Search Results (251)

Conventional thresholding methods for rotorcraft Health and Usage Monitoring Systems (HUMS) often neglect flight-condition variability, resulting in frequent false alarms. To address this, a flight-state-driven threshold optimization framework that explicitly incorporates flight parameters and operational context is proposed. The proposed method combines proactive spike filtering with Principal Component Analysis (PCA) of flight parameters to distinguish flight-state-driven Condition Indicator (CI) variations from spike-like artifacts, and then re-estimates thresholds from the filtered CI distribution. The framework is evaluated using HUMS data collected from in-service rotorcraft, focusing on vibration- and fatigue-sensitive transmission components. Quantitative results show that the framework significantly reduces the Background Alarm Rate (BAR) to approximately 0.030 compared to the baseline of 0.202, while maintaining a high In-window Alarm Concentration (IAC) comparable to conventional methods. These validation results using real fault cases confirm the practical applicability of the approach to operational rotorcraft environments, indicating that the framework effectively reduces unnecessary alarms and enhances the stability and reliability of fault detection compared with conventional methods. The proposed framework offers an explainable, consistent, and operationally grounded basis for periodic threshold reviews in HUMS. It complements existing practices in Condition-Based Maintenance (CBM), providing a practical pathway to enhance confidence in vibration-based diagnostics under diverse flight conditions. Full article

This work focuses on the development of a diagnostic approach for detecting and localizing sensor faults in an autonomous photovoltaic system. The considered system is composed of a photovoltaic module and a resistive load. However, an adaptation stage formed by a DC/DC voltage boost converter is necessary to transfer energy from the source to the load. The diagnostic scheme is based on a sliding mode observer (SMO) that is robust to uncertainties and parametric variations. The SMO incorporates adaptive gains optimized via parametric adaptation laws, with stability rigorously verified through Lyapunov analysis. The method effectively identifies both independent and simultaneous sensor faults, employing an optimized threshold selection strategy to balance detection sensitivity and false alarm resistance. Simulation results under varying environmental conditions, system parameter fluctuations, and noisy measurement demonstrate the approach’s superior performance, achieving a 20% reduction in mean absolute percentage error (MAPE) and 90% faster settling time compared to existing techniques. These enhancements immediately increase the dependability, efficiency, and lifetime of the PV system, which are critical for lowering carbon emissions and ensuring the economic feasibility of solar energy investments. Key innovations include a novel residual generation mechanism, seamless integration with backstepping sliding mode maximum power point tracking (MPPT) control, and enhanced transient response characteristics. Full article

The safe and reliable operation of energy storage stations critically depends on their thermal management systems, specifically the health states or working conditions of involved sensors, such as temperature, humidity, and pressure sensor. Impacted by several environmental factors, some indiscernible defects including signal drift, elevated noise, and response lag may affect the exact surveillance of batteries, leading to potential combustion or even explosion, which requires fault risk early-warning to support timely maintenance. These multi-sensor environmental factor data typically exhibit mixed characteristics, component coupling, and high uncertainty, thus impacting diagnostic accuracy and robustness. With this motivation, this study proposes a pattern-guided framework for vulnerability diagnosis using Component Importance Measure. A pattern-guided strategy is first designed to perform rule induction and fuzzy processing on discrete and continuous sensor data, respectively, to extract underlying vulnerability-related components. Subsequently, a component Importance Measure, which assesses the impact of individual risks on the whole reliability, is established to achieve unified integration and mapping of previous heterogeneous information, therefore providing multidimensional vulnerability representations. An empirical case study demonstrates the fault detection rate, false alarm control, and diagnostic stability of the proposed framework. Full article

