Search Results (968)

Heavy-duty industrial robots (HIRs) are essential for high-payload operations in the automotive, aerospace, and nuclear industries. However, existing reviews are often limited to specific domains or control methods. This paper provides a concise review of recent advances in HIRs from two perspectives: structural innovation and intelligent control. The review shows that structural design is evolving toward lightweight, robust, and maintainable architectures, while control strategies are increasingly shifting from conventional PID methods to adaptive, robust, and learning-based approaches to handle high inertia, nonlinear dynamics, and uncertainty. Representative applications, including friction stir welding and nuclear operations, are also summarized. Based on the reviewed literature, we identify several key challenges for future research, including structure–control co-design, energy-aware motion planning, robust autonomy in hazardous environments, safe human–robot collaboration, digital-twin-enabled lifecycle optimization, and interpretable fault diagnosis. These findings outline the research agenda for the next generation of HIRs. Full article

Currently, data-driven fault diagnosis methods have achieved remarkable progress. However, in industrial scenarios, acquiring a sufficient amount of fault data poses a challenge, thereby leading to the issue of imbalanced data in intelligent fault diagnosis. Furthermore, manual recording and instrument measurement errors will introduce label noise, which significantly impacts diagnosis performance. To address these problems, this paper proposes a safe-domain generative adversarial network with Swin Transformer (SDGAN-ST). A safe domain selection method is utilized to eliminate noisy samples and construct a pure dataset that poses no risk to the GAN training process. Consequently, GAN can generate high-quality minority samples to rebalance the original dataset. Additionally, the Swin Transformer is employed as a classifier to capture global information for each fault sample, thereby achieving high diagnostic accuracy. Experiments on the CWRU dataset and a real-world oxygen compressor bearing dataset demonstrate the effectiveness of the proposed method. On the CWRU dataset, SDGAN-ST achieves accuracies of 98.88%, 97.63%, and 97.50% under imbalance ratios of 1:10, 1:20, and 1:30, respectively. On the real-world dataset, SDGAN-ST achieves 100% accuracy under all three imbalance ratios. Additional experiments under noise ratios of 20%, 30%, and 40% show that SDGAN-ST maintains stable diagnostic performance and is more robust to label noise than ordinary WGAN-GP-based methods. Full article

Plunger pumps serve as core power equipment in oilfield water injection systems, where their reliable operation directly affects crude oil recovery efficiency and production safety. Failures such as mechanical wear and seal leakage can cause injection pressure fluctuations, increased energy consumption, and even pipeline burst accidents. This study addresses the challenges in plunger pump fault diagnosis, including the difficulty in capturing multi-scale fault features, interference from redundant information in high-dimensional feature spaces, and high model computational complexity. We propose a lightweight fault diagnosis approach called Multi-scale Attention Neural Network (MSLAN), which combines multi-scale convolution and attention mechanisms. In this model, a Separable Multi-scale Fusion Module (SMSF) employs parallel multi-branch convolutional kernels to acquire fault signatures across multiple scales, while computational overhead is reduced through depthwise separable convolution and shared pointwise convolution. Additionally, a Multi-Branch Parallel Attention Module (MBPA) is introduced to finely model complex inter-channel dependencies through a four-branch parallel structure, enhancing the perception of key features and suppressing redundant information. Experimental results on a self-constructed plunger pump dataset, the Case Western Reserve University bearing dataset, and the Southeast University gearbox dataset demonstrate that MSLAN achieves F1-scores of 88.95%, 98.89%, and 99.90%, respectively. While maintaining high diagnostic accuracy, the model exhibits significantly lower parameter count and computational cost compared to baseline models, effectively balancing diagnostic precision and computational efficiency. Ablation studies and visualization analyses further validate the effectiveness of each module. This study establishes an accurate and efficient intelligent fault diagnosis solution for plunger pumps, which is also readily applicable to a broader range of rotating machinery. Full article

