Aug 2026· Engineering Research Express· Vol 8, pp. 165533· 0 citations· 32 references
Physics
TL;DR
A fault diagnosis model for pilot-operated solenoid valves based on cross-recurrence quantification analysis (CRQA) and stacking heterogeneous ensemble learning is proposed, achieving high accuracy and stability in fault diagnosis for sparse and imbalanced fault samples.
Abstract
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.
A multi-scale linear attention multi-source subdomain adaptation network (MLAMSAN) that integrates the multi-scale linear attention (MLA) that can achieve fault diagnosis under cross operating conditions through subdomain feature alignments that exhibits the superior diagnostic performance and the strong generalization ability.
Zheng Han, Yuqi Fan, Yaping Wang et al.· Engineering Research Express· 0 citations
In large office buildings and commercial complexes, HVAC systems account for nearly two-thirds of total electricity consumption. However, early fault detection and diagnosis (FDD) in water-cooled chillers remains challenging because faults usually develop slowly, produce weak initial signatures, and exhibit strongly nonlinear thermodynamic behavior. Moreover, overlapping operational characteristics between faults make accurate diagnosis under mild and moderate conditions difficult. Previous studies using the ASHRAE RP-1043 dataset reported limited diagnostic performance for incipient faults, particularly refrigerant leakage and condenser fouling. To address these gaps, this study proposes a hybrid optimization-based FDD framework for early fault diagnosis in water-cooled chillers, integrating the Non-Dominated Sorting Genetic Algorithm III with Local Search (NSGA-III-LS) and the M5 Prime regression model for hyperparameter tuning and nonlinear operational modeling. The proposed framework offers a balanced trade-off between fault sensitivity, residual stability, and diagnostic accuracy. Validation results demonstrate high detection rates of 62.5-95.83% for mild faults and nearly 100% for severe faults, outperforming conventional methods, especially during incipient fault stages. The proposed method also supports earlier detection of abnormal thermal behavior, contributing to energy savings of approximately 15-30%, extended equipment lifespan, and more effective predictive maintenance planning.
Thanh Duc Nguyen, Dinh Anh Tuan Tran· Journal of Engineering and S...· 0 citations
A transferred SISA (Sharded, Isolated, Sliced, and Aggregated) fault diagnosis framework is developed and applied to rolling bearing data, demonstrating a 84.32% decrease in retraining time compared to non-SISA full-retraining while restoring accuracy to the pre-poisoning SISA level.
Emily Yin, Jingyi Yan, Nanhong Liu et al.· 0 citations
An experimental/synthetic hybrid, data-driven FDI framework that leverages supervised machine learning (ML) integrated with an experimentally validated second-order electro-thermal battery model to generate a mixed experimental–synthetic dataset enables fast, real-time diagnosis of complex multi-fault scenarios at the cell or module level in series–parallel LIB pack architectures.
Taha Mohamed Abdelatif Maaradji, Saïd Alem, Emanuele Gravante et al.· Transactions of the Institut...· 0 citations
Results show that using statistical vibration features with ensemble classifiers is a good way to diagnose multi-class bearing faults and establishes a comprehensive benchmark for ML- and DL-based rolling bearing FDD.
M. I. Quamar, Abdulrazaq Nafiu Abubakar, Ali Nasir· Journal of Vibration Enginee...· 0 citations
Electromagnetic valves are widely utilized in electrohydraulic control systems because of simple structure, low cost, and fast response. Accurate diagnosis of electromagnetic valve faults is crucial to ensure high effective operation of the control systems. This paper proposes a faults diagnosis model of electromagnetic valve based on Bayesian optimization (BO) and a two‐layer long short‐term memory (LSTM) neural network. First, to fully extract information features from the fault samples, a two‐layer LSTM network structure is used as the core of the electromagnetic valve fault diagnosis framework. Second, the BO algorithm is adopted to automatically adjust the hyperparameters of the two‐layer LSTM network model. The BO algorithm restricts the search space of hyperparameters in each iteration, which enhances the algorithm's efficiency. Moreover, owing to the limited availability of fault samples, cross‐recurrence quantification analysis is utilized for data preprocessing to strengthen and expand the fault characteristic data of the electromagnetic valve. This method effectively improves the diagnostic efficiency of the model. Finally, a real case study on electromagnetic valves is conducted to validate the performance and feasibility of our proposed method. The fault diagnosis accuracy of our proposed method reaches 94.29%, which is significantly higher than other fault diagnosis methods.
J. Pang, Yuanzhong Chen, Jinkun Dai et al.· Quality and Reliability Engi...· 0 citations
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