Search Results (244)

This paper proposes a novel integrated spectral kurtosis (ISK) technology, which is a new conceptualization for fault diagnosis, and compares it with conventional spectral kurtosis technology. The vibration signals from a gearbox are processed by time synchronous averaging (TSA) and analysed using the spectral kurtosis (SK). The ISK feature is estimated across the entire frequency domain, while the envelope is obtained through SK-based filtering and a Hilbert demodulation. The ISK technology demonstrates the ability to distinguish between healthy and defected gearbox cases, achieving a total probability of correct diagnosis (TPCD) of 91.5% for pinions and 96.1% for gears, whereas the SK-based squared envelope technology provides a limited diagnosis effectiveness, with a maximum TPCD of 80%. The motor current signals are also analysed through harmonic amplitude tracking within the current spectrum. A comparison of the ISK and motor current technologies is also made, showing that the motor current technology reaches a maximum of 90% TPCD for gears, which remains lower than the TPCD for the ISK technology. Full article

Modern machine tools are safety-critical mechatronic systems, yet shop floor maintenance from abnormal events still relies heavily on scarce expert know-how and time-consuming manual searches across heterogeneous controller documentation. This paper presents an on-premise multimodal AI assistant. It integrates large language models with retrieval augmented generation and real-time machine signals to support operator-in-the-loop fault diagnosis. The proposed system provides three tightly coupled functions: (1) alarm-grounded guidance, which answers controller alarms and recommends corrective actions by grounding generation on manuals, maintenance procedures, and historical alarm cases; (2) parameter-aware reasoning, which injects live process and health indicators (e.g., spindle temperature, vibration, and axis states) into the reasoning context through an industrial data pipeline, enabling context specific troubleshooting; and (3) vision enabled support, which retrieves similar visual cases and generates concise visual instructions when text alone is insufficient. The assistant is deployed within an intranet environment to satisfy industrial security and privacy requirements and is orchestrated via lightweight tool calling for seamless integration with existing shop floor systems. Experiments on real machine tool alarm scenarios demonstrate that the proposed system achieves 82% answer correctness for alarm Q&A and improves response consistency and time-to-resolution compared with baseline keyword search and template-based guidance. The results suggest that grounded, multimodal chatbot assistants can act as practical AI-based feedback and decision support mechanisms for mechatronic production equipment, bridging human skill gaps while enhancing reliability and maintainability. Full article

The presence of non-stationary features poses a major challenge to root cause diagnosis in industrial processes, as they can distort fault propagation paths inferred through causal testing. To address this issue, an adaptive moving-window dual-test Granger causality framework is proposed for non-stationary industrial processes. First, a dual non-stationary test mechanism, which integrates the Augmented Dickey–Fuller and Kwiatkowski–Phillips–Schmidt–Shin tests, is developed to assess the stationarity of process variables. Next, an adaptive moving-window strategy is designed to adjust window lengths based on the non-stationarity test results. Time series are then segmented according to the selected windows, and a vector error-correction model is fitted to provide a robust basis for causal testing. Subsequently, Granger causality tests are conducted within each window to capture the true causal relationships among variables. Finally, window-wise scores are aggregated to identify the root cause and infer the fault propagation path. The proposed framework is evaluated on the Tennessee Eastman Process, and the results demonstrate that it effectively improves the accuracy of root cause diagnosis. Full article

Quantum error correction is a prerequisite for quantum computing; however, the performance critically depends on the accuracy of the decoding algorithm. To address these challenges, we propose a hybrid decoding architecture, BP + UF + BP. The protocol initiates with a truncated global BP stage to extract probabilistic gradients without requiring full convergence. This soft information guides a reliability-based Union-Find (UF) algorithm to prioritize high-likelihood error mechanisms. Finally, a local subgraph BP refinement maximizes correction accuracy. Numerical simulations on rotated surface codes under circuit-level depolarizing noise demonstrate a fault-tolerance threshold of approximately 0.72%. This significantly outperforms standard Minimum Weight Perfect Matching (MWPM) and Union-Find (UF) baselines. Notably, our method significantly reduces the logical error rate compared to the conventional decoders. With its empirically near-linear scaling under fixed iteration, the proposed architecture presents a scalable solution for real-time fault-tolerant quantum computing. Full article