Failures in critical systems and equipment within nuclear power plants (NPPs) significantly threaten operational safety and reliability. Therefore, rapid and accurate root cause localization during the incipient stages of failure is critical to preventing escalation. Traditional modeling methods often fail to address the inherent structural complexity of NPPs, the diversity of failure modes, and the stochastic mapping relationships between symptoms and causes. To address these challenges, this paper proposes an intelligent fault diagnosis framework integrating knowledge graphs (KGs) and Bayesian networks (BNs). First, by analyzing failure modes and anomaly characteristics, we define discrimination criteria for typical faults. Second, a structured knowledge modeling approach is developed to transform unstructured fault information into a KG, which is subsequently mapped to a BN topology. Finally, to mitigate the subjectivity of expert priors, data-driven structure and parameter learning algorithms are employed to optimize the model, enhancing inference accuracy. Robustness was validated through experiments targeting three fault severity levels, using signed directed graphs (SDGs), support vector machines (SVMs), domain generalization softmax (DG-softmax) and long short-term memory (LSTM) as benchmarks. Experimental results demonstrate that the proposed method maintains high diagnostic precision across varying severities, outperforming traditional data-driven methods in accuracy and stability. This study enhances the interpretability and engineering applicability of intelligent diagnosis in nuclear power systems. Full article

In recent years, explainable artificial intelligence has received increasing attention in the field of bearing fault diagnosis. However, existing interpretability methods, such as Shapley Additive Explanations (SHAP), often rely on the quality of input features. To achieve high diagnostic accuracy, researchers often extract a large number of features from vibration signals across multiple domains, leading to feature redundancy. This redundancy not only increases the computational cost and risk of overfitting in diagnostic models but also dilutes the contributions of core features during interpretability analysis, resulting in biased explanations. To address this challenge, we propose a two-stage interpretable fault diagnosis approach. In the first stage, the Explainable Boosting Machine (EBM) selects core features to reduce redundancy. In the second stage, EBM is enhanced by Random Forest (RF) through residual learning to form the RF-EBM diagnostic model. EBM and SHAP are further used for dual interpretability analysis. Experimental results on public laboratory benchmark datasets demonstrate that the proposed approach achieves good diagnostic performance and outperforms traditional EBM. Overall, the approach reduces redundancy through feature selection, improves diagnostic performance, and makes the decision-making process more transparent, providing a useful methodological reference for trustworthy fault diagnosis in industrial applications. Full article

Rolling bearing vibration signals often exhibit strong nonstationarity and are susceptible to noise interference, which makes fault feature extraction and accurate diagnosis challenging under complex operating conditions. To address these issues, this paper proposes a fault diagnosis pipeline that sequentially combines an improved snow ablation optimizer (ISAO), variational generalized nonlinear mode decomposition (VGNMD), and a bidirectional temporal sequence fusion network (BiTSF-Net). Firstly, ISAO is used to optimize the key parameters of VGNMD, including the bandwidth penalty parameter and smoothing constraint parameter, with minimum envelope entropy as the fitness function. Secondly, the optimized VGNMD decomposes raw vibration signals into modal components, and the modal component with the minimum envelope entropy is selected to highlight fault-related impulsive characteristics. Thirdly, 11-dimensional time-domain statistical features are extracted from the selected optimal modal component to characterize bearing health states. Finally, these extracted features are used as the input to BiTSF-Net, which combines bidirectional temporal convolutional networks and bidirectional long short-term memory networks in a parallel structure to learn local transient features and temporal dependencies for fault classification. Experimental validation is conducted on the Case Western Reserve University dataset. Comparative results with convolutional neural networks, gated recurrent units, and long short-term memory networks demonstrate that the proposed pipeline achieves superior diagnostic performance, with an average accuracy of 99.63% and a maximum accuracy of 100%. These results confirm the effectiveness and robustness of the proposed ISAO-VGNMD feature extraction and BiTSF-Net classification pipeline for bearing fault diagnosis under complex nonstationary conditions. Full article

With the rapid development of big data and artificial intelligence, motor fault diagnosis has become increasingly important in the fields of intelligent transportation and new energy vehicles. As a core component of the electric vehicle drive system, the operating condition of an asynchronous AC motor is directly related to the safety and reliability of the vehicle powertrain. Once a motor fault occurs, it may lead to powertrain failure, thereby causing traffic accidents, financial losses, and even threats to human life, particularly under high-speed driving conditions where the safety risks are more severe. Therefore, the timely and accurate diagnosis of motor faults in electric vehicles, especially autonomous vehicles, is of great practical significance. To effectively capture the fault-related features embedded in the vibration and voltage signals of asynchronous AC motors and efficiently perform fault diagnosis, this paper introduces a fault-diagnosis model that integrates the exponential moving average (EMA), a two-dimensional multi-scale convolutional neural network (2DMSCNN), and the Squeeze-and-Excitation Network (SENet) mechanism. Experimental validation based on a publicly available asynchronous AC motor fault-diagnosis dataset demonstrates that, compared with traditional machine learning models and ensemble learning methods, the proposed EMA-2DMSCNN-SENet model achieves higher diagnostic accuracy and stronger robustness. Full article

