A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants

Wang, Hang; Peng, Minjun; Hines, J. Wesley; Zheng, Gangyang; Liu, Yong-kuo; Upadhyaya, B.R.

doi:10.1016/j.isatra.2019.05.016

Cited by 107 publications

(51 citation statements)

References 24 publications

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“…The Hu invariant moments are calculated based on the image features. The procedure is based on (12), (13), (14), (15), (16), (17), (18), (19), (20), (21), and (22). The Hu invariant moments of the samples are shown in Table 2.…”

Section: Feature Extraction Based On Hu Invariant Momentsmentioning

confidence: 99%

“…The fuzzy clustering method is used to cluster and identify the axis orbit of multiple faults in a hydropower station [20], and the gray correlation analysis method is used to automatically identify the axis orbit of water turbine generator units [21]. Some researchers have applied SVM to the fault diagnosis of nuclear power plants, centrifugal pumps, and flow control valves and other equipment [22][23][24]. The feature extraction and automatic identification of the axis orbit often use invariance pattern identification of its two-dimensional graphics, extract invariance features, and automatically perform identification.…”

Section: Introductionmentioning

confidence: 99%

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Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

Pang

Shao

Xue

et al. 2020

International Journal of Rotating Machinery

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The shape characteristic of the axis orbit plays an important role in the fault diagnosis of rotating machinery. However, the original signal is typically messy, and this affects the identification accuracy and identification speed. In order to improve the identification effect, an effective fault identification method for a rotor system based on the axis orbit is proposed. The method is a combination of ensemble empirical mode decomposition (EEMD), morphological image processing, Hu invariant moment feature vector, and back propagation (BP) neural network. Experiments of four fault forms are performed in single-span rotor and double-span rotor test rigs. Vibration displacement signals in the X and Y directions of the rotor are processed via EEMD filtering to eliminate the high-frequency noise. The mathematical morphology is used to optimize the axis orbit including the dilation and skeleton operation. After image processing, Hu invariant moments of the skeleton axis orbits are calculated as the feature vector. Finally, the BP neural network is trained to identify the faults of the rotor system. The experimental results indicate that the time of identification of the tested axis orbits via morphological processing corresponds to 13.05 s, and the identification accuracy rate ranges to 95%. Both exceed that without mathematical morphology. The proposed method is reliable and effective for the identification of the axis orbit and aids in online monitoring and automatic identification of rotor system faults.

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Section: Feature Extraction Based On Hu Invariant Momentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

Pang

Shao

Xue

et al. 2020

International Journal of Rotating Machinery

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“…As the search progresses, ϖ can be gradually reduced linearly, thereby enabling the particles to explore the optimal value region at a faster speed in the early stage of the algorithm. And in the late stage of the algorithm, it can perform fine search in the optimal value area, so that the algorithm has a greater probability of converging to the global optimal solution position [7]. is algorithm is called Linear Decreasing Weight particle swarm optimization (LDWPSO), and the expression is shown in formula (3):…”

Section: Competitive Multigroup Coevolutionmentioning

confidence: 99%

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

Xiao

Wang

et al. 2020

Shock and Vibration

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With the rapid development of high-power tractor, the fault diagnosis of high-power tractor has become more and more important for ensuring the operating safety and efficiency. PSO is an iterative optimization evolutionary algorithm, which can iterate through different particles to find the optimal solution. However, there is only one population in the standard PSO algorithm, and the information exchange between the populations is relatively single, which can easily lead to the stagnation of the development of the population. In this paper, due to high-power tractor diesel engine fault complexity, fault correlation, and multifault concurrency, a multigroup coevolution particle swarm optimization BP neural network for diesel engine fault diagnosis method was proposed. First, the USB-CAN device was used to collect data of 8 items of the diesel engine under five different working conditions, and the data was parsed through the SAE J1939 protocol; then, the BP neural network was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. Finally, the data of optimized neural network under five different fault conditions show that, compared with BP neural network and PSO optimized BP neural network, the fault diagnosis of COM-MCPSO optimized BP neural network not only improves the network training speed, but also enhances generalization ability and improves recognition accuracy.

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“…Finally, XGBoost is used to predict the results. In [20], a model combining a support vector machine and particle swarm optimization is proposed. This model selects the hyperparameter of the support vector machine by particle swarm optimization algorithm, and achieves good results in small-sample mixed fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Time Convolutional Network Based Outlier Detection for Multidimensional Time Series in Cyber-Physical-Social Systems

et al. 2020

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With the development of the Cyber-Physical-Social Systems(CPSS), a large number of multidimensional time series have been generated in today's world, such as: sensor data for industrial equipment operation, vehicle driving data, and cloud server operation and maintenance data and so on. The key task of Cloud-Fog-Edge Computing in managing these systems is how to detect anomalous data in a specific time series to facilitate operator action to solve potential system problems. So multidimensional time series outlier detection become an important direction of CPSS data mining and Cloud-Fog-Edge Computing research, and it has a wide range of applications in industry, finance, medicine and other fields. This paper proposes a framework called Multidimensional time series Outlier detection based on a Time Convolutional Network AutoEncoder (MOTCN-AE), which can detect outliers in time series data, such as identifying equipment failures, dangerous driving behaviors of cars, etc. Specifically, this paper first uses a feature extraction method to transform the original time series into a feature-rich time series. Second, the proposed TCN-AE is used to reconstruct the feature-rich time series data, and the reconstruction error is used to calculate outlier scores. Finally, the MOTCN-AE framework is validated by multiple time series datasets to demonstrate its effectiveness in detecting time series outliers. INDEX TERMS Time series, outlier detection, time convolution network, autoencoder.

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A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants

Cited by 107 publications

References 24 publications

Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

A Time Convolutional Network Based Outlier Detection for Multidimensional Time Series in Cyber-Physical-Social Systems

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