Fault Detection Based on Graph Model for Dead Zone of Steam Turbine Control Valve

Zhang, Yi-Jing; Yuan, Y.; Hu, Li-Sheng

doi:10.1007/s12555-021-0061-9

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Many studies employ physical modeling [5,6], statistical analysis [7,8], and machine learning [9][10][11] approaches to detect faults in control valves. Zhang et al [12] developed a graphical model capable of simultaneously detecting multiple faults while reducing dependence on statistical methods. Shi et al [13] proposed a method based on Intrinsic Mode Functions (IMF) and one-dimensional WDenseNet for diagnosing internal leakage faults in directional control valves.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition

Liu,

Zhao,

Zhang

2024

Actuators

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In the process industrial systems, flow control valves are deemed vital components that ensure the system’s safe operation. Hence, detecting faults in control valves is of significant importance. However, the stable operating conditions of flow control valves are prone to change, resulting in a decreased effectiveness of the conventional fault detection method. In this paper, an online fault detection approach considering the variable operating conditions of flow control valves is proposed. This approach is based on residual analysis, combining LightGBM online model with Seasonal and Trend decomposition using Loess (STL). LightGBM is a tree-based machine learning algorithm. In the proposed method, an online LightGBM is employed to establish and continuously update a flow prediction model for control valves, ensuring model accuracy during changes in operational conditions. Subsequently, STL decomposition is applied to the model’s residuals to capture the trend of residual changes, which is then transformed into a Health Index (HI) for evaluating the health level of the flow control valves. Finally, fault occurrences are detected based on the magnitude of the HI. We validate this approach using both simulated and real factory data. The experimental results demonstrate that the proposed method can promptly reflect the occurrence of faults through the HI.

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Section: Introductionmentioning

confidence: 99%

Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition

Liu,

Zhao,

Zhang

2024

Actuators

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“…Despite these contributions, research on pneumatic control valve troubleshooting remains somewhat underexplored. Efforts to bridge this gap have seen Zhang et al [9] develop a diagnostic model specifically for deadband faults in control valves, though its applicability to other fault types remains limited. Zhang et al [1] analyzed pneumatic control valve fault characteristics, leveraging expert experience and the particle swarm optimization (PSO) algorithm for an improved expert system-based diagnostic method.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of control valves based on small-sample hybrid physics improved Resnet

Xiaolin,

Hongkun,

Zhihua

2024

Meas. Sci. Technol.

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Pneumatic control valves, as vital components of industrial process automation, ensure the smooth operation of industrial production systems. However, they are susceptible to various malfunctions due to harsh working environments and complex transmission media, which can significantly impact production safety and efficiency. To address the challenge of obtaining fault data in actual operational settings, we constructed a fault test bench for pneumatic control valves and simulated a variety of fault conditions. We collected 421 fault data samples across 4 valve opening conditions, categorizing them into 27 distinct states with varying sample sizes, averaging 3-4 samples per state. To tackle the small-sample issue, we proposed a data augmentation method using periodic extension, validated through comparative analysis with other algorithms. Additionally, we innovatively analyze the data flow of pneumatic control valves and explore the relationships between different parameters. Based on these relationships, the input structure of the residual network is optimized. The above theoretical approach reduces the number of variables that need to be captured by the pneumatic control valve inspection system. Finally, through experiments under extreme conditions, our approach successfully diagnoses faults in 26 subclasses of pneumatic control valves, providing a reliable safeguard for industrial production safety and stability.

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Dynamic Simulation and Effect Analysis of Power Load Unbalance Protection for the Turbo-Generator Unit

Li,

Hu,

Qin

et al. 2024

Lecture Notes in Electrical Engineering

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Fault Detection Based on Graph Model for Dead Zone of Steam Turbine Control Valve

Cited by 8 publications

References 32 publications

Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition

Fault Detection of Flow Control Valves Using Online LightGBM and STL Decomposition

Fault diagnosis of control valves based on small-sample hybrid physics improved Resnet

Dynamic Simulation and Effect Analysis of Power Load Unbalance Protection for the Turbo-Generator Unit

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