The aim of this study is to investigate and demonstrate the role of correction functions and optimisation-based refinement algorithms in enhancing the performance of machine learning models, particularly in predictive anomaly detection tasks applied in industrial environments. The performance of machine learning models is highly dependent on the quality of data preprocessing, model architecture, and post-processing methodology. In many practical applications—particularly in time-series forecasting and anomaly detection—the conventional training pipeline alone is insufficient, because model uncertainty, structural bias and the handling of rare events require specialised post hoc calibration and refinement mechanisms. This study provides a systematic overview of the role of correction functions (e.g., Principal Component Analysis (PCA), Squared Prediction Error (SPE)/Q-statistics, Hotelling’s T², Bayesian calibration) and adaptive improvement algorithms (e.g., Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), Simulated Annealing (SA), Gaussian Mixture Model (GMM) and ensemble-based techniques) in enhancing the performance of machine learning pipelines. The models were trained on a real industrial dataset compiled from power network analytics and harmonic-injection-based loading conditions. Model validation and equipment-level testing were performed using a large-scale harmonic measurement dataset collected over a five-year period. The reliability of the approach was confirmed by comparing predicted state transitions with actual fault occurrences, demonstrating its practical applicability and suitability for integration into predictive maintenance frameworks. The analysis demonstrates that correction functions introduce deterministic transformations in the data or error space, whereas improvement algorithms apply adaptive optimisation to fine-tune model parameters or decision boundaries. The combined use of these approaches significantly reduces overfitting, improves predictive accuracy and lowers false alarm rates. This work introduces the concept of an Organically Adaptive Predictive (OAP) ML model. The proposed model presents organic adaptivity, continuously adjusting its predictive behaviour in response to dynamic variations in network loading and harmonic spectrum composition. The introduced terminology characterises the organically emergent nature of the adaptive learning mechanism. Full article

In modern distribution networks (DNs), extreme weather events and cascading faults pose severe challenges to operational safety. However, existing defense mechanisms struggle with a core question: How to maintain high-fidelity situational awareness and make precise active decisions when physical parameters drift and historical fault data is scarce? To address this, this paper proposes a situational deduction and active defense framework based on a service-oriented digital twin. First, regarding the modeling fidelity gap, a data–physics fusion mechanism is constructed. By integrating Kirchhoff’s laws with data-driven error correction, it dynamically calibrates time-varying parameters to resolve mapping distortion. Second, regarding the data scarcity bottleneck, a predictive perception method is introduced. Utilizing the digital twin as a generative engine, it augments rare fault samples to enable super-real-time deduction of future trends. Third, regarding the decision-making passivity, a service-driven simulation model is established. It transforms abstract indicators (safety, economy, resilience) into executable constraints, shifting the paradigm from ‘passive response’ to ‘active defense.’ Case studies on a modified IEEE 123-node system demonstrate that the proposed method significantly enhances resilience and decision accuracy under complex conditions. Full article

The early detection and diagnosis of faults in induction motors is vital in today’s industry, since these are the motors used for the largest number of applications in the industrial environment and failure to detect a fault early can lead to significant losses. Bearing faults are the main problems detected in induction motors and several techniques have been developed to detect them. The use of the information contained in the motor vibrations is the main traditional source for its diagnosis, although there are also proposals that use the supply current, or the sound of the motor. Furthermore, these variables can be used in the time domain or in the frequency domain. The purpose of this work is to use explainable artificial intelligence (XAI) to determine which of these variables, and in which domain, contributes most to a correct diagnosis and how much can be gained in diagnosis by using multisensor data fusion. To carry out this comparison in the most objective way possible, a model selection procedure is proposed and boosting techniques are considered that prove to give a very precise diagnosis. The obtained diagnostic rules are then interpreted using SHAP values, a recent interpretation technique for complex classification procedures. Full article

This study explores the hydrochemical and thermal characteristics of a fault-controlled geothermal field within the Northern Qinghai Lake Coalfield Area on the northeastern Qinghai–Tibetan Plateau (QTP). This research integrates hydrochemical analyses, isotopic tracers, and the regional geological framework to define hydrochemical signatures, identify recharge sources and flow paths, assess cold–hot water mixing, estimate reservoir temperatures, determine circulation depths and residence times, and explain the geothermal system’s formation. Systematic sampling included geothermal waters, cold springs, and surface waters, followed by laboratory analysis of major ions, stable isotopes (δ²H, δ¹⁸O), radiocarbon (¹⁴C), and tritium (³H). The geothermal water is categorized as a low-temperature, weakly acidic to near-neutral HCO₃-Ca•Mg type, exhibiting temperatures from 35.6 to 46.2 °C. Isotopic analyses indicate that cold spring and river waters align with the local meteoric water line, while geothermal waters display distinct isotopic signatures, suggesting deeper circulation. A silica–enthalpy mixing model reveals substantial cold-water mixing during upwelling, with mixing ratios between 74.5% and 85.6%. The corrected recharge elevation is estimated to be 4378–4456 amsl, implying a primary recharge zone in the branch of the Qilian mountains—the middle section of Datong Mountain to the northeast. Geothermometry, employing quartz and chalcedony temperature scales and accounting for mixing, estimates reservoir temperatures of 150–202 °C. The calculated circulation depth spans 3211–4291 amsl. Low tritium levels and carbon dating suggest a deep-cycling system predating 1952, characterized by deeply circulating “ancient water”. The geothermal system’s development is associated with regional tectonics, fault systems, and the Kesuer Formation (J_xk) acting as the reservoir. This study provides a scientific foundation for the development and sustainable use of geothermal resources in the northern Qinghai Lake region and offers insights applicable to comparable fault-controlled geothermal systems across the QTP. Full article

