Search Results (1,572)

Fault diagnosis in power electronic systems is challenging when fault categories geometrically overlap within the measurement space, limiting class separability and introducing classification ambiguity. This work proposes an edge-oriented hybrid fault-space restructuring methodology that utilizes UMAP-based embeddings and hierarchical clustering to group overlapping fault conditions into robust hybrid representations. Subsequently, supervised machine learning models execute the final classification over this optimized space. Validation was conducted using a large-scale synthetic dataset generated via real-time hardware-in-the-loop (HIL) simulation, evaluating electrical measurements from three-dimensional RMS values to 60-dimensional instantaneous waveforms. Tested with Decision Tree and Random Forest algorithms, the restructuring strategy significantly improves robustness under geometric ambiguity compared to conventional classification without space restructuring. Specifically, low-dimensional measurements achieved F1-score improvements of approximately 72% and 46% for the Decision Tree and Random Forest algorithms, respectively, while high-dimensional measurement configurations still exhibited significant improvements of 36% and 52%. Consequently, these results confirm that the combined restructuring and classification pipeline is highly effective across the analyzed measurement dimensionalities, establishing a dependable cluster-based diagnostic strategy that enhances classification robustness while accepting a trade-off in individual fault-isolation granularity. Finally, hardware deployment experiments on a Raspberry Pi 4 platform demonstrated the feasibility of executing the trained classifiers for real-time inference under constrained computational environments. The experimental evaluation validated real-time execution capabilities, achieving sub-millisecond inference latencies (as low as 0.32 ms), a memory footprint under 0.14 MB, and processing rates exceeding 2600 inferences per second using lightweight Decision Tree classifiers. Ultimately, these findings indicate that the proposed strategy improves fault detection across the evaluated measurement configurations while ensuring a highly viable execution on resource-constrained devices once the classifiers are trained. Full article

Effective condition monitoring (CM) in the mining industry is crucial for operational excellence, given the harsh environments, continuous operation, and high-value nature of assets. Traditional fault diagnosis methods like vibration analysis often prove inadequate due to signal noise, logistical challenges for sensor placement, and limitations in detecting subtle failures. This paper addresses these challenges by proposing an advanced contactless diagnostic system that integrates Infrared Thermography (IRT) with an optimized Convolutional Neural Network (CNN) for detecting machinery faults in mining operations. The core of the approach involves a customized ResNet-50 architecture, chosen for its inherent ability to extract hierarchical features directly from raw thermal image data, thereby circumventing the laborious and error-prone process of manual feature engineering. Recognizing the profound impact of hyperparameters on model performance, a novel optimization strategy is developed. This strategy utilizes an amended Salp Swarm Algorithm (SSA), which incorporates a Levy flight mutation strategy and improved position update mechanisms to enhance its exploration capabilities and prevent premature convergence, ensuring a thorough search of the complex hyperparameter space. The proposed methodology is rigorously evaluated using thermal images acquired from a heavy-duty belt conveyor system at the JARO S.A. mine. The optimized ResNet-50 model achieved a remarkable validation accuracy of 97.22%, demonstrating superior performance. Comparative analysis showed that our model significantly outperformed other state-of-the-art deep learning architectures, such as InceptionV3 and ResNet-18, as well as other metaheuristic optimization algorithms, yielding a 15.6% improvement over the basic SSA. This robust performance, combined with efficient convergence, underscores the model’s capacity for accurate and timely fault identification, paving the way for proactive maintenance, reduced downtime, and enhanced safety in demanding mining environments. Full article