This review synthesises fault detection and diagnosis (FDD) methods for robotic actuation in healthcare, where precise, compliant, and safe physical human–robot interaction (pHRI) is essential. Actuator families—harmonic-drive electric transmissions, series-elastic designs, Cable/Bowden mechanisms, permanent-magnet synchronous motors (PMSM), and force–torque-sensed architectures—are mapped to characteristic fault classes and to sensing, residual-generation, and decision pipelines. Four methodological families are examined: model-based observers/parity relations, parameter-estimation strategies, signal-processing with change detection, and data-driven pipelines. Suitability for pHRI is assessed by attention to latency, robustness to movement artefacts, user comfort, and fail-safe behaviour. Aligned with ISO 14971 and the IEC 60601/80601 series, a validation framework is introduced, with reportable metrics—time-to-detect (TTD), minimal detectable fault amplitude (MDFA), and false-alarm rate (FAR)—at clinically relevant thresholds, accompanied by a concise reporting checklist. Across 127 studies (2016–2025), a pronounced technology-dependent structure emerges in the actuator-by-fault relationship; accuracy (ACC/F1) is commonly reported, whereas MDFA, TTD, and FAR are rarely documented. These findings support actuation-aware observers and decision rules and motivate standardised reporting beyond classifier accuracy to enable clinically meaningful, reproducible evaluation in contact-rich pHRI. Full article

Fault diagnosis of battery energy storage systems (BESSs) in dynamic operating conditions presents significant challenges due to complex spatiotemporal patterns and measurement noise. This research proposes a novel thermal fault diagnosis framework for BESSs based on Bayesian inference and a Kalman filter. Firstly, PLS-based spatiotemporal feature extraction is designed to capture temporal dependencies. Based on Bayesian global exploration and Kalman real-time weight adaptation, a dual-stage optimization strategy is proposed to derive a multiscale detection index with the dominant statistic, the residual statistic, and the module voltage similarity. A time window-based cumulative contribution strategy is constructed for precise cell localization. Finally, the experimental validation on a Li-ion battery pack demonstrates the proposed method’s superior performance: 96.92–99.90% anomaly detection rate, false alarm rate ranging from 0.10% to 7.22%, detection delays of 1–27 s, and 100% accuracy in fault localization. The proposed framework provides a comprehensive solution for safety management of BESSs and is significant for battery life and energy sustainability. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

Advances in motor technology and the application of distributed electric propulsion systems have greatly promoted the development of electric vertical take-off and landing aircraft. As a critical safety component of eVTOL aircraft, the motor system design must satisfy both performance requirements and stringent airworthiness standards. This paper studies the lift–thrust unit drive motor system of an eVTOL aircraft and proposes an architecture that utilizes analytical redundancy to enhance system-level reliability. This paper focuses on threshold optimization in analytical redundancy systems. Through simulations and reliability analyses, the performance of the analytical redundancy system is quantified, with false alarm and missed detection probabilities evaluated, fault detection thresholds optimized, and overall system reliability enhanced analytical redundancy systems is improved. Simulation and calculation results demonstrate that the proposed fault detection method can effectively meet the requirements for rapid detection and achieve optimal reliability at the given optimal threshold. Full article

Single-phase ground faults (SPGFs) in isolated neutral medium-voltage networks are difficult to detect, especially under high transient resistance. This paper proposes a centralized ground fault protection unit (CGFPU) that combines zero-sequence current (ZSC) magnitude and phase-angle analysis to enhance selectivity. Simulation results show that as transient resistance increases from 1 Ohm to 10 kOhm, fault currents decrease significantly, yet the CGFPU reliably identifies the faulty feeder by exploiting the characteristic 180° phase shift of ZSC phasors. The method remains selective with angular deviations up to ±20° and distinguishes between feeder and busbar faults. Compared with conventional amplitude- or model-based techniques, the proposed approach achieves faster detection, lower computational complexity, and robustness against unbalanced and charging currents. Furthermore, the CGFPU operates adaptively in alarm or trip mode depending on fault severity, thus preserving continuity for high-resistance faults and ensuring rapid isolation of bolted faults. These contributions establish a practical, scalable, and future-ready solution for SPGF protection in medium-voltage isolated neutral networks. Full article

In complex process systems, accurate real-time anomaly detection is essential to ensure operational safety and reliability. This study proposes a novel detection method that combines information granulation with kernel principal component analysis (KPCA). Here, information granulation is introduced as a general framework, with the principle of justifiable granularity (PJG) adopted as the specific implementation. Time series data are first granulated using PJG to extract compact features that preserve local dynamics. The KPCA model, equipped with a radial basis function kernel, is then applied to capture nonlinear correlations and construct monitoring statistics including $T^2$ and SPE. Thresholds are derived from training data and used for online anomaly detection. The method is evaluated on the Tennessee Eastman process and Continuous Stirred Tank Reactor datasets, covering various types of faults. Experimental results demonstrate that the proposed method achieves a near-zero false alarm rate below 1% and maintains a missed detection rate under 6%, highlighting its effectiveness and robustness across different fault scenarios and industrial datasets. Full article

