Fouling fault detection and diagnosis in district heating substations: Validation of a hybrid CNN-based PCA model with uncertainty quantification on virtual replica synthesis and real data

Jallal, Mohammed Ali; Vallée, Mathieu; Lamaison, Nicolas

doi:10.1016/j.energy.2024.133590

Energy

2024

DOI: 10.1016/j.energy.2024.133590

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Fouling fault detection and diagnosis in district heating substations: Validation of a hybrid CNN-based PCA model with uncertainty quantification on virtual replica synthesis and real data

Mohammed Ali Jallal,

Mathieu Vallée,

Nicolas Lamaison

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2024

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Cited by 2 publications

References 26 publications

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Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger

Qin,

Yan,

et al. 2024

Energies

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Heat exchangers play essential roles in the oil and gas production process for convective heat transfer and heat conduction. The health management of heat exchangers stays in the direct monitoring of performance parameters. Aiming at the difficulty of precise fault identification and quantification for heat exchangers in multiple unknown failure modes, a data-model fusion-driven fault quantitative diagnosis method is proposed. Firstly, based on the monitoring data such as temperature, pressure and flow rate, the secondary parameters characterizing the heat exchanger running state are constructed combined with structural physical parameters. Then, by analyzing the correlation among parameter variation, failure modes and deterioration degree, a qualitative inference model of heat exchanger is formed for fault identification, where weights of parameters are introduced based on their sensitivity for different failure modes. After the fault mode is identified, to achieve quantitative analysis of the failure degree, an index-integrated mechanism equation is constructed using monitoring data and secondary parameters, where the index is dynamically modified by online data. Finally, a heat exchanger experiment is carried out to demonstrate the robustness and accuracy of the proposed method.

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Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger

Qin,

Yan,

et al. 2024

Energies

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Real-World Steam Powerplant Boiler Tube Leakage Detection Using Hybrid Deep Learning

Khalid,

Azad,

Kim

2024

Mathematics

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The detection of boiler water-wall tube leakage in steam power plants is essential to prevent efficiency loss, unexpected shutdowns, and costly repairs. This study proposes a hybrid deep learning approach that combines convolutional neural networks (CNNs) with a support vector machine (SVM) classifier to allow early and accurate leak detection. The methodology utilizes temperature data from multiple sensors positioned at critical points in the boiler system. The data of each sensor are independently processed by a dedicated CNN model, allowing for the autonomous extraction of sensor-specific features. These features are then fused to create a comprehensive feature representation of the system’s condition, which is analyzed by an SVM classifier to accurately identify leakages. By utilizing the feature extraction capabilities of CNNs and the classification strength of an SVM, this approach effectively identifies subtle operational anomalies that are indicative of potential leaks. The model demonstrates high detection accuracy and minimizes false-positives, providing a robust solution for real-time monitoring and proactive maintenance strategies in industrial systems.

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Fouling fault detection and diagnosis in district heating substations: Validation of a hybrid CNN-based PCA model with uncertainty quantification on virtual replica synthesis and real data

Cited by 2 publications

References 26 publications

Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger

Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger

Real-World Steam Powerplant Boiler Tube Leakage Detection Using Hybrid Deep Learning

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