As industrial robots become increasingly essential to modern manufacturing and automation systems, ensuring their durability and operational integrity has emerged as a key concern. Traditional defect detection methods typically depend on labeled datasets and supervised learning techniques, which can be difficult and impractical to implement in real-world industries. In contrast, unsupervised learning presents a compelling alternative by facilitating anomaly detection and fault diagnosis without the need for labeled data. This article offers a thorough analysis of unsupervised learning techniques used in the health monitoring of industrial robots. We explore significant trends and key algorithms, such as clustering, autoencoders, and generative models, assessing their effectiveness in identifying faults and performance degradation. The research addresses the unique challenges associated with high-dimensional sensor data, variable operating conditions, and the lack of ground truth labels. Additionally, we highlight unresolved research questions and potential future directions, emphasizing the need for scalable, interpretable, and real-time solutions. This survey serves as a foundational reference for researchers and practitioners aiming to develop resilient and autonomous health monitoring systems for industrial robots. Full article

The supervisory control and data acquisition (SCADA) data are increasingly used for wind turbine anomaly detection, but purely data-driven methods may be limited by weak physical interpretability, class imbalance, and reduced generalization under changing wind-farm operating conditions. Although the Extreme Gradient Boosting (XGBoost) is effective for structured nonlinear classification, its use in SCADA-based anomaly detection remains affected by label quality, probability calibration, and cross-farm transferability. This paper validates a physics-informed XGBoost decision layer using residual-based indicators, including power-curve residuals, gearbox and generator thermal residuals, rotor-speed variance, active-power ratio, and wind-speed fluctuation. Comprehensive Anomaly Detection Benchmark for Wind Turbine SCADA Data (CARE) logbook labels are used as the reference labels, while 2σ, 3σ, and 4σ residual thresholds are evaluated as competing rule-based detectors. The decision layer is trained and internally tested using event-grouped chronological splits from Wind Farm A and externally evaluated on unseen Wind Farms B and C. The results show physically interpretable anomaly detection behavior, although performance varies across validation settings. Under external Farm A to Farm B/C transfer, XGBoost achieved row-level F1-scores of 0.6296 and 0.6551, respectively. Shapley additive explanations (SHAPs) link anomaly predictions mainly to thermal, power-conversion, and operating-context features. The findings support the proposed decision layer as an interpretable benchmark-validation framework, while showing that additional maintenance-log validation is required before definitive component-level fault-diagnosis claims can be made. Full article

Real-time industrial anomaly detection is increasingly shifting from cloud-based diagnosis to edge intelligence deployed close to machines. However, practical industrial scenarios are constrained by scarce fault samples, unknown anomaly types, cross-machine distribution shifts, strict false alarm requirements, data privacy restrictions, and limited edge device resources. To address these challenges, this paper proposes EPC-TinyAD, an energy- and privacy-aware compressed TinyML framework for reliable industrial anomaly detection on resource-constrained edge devices. EPC-TinyAD follows a normal-only learning paradigm and employs a tiny depthwise-separable CNN autoencoder as the deployable student model, guided by a wider teacher autoencoder during training. Instead of relying solely on reconstruction error, the proposed anomaly score integrates spectrogram reconstruction deviation, compact normal-center distance, and teacher–student distillation discrepancy. Masked spectrogram modeling is introduced to enhance few-shot normal representation learning, while domain-adversarial invariant embedding improves cross-machine generalization. To support reliable deployment, split and adaptive conformal thresholding calibrate anomaly decisions under target false alarm rates. Furthermore, federated training with clipped and noisy updates reduces raw industrial data exposure, and energy-aware compression integrates pruning, INT8 size estimation, model export, latency benchmarking, and Pareto analysis. Experiments on industrial anomaly detection data demonstrate that EPC-TinyAD achieves 96.5% accuracy, 95.4% recall, 96.1% F1 score, 0.964 AUROC, and 0.952 AUPRC over five random seeds. These results indicate that EPC-TinyAD provides a reliable, lightweight, privacy-aware, and deployment-oriented framework for industrial edge anomaly detection, while future work will further validate its runtime memory, latency, and power consumption on physical Raspberry Pi-, Jetson-, or MCU-class edge devices. Full article