With the rapid growth of transportation electrification and smart energy systems, the reliable operation of electric vehicle (EV) charging infrastructure has become an important issue for sustainable transport, since charging faults may interrupt service and shorten equipment lifetime. However, practical charging environments are often characterized by heterogeneous operating conditions and severely imbalanced fault distributions, which limit the effectiveness of conventional fault diagnosis methods. To address these challenges, this study proposes a lightweight Proto-Contrastive Discriminative Learning (PCDL) framework for intelligent fault diagnosis in EV charging systems. The proposed method combines supervised contrastive learning with a prototype-distance discrimination mechanism to improve the identification of rare abnormal states under long-tailed data conditions. Heterogeneous charging features, including discrete control signals and continuous total harmonic distortion (THD) indicators, are projected into a discriminative embedding space, while anomaly detection is performed according to the relative distances between samples and class prototypes. Experimental results on a publicly available EV charging-pile monitoring dataset, containing 122,144 samples with four discrete control/safety features and two THD-based power-quality features, demonstrate that the proposed framework maintains stable detection performance under imbalance ratios of 1:1, 1:10, and 1:100. Under the most challenging 1:100 condition, the proposed method achieves an F1-score of 84.21%, representing a 29.08% improvement over the strongest baseline method. In addition, the framework requires only approximately 11 KB of memory and maintains CPU inference latency below 6.3 ms, demonstrating strong potential for real-time deployment on resource-constrained edge devices. These results suggest that the proposed framework can provide a lightweight diagnostic tool for practical charging stations and support safer and more reliable EV charging operation. Full article

In-wheel motor bearings in electric vehicles operate in harsh environments where strong background noise often masks early fault features, limiting the accuracy of traditional diagnostic methods. This study proposes an intelligent fault diagnosis framework integrating improved Successive Variational Mode Decomposition (SVMD) with a ResNet–Kolmogorov–Arnold Network (ResNet-KAN). To enhance feature extraction, a multi-strategy Crested Porcupine Optimizer (CPO) is employed to adaptively optimise SVMD parameters. Subsequently, a Gramian angular difference field (GADF) reconstruction strategy transforms one-dimensional vibration signals into two-dimensional images to improve spatial distinguishability. Finally, a ResNet-KAN model, featuring a ReLU-based non-linear classification head, is developed to capture complex fault boundaries more effectively than traditional linear layers. Experimental results demonstrate that the CPO-SVMD method increases the kurtosis of extracted components by at least 25.6% compared to traditional optimisation methods. Furthermore, the ResNet-KAN model achieves an identification accuracy exceeding 98% on the in-wheel motor bearing dataset, outperforming 2DCNN, ResNet, and ViT models by at least 2%. This integrated approach provides a robust, high-precision solution for the intelligent condition monitoring and early warning of in-wheel motor drive systems under complex, high-noise operating conditions. Full article

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

The increasing complexity and distributed nature of Robot Operating System (ROS)-based robotic systems require advanced Fault Detection and Diagnosis (FDD) approaches that operate autonomously with minimal human intervention. The goal of this systematic literature review is to investigate how observability-driven FDD can be automated in ROS-based robotic systems to minimise human effort. Through this lens, the review surfaces four recurring gaps that collectively limit observability-driven automation: rich telemetry sources—logs, traces, and metrics—exist in isolation and are rarely integrated into real-time detection pipelines or leveraged collectively to improve failure diagnostics; online monitoring enables automatic fault detection but depends heavily on predefined rules and expert configuration and interpretation; failure explanations are generated post hoc and rely heavily on logs; and systems remain largely reactive, lacking the continuous monitoring infrastructure needed to anticipate faults before they propagate. Although Large Language Models (LLMs) show considerable promise for automated fault explanation and natural language interaction with robotic systems, current implementations fall short of comprehensive, real-time monitoring that unifies logs, traces, metrics, and sensor streams with Artificial Intelligence (AI) reasoning. To address these gaps, this paper motivates hybrid architectures that combine observability-first design, runtime monitoring, static analysis, and agentic LLM-based reasoning, laying the groundwork for more proactive and autonomous fault management in ROS-based systems. Full article

