Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-7· 0 citations· 21 references
Abstract
Main steam isolation valves face severe challenges during monitoring under high-temperature, high-pressure, and noisy environments. These challenges include weak fault features, data blocking in multi-channel acquisition, and high false alarm rates. To address these issues, this paper proposes a dedicated intelligent fault diagnosis software based on Model-Based Design (MBD) and the PyQt5 framework. The software innovatively employs a QThread pool architecture to achieve non-blocking synchronous acquisition across 26 channels, effectively resolving resource contention under high-throughput data transmission. For diagnosis, the system integrates dual lightweight algorithms (SVM and LSTM) to balance computing constraints with precise status determination and trend prediction, while introducing a spatial weighted algorithm based on time-domain deviation for accurate fault localization. Crucially, a linear regression dynamic threshold model corrected by temperature and pressure is established to mitigate false alarms under non-stationary operating conditions. Validation using full-power operation data from a nuclear power plant demonstrates that the software limits synchronous acquisition delay to within 50ms and achieves a fault determination accuracy of 94%. Furthermore, the conditioncorrected mechanism reduces the false alarm rate by over 98% (to 0.07%) and shortens the average fault investigation cycle by 90%. This system effectively overcomes the lag in traditional monitoring, marking a critical shift from “passive repair” to “proactive prediction” in steam valve maintenance.
Hydromachinery is vital for clean and sustainable power generation, where reliable and efficient operation directly supports the stability of hydropower plants. To achieve this, real-time performance tracking and fault monitoring are becoming increasingly important. This review summarizes recent techniques and technologies used for monitoring turbines and their components in operation. Key areas include sensor-based data collection, modern signal processing tools, and artificial intelligence methods for detecting issues such as cavitation, vibration irregularities, pressure fluctuations, and mechanical wear. Methods like wavelet analysis, principal component analysis (PCA), support vector machines (SVM), and digital twins are discussed for their roles in fault diagnosis and performance evaluation. Advances in IoT-enabled monitoring and predictive maintenance are also highlighted, demonstrating their potential to enhance reliability and minimize downtime. The paper further outlines challenges such as harsh operating conditions, large data handling, and the need for accurate predictive models. Future directions are suggested, focusing on hybrid machine learning approaches, adaptive monitoring strategies, and digital twins for smart, autonomous health management of hydro machinery.
Juhi Padma, Hemant J. Sagar· IOP Conference Series: Earth...· 0 citations
As a key control component in fluid systems, fault diagnosis of pilot-operated solenoid valves is crucial for ensuring the stability and reliability of fluid systems. The highly overlapping fault signatures of solenoid valves, together with the industrial difficulty of mass-producing faulty valve specimens. Lead to misjudgment of diagnostic models and further system shutdowns under imbalanced sample distribution. To solve this problem, this paper proposes a fault diagnosis model for pilot-operated solenoid valves based on cross-recurrence quantification analysis (CRQA) and stacking heterogeneous ensemble learning. Firstly, the CRQA method is adopted to extract deep-seated features and expand the sample size. Secondly, classification and regression tree, K-Nearest Neighbor (KNN) and support vector machine are selected as base classifiers, while multinomial logistic regression is used as the meta-classifier to construct a Stacking heterogeneous ensemble model. Bayesian optimization is applied to adjust the hyperparameters of the classifiers, thereby improving the efficiency of ensemble model construction. Then, to tackle the imbalanced distribution of samples, a cost matrix is set up to compensate for misclassification by the model. Finally, fault sample data of solenoid valves are collected through the experimental platform. Ablation experiments and comparative analysis are conducted on the new method. The results show that the new method proposed in this paper achieves high accuracy and stability in fault diagnosis for sparse and imbalanced fault samples.