In view of the highly coupled, time-varying, and susceptible to differences in aircraft configuration of the Boeing 787 Electric Environmental Control System (E-ECS), a simplified mechanism model based on effectiveness-number of transfer units is proposed. Firstly, considering the influence of differences in aircraft configuration, part number, and optional components, a heat conduction correction coefficient is introduced to adjust the calculation process of heat exchange efficiency. Secondly, the steady-state characteristic equation of the electric compressor/turbine is established by utilizing the principle of isentropic work. Then, the outlet temperature value of the water removal component is calculated by using secondary heat recovery technology. Finally, to solve the problem of easily getting stuck in local optima during high-dimensional parameter identification, an adaptive hybrid optimization algorithm combining Dung Beetle Optimization (DBO) with mutation operator and Particle Swarm Optimization (PSO) is proposed. The experimental results show that the proposed mechanism model can achieve dynamic representation of the outlet temperature of each component of E-ECS under different aircraft stages. The DBO-PSO algorithm has a fast convergence speed and a low probability of falling into local optima. The temperature values calculated by the model have high computational accuracy, which can provide reliable data support for component level E-ECS health monitoring and early fault warning. Full article

Steer-by-wire (SBW) systems in wheel-steering vehicles enhance maneuverability by eliminating mechanical linkages. However, they are susceptible to delays between steering input and pinion response, which can compromise control precision and safety. To mitigate these delays, we developed a machine learning-based compensation method employing a hybrid architecture of convolutional neural networks (CNNs) and gated recurrent units (GRUs) to predict and adjust pinion behavior in real time. The model was trained using experimental data collected from a four-wheel steering test platform, including steering angle inputs, motor signals, and pinion position feedback. By learning the relationship between steering commands and rack force, the model enables dynamic delay correction under both nominal and fault conditions. The system is implemented on an NXP microcontroller and validated through experimental testing, and compared with other hybrid model configurations for performance evaluation. The results demonstrate that the CNN–GRU approach reduces the average steering delay to 3 ms, outperforming conventional PID tuning methods while maintaining high accuracy and system stability. Full article

In the event of a fault in a shipboard medium-voltage direct-current (MVDC) power system, a fault reconfiguration method issues control commands to the switchgear to execute switching actions, thereby redistributing power flow, isolating the faulted zone, and restoring power to the de-energized loads. Existing fault reconfiguration strategies mainly use classical optimization methods. These methods are usually centralized, and as the system scale increases, they suffer from the curse of dimensionality, which degrades real-time performance and reduces computational efficiency. This paper proposes a MADRL-based fault reconfiguration method for shipboard MVDC power systems. The proposed method considers load priority levels, maximizes total restored load, and improves load balancing. To this end, a QMIX-based method, Dependency-Corrected QMIX with Action Masking (Dep-QMIX-Mask), was developed, introducing a dependency correction mechanism to handle action dependencies during decentralized execution and applying action masking to rule out invalid switching actions under operational constraints. A shipboard MVDC power system model was established and used for validation. Across three representative fault cases, Dep-QMIX-Mask achieves served load rates of 0.88, 0.67, and 0.43, with SLR improvements of up to 19.6% over baseline methods. It consistently produces feasible switching sequences in all 20 independent runs per case, improving feasibility by 10 to 30 percentage points. In addition, Dep-QMIX-Mask improves zonal load balancing by reducing the PUR variance by 40.5% to 99.2% compared with baseline methods. These results indicate that Dep-QMIX-Mask can generate feasible sequential reconfiguration strategies while improving both load restoration and load balancing. Full article