Modular process plants represent a promising strategy to address the increasing need for flexibility and accelerated market deployment in the production of fine and specialty chemicals. However, these modular systems are inherently susceptible to wear and fault development, while condition monitoring methods tailored to such systems remain scarce. This study presents a proof of concept for a targeted fault diagnosis approach of the modular hydraulic systems of such modular process plants and reports on its experimental validation. The methodology comprises two stages: First, model-based symptoms are calculated independently for each module and subsequently utilized within a centralized diagnostic system. This rule-based diagnosis incorporates generalized module interactions, quantified fault degrees, and the plant topology. Importantly, uncertainties arising from measurement equipment, model fidelity, and parameter variability are incorporated and systematically propagated throughout the diagnosis. The validation was conducted on a modular test rig specifically designed to simulate a range of single-fault scenarios across more than 1200 stationary operating points. The results underscore the robustness of the proposed approach: the correct fault was consistently identified, with the estimated fault magnitudes closely aligning with the actual values, exhibiting an average discrepancy of 0.029 for internal leakage of a positive displacement pump. The overall discrepancy for the experimental validation of all fault types was 0.12. Notably, no false alarms were observed, and the displayed uncertainty was considered plausible, though there remains potential for refinement. In summary, this study demonstrates the successful application of model-based symptoms for a rule-based diagnosis, representing a significant advancement toward reliable fault detection in modular hydraulic systems. Full article

Unrecognized mechanical faults and emergency sounds in vehicles can compromise safety, particularly for individuals with hearing impairments and in sound-insulated or autonomous driving environments. As intelligent transportation systems (ITSs) evolve, there is a growing need for inclusive, non-intrusive, and real-time diagnostic solutions that enhance situational awareness and accessibility. This study introduces an interpretable, sound-based machine learning framework to detect vehicle faults and emergency sound events using acoustic signals as a scalable diagnostic source. Three purpose-built datasets were developed: one for vehicular fault detection, another for emergency and environmental sounds, and a third integrating both to reflect real-world ITS acoustic scenarios. Audio data were preprocessed through normalization, resampling, and segmentation and transformed into numerical vectors using Mel-Frequency Cepstral Coefficients (MFCCs), Mel spectrograms, and Chroma features. To ensure performance and interpretability, feature selection was conducted using SHAP (explainability), Boruta (relevance), and ANOVA (statistical significance). A two-phase experimental workflow was implemented: Phase 1 evaluated 15 classical models, identifying ensemble classifiers and multi-layer perceptrons (MLPs) as top performers; Phase 2 applied advanced feature selection to refine model accuracy and transparency. Ensemble models such as Extra Trees, LightGBM, and XGBoost achieved over 91% accuracy and AUC scores exceeding 0.99. SHAP provided model transparency without performance loss, while ANOVA achieved high accuracy with fewer features. The proposed framework enhances accessibility by translating auditory alarms into visual/haptic alerts for hearing-impaired drivers and can be integrated into smart city ITS platforms via roadside monitoring systems. Full article

Monitoring multimode industrial processes presents significant challenges due to varying operating conditions, nonlinear dynamics, and mode-dependent feature distributions. This paper proposes a novel process monitoring framework that integrates an enhanced Gaussian Mixture Model (GMM) for mode identification with Label Consistent K-SVD (LC-KSVD) for sparse dictionary learning. The improved GMM employs a parallelized Expectation–Maximization algorithm to achieve accurate and scalable mode partitioning in high-dimensional environments. Subsequently, the LC-KSVD then learns label-consistent, discriminative sparse representations, enabling effective monitoring across modes. The proposed method is evaluated through a simulation study and the widely used Continuous Stirred Tank Heater (CSTH) benchmark. Comparative results with traditional techniques such as LNS-PCA and FGMM demonstrate that the proposed method achieves superior fault detection rates (FDRs) and significantly lower false alarm rates (FARs), even under complex mode transitions and mild fault scenarios. Furthermore, the method also provides interpretable fault isolation through reconstruction-error-guided variable contribution analysis. These findings confirm that the proposed LC-KSVD-based scheme offers a reliable solution for fault detection and isolation in multimode process systems. Full article