J. Pang, Yuanzhong Chen, Jinkun Dai et al.· Engineering Research Express· 0 citations
Pumped storage hydropower (PSH) is a key regulating resource for renewable energy integration and power system stability. Due to frequent start-stop operations, deep peak-load regulation, and bidirectional operating conditions, stator winding insulation degradation, rotor inter-turn short circuits, and end-winding vibration have become the dominant failure modes of pumped storage units. Conventional monitoring systems are limited by single-source sensing, asynchronous data acquisition, high misdiagnosis rates, and maintenance decisions that rely heavily on expert experience, making traditional periodic maintenance increasingly inadequate. To address these challenges, this study proposes an intelligent diagnosis and decision support system based on multi-source data fusion and hybrid intelligent algorithms. An Intelligent Electronic Device (IED)-based condition monitoring platform is developed by integrating multiple sensing technologies. Complete Variational Mode Decomposition (CVMD) and Kernel Principal Component Analysis (KPCA) are employed to extract representative features from multi-physical-field data, while an attention-enhanced Long Short-Term Memory (LSTM) network is introduced for accurate fault identification. In addition, adaptive time-alignment and joint denoising algorithms are developed to improve data quality and diagnostic robustness. A predictive maintenance framework incorporating health assessment and remaining useful life prediction is further established to optimize maintenance scheduling. Results demonstrate that the proposed system achieves a fault prediction accuracy of over 90% and reduces annual maintenance costs by approximately 15–20%. The proposed framework provides an effective solution for intelligent operation and maintenance of modern pumped storage units.
Xuan Liu, Jie Bai, Bingjie Dou et al.· Processes· 0 citations
Based on digital twin technology, this paper constructs a three-level coupled twin computing model of component-subsystem-whole machine, focusing on the health management requirements of automotive engines under complex working conditions. By integrating the spatio-temporal consistency modeling of multi-source sensor data, state variable encoding and dynamic update, it achieves fine reconstruction and evolution characterization of key working condition quantities. On this basis, a self-correction mechanism driven by virtual-real closed-loop residuals, multiscale health index representation, and an anomaly scoring model constrained by Mahalanobis distance are introduced. Combined with working condition adaptive thresholds and lightweight fault discrimination networks, a complete algorithm framework covering perception, diagnosis, and prediction is formed. Experimental results show that the proposed method maintains high diagnostic stability under typical working conditions such as steady state, high speed, cold start, and frequent acceleration and deceleration, with an average detection delay controlled within 20ms and an overall fault recognition accuracy rate exceeding 94%. It is suitable for online fault early warning and predictive maintenance scenarios of engines.
Chengchun Chen, Pufang Guan, Yunqin Li· The 2026 International Confe...· 0 citations
Experimental results on stator inter-turn faults across multiple low-load operating conditions demonstrate superior reconstruction performance compared with representative recurrent, transformer-based, and graph-based autoencoder models while maintaining computational efficiency suitable for low-latency deployment.
Chibuzo Nwabufo Okwuosa, J. Hur· IEEE Access· 0 citations
This article proposes a cable overheating state recognition method based on a metal–oxide–semiconductor (MOS) gas sensor array. Given the limited number of original experiments and the temporal misalignment caused by heating delay, gas diffusion, and sensor–response hysteresis, this work designs a response-aligned sliding-window strategy. Specifically, a reference sensor channel is used to locate the response onset, and an offset window is then constructed to capture the rising response stage with stronger state discrimination. Multidimensional features were extracted, and both traditional machine learning models and time-series classification models were systematically evaluated. MiniRocket achieved the best offline accuracy and F1-score, while compact traditional models such as K-nearest neighbors (KNNs) and support vector machines (SVMs) also achieved accuracies above 98%. Considering offline recognition performance, online stability, and STM32 resource constraints, selected models were deployed on an STM32 platform for online recognition every 15 s. Pressure experiments further showed that the deployed model can correctly identify heating states within the evaluated pressure range of 91.2–101.3 kPa. The proposed method provides an early warning approach for cable overheating and shows potential for embedded engineering applications.
Jia Zhang, Guishuai Ji, Tongtong Wu et al.· IEEE Sensors Journal· 0 citations
