Jul 2026· 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)· pp. 1-8· 0 citations· 26 references
Abstract
Condition monitoring of solenoid valves is essential for ensuring system reliability across a wide range of industrial applications. Traditional strategies rely either on physics-based models, which often depend on uncertain parameters and fail to capture all failure modes, or on data-driven methods, which suffer from limited interpretability and robustness under varying conditions. This paper proposes a sequential framework that combines model-based parameter identification with machine-learning classification. Key parameters, i.e. coil resistance and piecewise-linear inductance, are extracted from an equivalent RL-circuit using an online Total Least Squares identification scheme. These parameter features serve as inputs to a Feedforward Neural Network classifier, which distinguishes between three flow quality states: Good, Leaking, and Stuck. Its classification performance is validated against a Support Vector Machine with a Gaussian kernel. This sequential approach improves interpretability by grounding the analysis in physical parameter features, and efficiency by reducing the size of training data. Experimental validation is performed on 48 solenoid valves subjected to accelerated lifetime testing. The proposed method achieves high classification accuracy, reaching 91.9% on a balanced test set and 99.1% on an unseen valve. The method shows reduced predictive capabilities in the transition regime between Good and Faulty states, where gradual degradation occurs. An ablation study shows the necessity of inductive features for reliable multi-class fault detection. Only using a resistance-based feature leads to binary fault classification.
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
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
Monitoring the health of bearings in industrial rotating machines is a major challenge for ensuring the reliability and continuous operation of installations. Conventional fault detection methods, based on multivariate control charts such as Hotelling’s T2, multivariate exponentially weighted moving average, or multivariate cumulative sum control chart, are limited by the complex nonlinear dynamics of the system. In this article, we propose an innovative monitoring approach based on the Koopman operator, allowing the linearization of a nonlinear system in an observed space and the application of drift detection techniques via an extended T2 control chart. The study is based on two experimental approaches: one using controlled simulated data to analyze the responsiveness and robustness of the model, and the other applied to real data from an industrial turbogenerator monitoring the vibrations, temperatures, and speeds of the front and rear bearings. Comparative results show that the Koopman-based T2 map detects defects earlier, with better accuracy under noise and a reduced false alarm rate compared to conventional methods. The integration of wavelet preprocessing, statistical feature extraction by sliding windows, and PCA representation of the trajectories enhances the robustness and interpretability of the model.
Erroumayssae Sabani, E. Loualid, H. Mastouri et al.· Engineer· 0 citations
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.
Yanjie Zhong, Renpu Fu, Haiyang Wang et al.· 2026 6th International Confe...· 0 citations
The results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications.
A. Oner, Meral Bayraktar· Italian National Conference...· 0 citations