In electrical networks, the coordination and selectivity of protective devices are key to improving reliability and ensuring operational safety. Protections play a fundamental role in maintaining system stability and detecting faults within the power system. This study presents an optimized genetic algorithm (OGA) as a method to optimize the configurations of overcurrent protections in high voltage distribution systems. The OGA obtained the best results in all tested systems, demonstrating its effectiveness in coordinating protections according to IEC 60255-151:2009. In addition, simulations performed with the integration of Python and PowerFactory DigSILENT software validated the correct coordination of the protections, showing that the OGA not only optimizes response times, but also guarantees greater selectivity and reliability in the protection of the electrical system in an efficient way. Full article

This paper proposes a resource optimized cascaded quantum surface repetition code architecture integrated with a Union Find (UF) enhanced hybrid decoder, which suppresses biased noise and improves the scalability of quantum error correction through synergistic inner outer quantum code collaboration. The hybrid architecture employs inner quantum repetition codes for local error suppression and outer rotated quantum surface codes for topological robustness, reducing auxiliary quantum qubits by 12.5% via shared stabilizers and compact lattice embedding. An optimized UF decoder employing path compression and adaptive cluster merging achieves near-linear time complexity $\mathcal{O}\left(n\alpha \right(n\left)\right)$, outperforming minimum-weight perfect matching (MWPM) decoders $\mathcal{O}\left(n^{2.5}\right)$. Under Z-biased noise $\eta =10$, simulations demonstrate a 28.2% error threshold, 2.6% higher than standard quantum surface codes, and 15% lower logical error rates via dynamic boundary expansion. At code distance $d=7$, resource savings reach 9.3% with maximum relative error below 8.5%, fulfilling fault-tolerance criteria. The UF decoder exhibits 38% threshold advantage over MWPM at low bias $\eta \approx {10}^{-3}$ and 15% less degradation at high noise $p=0.5$, enabling scalable real-time decoding. This framework bridges theoretical thresholds with practical resource constraints, offering a noise-adaptive QEC solution for near-term quantum devices including photonic quantum systems referenced in the paper’s background on repetition cat qubits. Full article

Microgrids are commonly connected through medium-voltage cables in coastal distribution networks and other microgrids. However, a faulted microgrid may increase the collapse risk if the supporting microgrids are disconnected due to voltage sags. Conventional voltage support methods, which primarily rely on the impedance characteristics of the transmission line, typically regulate the active-to-reactive current ratio (hereafter referred to as “current ratio”) to maximize positive sequence voltage while minimizing negative sequence voltage. Nevertheless, the distributed capacitance inherent in cables induces deviations in both the amplitude and phase of the transmitted current, while simultaneously intensifying the coupling between voltage and current. These effects complicate the voltage fluctuation behavior and impair the effectiveness of voltage support, thereby increasing the risk of disconnection and collapse for the faulted microgrid (hereafter referred to as “fault region”). To address this challenge, this study focuses on non-faulted microgrids (hereafter referred to as “microgrids”), proposing a method for active current correction and transient voltage support that considers the influence of cable distributed capacitance. By analyzing the voltage and current characteristics on both ends of the interconnecting cables, the method optimizes the current injection ratio. It mitigates deviation caused by cable capacitance effects, thereby enhancing the voltage support performance of the microgrid. Notably, the proposed method operates independently of real-time voltage and current measurements from the fault region, significantly reducing communication demands. Experimental results based on a practical microgrid validate the effectiveness of the proposed method, demonstrating a 27.9% improvement in voltage support performance compared to conventional methods. Full article

Early fault diagnosis in induction motors is important to maintain correct operation in terms of energy and efficiency, as well as to achieve a reduction in costs associated with maintenance or unexpected stoppages in production processes. These motors are widely used in industry due to their reliability, low cost, and great robustness; however, over time, they may be exposed to wear that can affect their performance, endanger the integrity of operators, or cause unexpected shutdowns that generate economic losses. Corrosion in the bearings is one of the most common failures, which is mainly triggered by high humidity in combination with high temperatures. However, despite its relevance, it has not been widely explored as a cause of failure in induction motors. Unlike failures that occur in specific or localized areas, corrosion in bearings does not manifest through specific frequencies associated with the phenomenon, since the corrosion occurs extensively on the surface of the raceway, making early diagnosis difficult with conventional techniques based on spectral analysis. Therefore, this work proposes an approach for the analysis of magnetic stray flux and vibration signals under different levels of corrosion using statistical and non-statistical parameters to capture variations in the dynamic behavior of the motors while employing genetic algorithms to select the most relevant parameters for each signal and optimize the configuration of an ensemble learning algorithm. The classification of the bearing condition is achieved using support vector machines in combination with the bagging method, which increases the robustness and accuracy of the model in the presence of signal variability. A classification accuracy between the healthy state and two gradualities greater than 99% was obtained, indicating that the proposed approach is reliable and efficient for corrosion diagnosis. Full article