As low-voltage distribution networks incorporate increasingly diverse loads, series arc faults exhibit weak characteristics that are easily masked by load currents, leading to high misjudgment rates in traditional detection methods. This paper proposes a series arc fault detection method based on an improved Light Gradient Boosting Machine (LightGBM) model. First, a test platform containing 12 household loads was built to collect arc data from both individual and composite loads. Composite loads refer to composite load conditions where multiple devices are running simultaneously and arcing occurs on some loads. To address the challenge of feature extraction, Variational Mode Decomposition (VMD) is employed to isolate the fundamental frequency component. To enhance high-frequency arc characteristics, singular value decomposition (SVD) is then applied. A multidimensional statistical feature set—comprising peak-to-peak value, kurtosis, and other indicators—is constructed. Finally, the LightGBM algorithm is used to identify arc faults based on these features. To overcome the LightGBM model’s limited ability to focus on hard-to-classify samples, a dynamic weighted hybrid loss function is developed. Experiments demonstrate that the proposed method achieves 98.9% accuracy across 223,615 sample groups. When deployed on STM32H723VGT6 hardware, the average fault alarm time is 83.8 ms, meeting requirements. Full article

Due to the increasing complexity of industrial systems, fault detection hinders the continuity of productivity. Also, many methods in industrial systems whose complexity increases over time have a mechanism based on human intervention. Therefore, the development of intelligent systems in fault detection is critical.. Avoiding false alarms in detecting real faults is one of the goals of these systems. Modern technology has the potential to improve strategies for detecting faults related to machine components. In this study, a hybrid approach was applied on two different datasets for fault detection. First, in this hybrid approach, data is given as input to the artificial neural network. Then, predictions are obtained as a result of training using the ANN mechanism with the feed forward process. In the next step, the error value calculated between the actual values and the estimated values is transmitted to the feedback layers. IWO (Invasive Weed Optimization) optimization algorithm is used to calculate the weight values in this hybrid structure. However the IWO optimization algorithm is designed to be initialized with fractal-based weighting. By this process sequence, it is planned to increase the global search power without getting stuck in local minima. Additionally, fractal-based initialization is an important part of the optimization process as it keeps the overall success and stability within a certain framework. Finally, a testing process is carried out on two separate datasets supplied by the Kaggle platform to prove the model’s success in failure detection. Test results exceed 98%. This success indicates that it is a successful model with high generalization ability. Full article

Accurate and timely detection of islanding events is essential for ensuring the stability and safety of hybrid power systems with high penetration of distributed energy resources. Traditional islanding detection methods often face challenges related to detection speed, false alarms, and robustness under dynamic operating conditions. This paper proposes a Robust Random Vector Functional Link Network (RVFLN)-based detection framework that leverages engineered features extracted from voltage, current, and power signals in a hybrid microgrid. The proposed method integrates statistical, spectral, and spatiotemporal features—including the Dynamic Harmonic Profile (DHP), which tracks rapid harmonic distortions during disconnection, the Sub-band Energy Ratio (SBER), which quantifies the redistribution of signal energy across frequency bands, and the Islanding Anomaly Index (IAI), which measures multivariate deviations in system behavior—capturing both transient and steady-state characteristics. A real-time digital simulator (RTDS) is used to model diverse scenarios including grid-connected operation, islanding at the Point of Common Coupling (PCC), synchronous converter islanding, and fault events. The RVFLN is trained and validated using this high-fidelity data, enabling robust classification of operational states. Results demonstrate that the RVFLN achieves high accuracy (up to 98.5%), low detection latency (average 0.05 s), and superior performance across precision, recall, and F1 score compared to conventional classifiers such as Random Forest, SVM, and k-NN. The proposed approach ensures reliable real-time islanding detection, making it a strong candidate for deployment in intelligent protection and monitoring systems in modern power networks. Full article