This research focuses on the application of convolutional neural networks (CNNs) for fault detection in ignition coils and fuel injectors of a YESA 3140 gasoline engine. The objective is to design a CNN capable of identifying when the spark ignition engine (SIE) is operating under optimal conditions and when it presents specific power supply disconnection faults in the four injectors and four coils. Signals from the knock sensor (KS) and camshaft position sensor (CMP) of the SIE were acquired using a MyDAQ data acquisition card and LabVIEW software version 2024. A strict sampling protocol was followed: each replicate had a duration of 5 s while the engine was running at normal operating temperature and idle speed. Prior to each sampling, the SIE was operated with the corresponding fault induced for 5 min. The signals obtained from the KS sensor were transformed into spectrograms, which were then used to train various CNN models. The resulting CNN achieved a classification error of 3.21%. The algorithm was validated by inducing supervised faults in various Otto cycle engines. Full article

Accurate fault diagnosis of rotating machinery is critical for ensuring the reliability of the energy, industrial, and transportation sectors. However, conventional methods face significant challenges, including the susceptibility of the Snow Ablation Optimizer (SAO) to local optima, the instability of Multiscale Differential Symbolic Entropy (MDSE) with short time series, and the non-adaptability of Support Vector Machine parameters. To address these issues, this study proposes a parameter-adaptive fault diagnosis framework integrating an improved SAO with Adaptive Refined Composite Multiscale Differential Symbolic Entropy (Adaptive-RCMDSE). First, the Logistic Sine Cosine strategy (LSC) is introduced to enhance SAO’s global search capability, forming the LSC-SAO algorithm. Subsequently, an Adaptive-RCMDSE method is developed wherein LSC-SAO optimizes the control parameter to significantly improve feature stability for short time series. Furthermore, an Adaptive Support Vector Machine (Adaptive-SVM) model is constructed, employing LSC-SAO to automatically tune the penalty factor and kernel parameters for precise fault identification. Finally, validation is performed on gearbox, ball bearing, and axle box bearing datasets. Results indicate that the proposed method achieves superior diagnostic performance, with average accuracies of 99.70%, 99.29%, and 99.28%, respectively, outperforming existing methods. This work provides an effective and robust solution for intelligent health monitoring of rotating machinery. Full article

Industrial robots are indispensable equipment in automated production lines and play a crucial role in advancing the development of intelligent manufacturing. Bearings are key components within robot joints. To ensure the precise execution of operational tasks and to prevent potential safety accidents in a timely manner, it is essential to perform fault diagnosis on the bearings within robot joints. However, fault diagnosis methods based on deep learning typically require a large amount of fault measurement data, which can be challenging to obtain due to various constraints. To address the issue of insufficient data, this paper proposes a fault diagnosis method based on the integration of digital twin technology and MTF-ResTLN. First, a digital twin model of the industrial robot is established, and fault excitations are injected into different nodes of the twin model to generate fault data under various node conditions. The measured data are then combined with the simulated fault data to form a training dataset. Furthermore, a novel classifier is developed by integrating the Markov Transition Field with a Residual Transfer Learning Network. It achieves cross-domain fault diagnosis and enhances the capability of fault diagnosis. Full article

To address the frequent occurrence of multiple fault types, the difficulty of feature extraction, and the susceptibility to noise interference in rolling bearings under complex operating conditions, this paper proposes a fault diagnosis method based on an improved one-dimensional convolutional neural network (1DCNN) integrated with a Transformer architecture. This approach leverages the 1DCNN to efficiently extract local impact and energy features from vibration signals, while the improved Transformer enables global modeling of long-range temporal dependencies, thereby significantly enhancing the recognition accuracy for multi-class fault signals and the generalization capability of the model. Experimental data are sourced from the Case Western Reserve University bearing fault dataset, with multi-channel vibration signals subjected to preprocessing and balanced sampling, and various types of simulated noise systematically introduced to comprehensively verify the noise robustness of the proposed model. Experimental results on the public dataset demonstrate that the improved 1DCNN-Transformer model achieves a classification accuracy of 99.43%, markedly outperforming traditional methods such as ANN, CNN, LeNet, and SVM. Further t-SNE visualizations and confusion matrix analyses reveal the method’s superior feature discrimination and high-precision performance across multiple fault categories. Tests under strong noise conditions further indicate that the model exhibits high robustness and excellent potential for engineering applications. In summary, the proposed method provides an efficient and reliable solution for intelligent fault diagnosis of rolling bearings in complex environments and lays a solid foundation for future model development and industrial deployment. Full article