical entities and their virtual counterparts, provides a revolutionary pathway toward the intelligent full-life-cycle management (FLCM) of HV bushings. This paper presents a review of the current state of research in this domain. It begins by reviewing research on the construction a five-dimensional digital twin framework that encompasses the entire lifecycle: design, manufacturing, operation and maintenance (O&M), and decommissioning. Subsequently, it delves into the application paradigms of digital twins across typical scenarios, including external insulation design, intelligent condition assessment, insulation defect identification, fault diagnosis, and predictive maintenance. The paper then examines the core technological underpinnings, such as multi-physics coupled modeling, multi-source heterogeneous data fusion, and data-driven model updating and condition assessment. Finally, it identifies current challenges related to data, models, standards, and costs, and offers a forward-looking perspective on future research directions, including group digital twins, deep integration with artificial intelligence, edge-side deployment, and standardization initiatives. This work aims to provide a theoretical reference and technical guidance for advancing the intelligent O&M of HV bushings and bolstering grid security. Full article

Three-dimensional (3D) reconstruction technology for space targets can provide information support such as target structures and dimensions for space missions including on-orbit services and fault diagnosis, which is crucial for maintaining the normal operation of space assets. Optical devices and ISAR (Inverse Synthetic Aperture Radar) can provide high-resolution two-dimensional (2D) images of space targets, serving as the primary means for space target observation. However, existing 3D imaging methods for space targets exhibit significant limitations: the fusion process of optical observation data and ISAR observation data lacks automation, and factors such as image offset that affect 3D imaging quality are not fully considered. To address these issues, this paper proposes a novel 3D imaging method for space targets utilizing optical-ISAR joint observation. This method first employs semantic segmentation networks to automatically extract target regions from optical and ISAR images. Then, it combines octree-space carving technology for efficient 3D reconstruction and performs correction of target region offset based on projection optimization to achieve high-quality 3D imaging. This method eliminates the need for manual target region extraction, improving the automation level of the algorithm. The application of octree-space carving technology greatly enhances the efficiency of 3D reconstruction. Moreover, by correcting target region offset, the proposed method delivers superior 3D imaging results. Simulation experiments demonstrate that the method exhibits significant superior performance in terms of reconstruction efficiency and imaging quality. Additionally, experiments based on measured data further verify the feasibility and practical application value of the proposed method. Full article

Magnetic levitation (Maglev) technology has long garnered significant attention in the engineering community due to its inherent advantages, such as contactless operation, minimal friction losses, low noise, and high precision. Based on electromagnetic suspension (EMS) and electrodynamic principles, these systems are primarily developed for advanced transportation, while also inspiring emerging applications such as vibration isolation and flywheel energy storage. Despite progress, practical deployment faces critical challenges, including accurate modeling, robustness against nonlinear and uncertain dynamics, and control stability under complex conditions. Artificial intelligence (AI), particularly machine learning (ML) offers promising solutions. Studies show ML-based methods, i.e., improved particle swarm optimization (PSO) optimize proportional-integral-derivative (PID) to reduce controller output overshoot, deep reinforcement learning (DRL) reduces levitation gap fluctuation under complex conditions, ensemble learning achieves high fault diagnosis accuracy, and convolutional neural network-long short-term memory (CNN-LSTM) predictive maintenance cuts costs. This review summarizes recent AI-enabled advances in Maglev transportation system modeling, control, and optimization, highlighting representative algorithms, performance comparisons, technical challenges, and future directions toward intelligent, reliable, and energy-efficient transportation systems. Full article