This paper proposes a novel data-driven K-Gap metric method based on the Koopman operator for the detection and isolation of multiplicative faults in high-speed train suspension systems. A systematic comparison is conducted with a data-driven K-Gap approach implemented through the fuzzy modeling framework. First, Takagi–Sugeno (T–S) theory is employed to extend the K-Gap metric for nonlinear dynamic modeling of the suspension system. Subsequently, the Koopman operator framework is introduced to lift the system states into a high-dimensional observable space, enabling a globally linear representation of the system. Building upon Koopman-based stable kernel representation (SKR), a more accurate K-Gap residual metric is constructed. Finally, a unified fault diagnosis scheme is developed with the K-Gap metric as the core indicator, and the two approaches are experimentally compared in terms of their performance in detecting and isolating multiplicative faults. The experimental results demonstrate that the Koopman-based method significantly outperforms the T–S fuzzy model in terms of residual separability, fault classification accuracy, and diagnostic stability, confirming its effectiveness and superiority for fault diagnosis in complex nonlinear systems. Full article

Process industries, including chemical, petrochemical, energy, and wastewater treatment systems, operate under high-dimensional, nonlinear, dynamic, and safety-critical conditions. Data-driven process monitoring and fault detection and diagnosis (FDD) have progressed from multivariate statistical monitoring to machine learning and deep learning. Yet many deployed or prototype systems still behave mainly as fault classifiers: they detect deviations, but offer limited causal explanation, weak integration of plant knowledge, and insufficient support for safe operator action. Recent advances in large language models, retrieval-augmented generation, digital twins, explainable artificial intelligence, and multi-agent systems make it timely to revisit FDD as an agentic decision-support workflow. This focused review examines how agentic AI can support process-industry monitoring and diagnosis by integrating process data, engineering knowledge, model outputs, and safety constraints. We synthesize established FDD foundations, deep-learning-based FDD, process-safety context, bridging technologies, and emerging LLM- and agent-based studies. The review argues that the near-term value of industrial agents lies not in unrestricted autonomous plant control, but in safety-aware, explainable, and human-in-the-loop decision support. We propose a process-industry-oriented taxonomy of agents, summarize enabling technologies and representative application settings, and identify evaluation criteria, benchmark requirements, limitations, and deployment conditions for trustworthy industrial agents. Full article

Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion network (SIL-MMFN) for detecting and classifying photovoltaic panel operating states in solar insecticidal lamps. The method combines time-series measurements with short-time Fourier transform (STFT)-based time–frequency images. A convolutional image branch extracts spatial features from time–frequency representations, whereas a bidirectional GRU branch with attention models temporal dependencies in the original signals. In addition, physics-informed features based on the illumination–current residual and output power are introduced to enhance discriminative fault information. Field data collected from four agricultural deployment nodes were used to classify normal, open-circuit, and mismatch states. Experimental results show that the proposed method achieved an accuracy of 97.5%, precision of 96.7%, recall of 97.8%, and macro-F1 score of 97.3%, outperforming single-modality and representative comparison models. The results indicate that multimodal fusion helps reduce confusion between open-circuit and mismatch faults and provides a potential approach for operating-state monitoring and maintenance of agricultural photovoltaic equipment. In this study, fault diagnosis refers to the detection and classification of photovoltaic panel operating states, including normal, open-circuit, and mismatch conditions. Full article

Detecting and identifying faults in industrial chemical plants is essential for safe and stable operation, and modern monitoring systems increasingly rely on deep learning to classify faults from multivariate sensor data. A practical obstacle to adoption is trust: most deep-learning diagnosers reach their decisions through a classification layer that operators cannot inspect, making it hard to see how the model maps process signals to a particular fault. This study targets fault diagnosis on the Tennessee Eastman (TE) process, a standard benchmark of simulated chemical-plant sensor data, and asks whether this final decision stage can be made directly inspectable without sacrificing accuracy. We propose CNN–Transformer–KAN (CTKAN), a hybrid model that learns local temporal patterns with a one-dimensional convolutional encoder, captures global inter-time-step dependencies with a Transformer encoder, and classifies faults with a Kolmogorov–Arnold Network (KAN) head whose learnable B-spline activations can be plotted and examined individually, in place of a conventional multi-layer perceptron (MLP). On the TE benchmark, CTKAN attains a Macro-F1 of 91.38 ± 0.26% over ten independent runs, comparable to a CNN + Transformer + MLP ablation (91.21 ± 0.32%) and a capacity-matched MLP-head variant (91.43 ± 0.37%) within seed-to-seed variability. The main finding is therefore not a higher score: at matched capacity the KAN and MLP heads are statistically indistinguishable in accuracy, so the KAN head’s value is to add a directly inspectable view of the classification stage at no measurable accuracy cost, helping process engineers sanity-check how the diagnoser separates faults in safety-critical settings. Full article

Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1–2 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability. Full article

In modern power systems such as new energy generation and smart grids, inverters serve as core equipment for electrical energy conversion and transmission. Their operational reliability directly impacts system power supply quality and safety stability. Currently, research on inverter fault diagnosis technology primarily focuses on linear load conditions, with diagnostic method design and validation based on linear load characteristics. However, with the rapid advancement of power electronics technology, power electronic loads such as variable frequency drives, charging stations, and distributed power sources are increasingly prevalent in power systems. These loads exhibit nonlinear and time-varying characteristics under complex operating conditions, leading to a growing variety of inverter faults with significantly diversified and complex fault signatures. Traditional diagnostic methods fail to adapt to the unique characteristics of power electronic loads, making it difficult to accurately identify various faults. Consequently, they no longer meet the diagnostic demands of practical engineering scenarios. In addition, current diagnostic methods for open-circuit power transistors, intermittent faults, and sensor faults often employ different approaches, which consume significant controller resources and are prone to mutual interference, leading to false triggers. This paper takes a three-phase four-wire inverter as the research subject. Targeting the challenge of fault diagnosis under power electronic load conditions, it proposes a comprehensive diagnostic method capable of simultaneously diagnosing power switch open circuits, intermittent faults, and current sensor faults. First, the characteristics of various faults are analyzed. Subsequently, fault diagnosis variables are constructed using the actual arm voltage of the inverter and the ideal arm voltage. Logical rules for each type of fault are established, and diagnosis is performed through fuzzy logic inference. Finally, experiments validated the effectiveness of this fault diagnosis scheme, with open-circuit faults detected in less than 2 ms, intermittent faults in less than 0.5 ms, and sensor faults in less than 3 ms. Full article

Due to the low inertia and small internal resistance of the DC line, the short-circuit fault is more harmful to the DC microgrid than the AC microgrid. Therefore, rapid and accurate detection of faults in DC microgrids plays an important role in ensuring the stable operation of DC microgrids. In this paper, the residual generator is designed based on the state observer, and the fault diagnosis of the DC microgrid is achieved by analyzing and processing the residual signal. Firstly, a mathematical model is established for a single line, and the corresponding residual generator is designed by using the unknown input observer to achieve the fault detection of a single key protection line. Secondly, considering the high cost of fault detection for each line alone, a residual generator is established for the entire DC microgrid to achieve fault detection of the entire DC microgrid, which effectively reduces the cost of fault detection. Finally, the radial DC microgrid and the ring DC microgrid are simulated and verified respectively to ensure that the designed fault diagnosis method is applicable to both topologies. Full article

IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown and a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at an AUROC of 0.93), detecting 100% of injected faults, with a mean delay under 25 min. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW’s shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW’s shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together, these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on operating conditions, enabling the model to generalize across different load levels and motor power ratings without retraining. A lightweight BiLSTM architecture is employed, reducing computational complexity while maintaining high diagnostic performance. The proposed method is validated under various operating conditions, including different speeds, load variations, motor power ratings, and noisy conditions. The results demonstrate an overall classification accuracy of 99.65%, with reliable fault detection achieved within less than half of a fundamental cycle. The proposed approach provides an efficient, robust, and scalable solution for inverter fault detection and diagnosis, offering strong potential for practical deployment in modern motor drive systems. Full article