Unsupervised Fault Detection With a Decision Fusion Method Based on Bayesian in the Pumping Unit

Pan, Yijun; An, Ruqiao; Fu, Dianzheng; Zheng, Zeyu; Yang, Zihao

doi:10.1109/jsen.2021.3103520

Cited by 15 publications

(8 citation statements)

References 33 publications

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“…In literature [7], the end-ring wear detection through a multicomponent approach is researched through simulation, laboratory results, and the diagnosis of two-field motors showing that new fault alarm levels need to be defined. Literatures [8][9][10][11][12] present pump health state recognition methods based on data fusion, machine learning and probability. Literature [8] proposes a decision fusion method based on Bayesian probability formula, and obtains the effective evaluation result of pump state.…”

Section: Introductionmentioning

confidence: 99%

“…Literatures [8][9][10][11][12] present pump health state recognition methods based on data fusion, machine learning and probability. Literature [8] proposes a decision fusion method based on Bayesian probability formula, and obtains the effective evaluation result of pump state. Literature [9] proposes an unsupervised learning algorithm named mixture slow feature analysis (MSFA) to timely evaluate the health states.…”

Section: Introductionmentioning

confidence: 99%

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Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Liu

Zhu

et al. 2023

Electronics

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Main circulation pump is the only high-speed rotating equipment in primary loop of nuclear power plant. Its function is to ensure the normal operation of primary loop system by controlling the circulating flow of reactor coolant. In order to ensure long-term healthy operation of nuclear power main circulating pump, a method for identifying the health states of nuclear power main circulating pump based on ensemble empirical mode decomposition (EEMD) and support vector machine optimized by optimized quantum genetic algorithm (OQGA-SVM) is proposed. Vibration signal of main circulating pump is decomposed by EEMD. Vibration signal characteristics of nuclear power main circulating pump in healthy state and different fault states are analyzed and target characteristic indexes are put forward. Then, health state identification model of main circulation pump of OQGA-SVM is established, and target characteristic indexes are used as input parameter of the model. Finally, combined with experimental data, the model analysis and validation show that the health state identification method of nuclear power main circulating pump based on EEMD-OQGA-SVM can accurately and effectively identify the states of main circulation pump, has a higher identification accuracy than EEMD-SVM method and is more efficient and accurate than EEMD-QGA-SVM method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Liu

Zhu

et al. 2023

Electronics

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“…Currently, the power diagram analysis method, expert system diagnosis method, computer diagnosis method, and the neural network diagnosis method, which has become popular in recent years, are commonly used at home and abroad [1,2] . The literature [3][4] shows that the downhole power diagram analysis method is complicated and inconvenient to operate, while the surface power diagram analysis method is too dependent on theory, so there are many limitations in practical application.…”

Section: Introductionmentioning

confidence: 99%

A Fourier descriptor and PSCS-RBF fusion method for pumping machine fault diagnosis

Raja

Li³

et al. 2023

Preprint

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For the current oilfield pumping machine fault diagnosis, there are time-consuming and inefficient problems, and at the same time high requirements for hardware resources and no universality, this study proposes a pumping machine fault diagnosis method based on improved Fourier descriptor combined with dynamic adaptive cuckoo search (PS cuckoo search, PSCS) to optimize RBF neural network. Firstly, the starting point position of the contour of the power diagram is determined by the minimum inertia axis, and the Fourier transform is performed to achieve the best matching between contours, and the effect of starting point irrelevance. Then feature extraction is performed by combining shape invariant moments as the input layer information of RBF neural network. Then dynamic discovery probability and adaptive step size are introduced to make the cuckoo search easier to retain the better solution, and the step size can be automatically adjusted according to the convergence rate of the objective function to maintain a balanced state of efficiency and accuracy in different search stages. Finally, the RBF neural network is optimized by the improved cuckoo search to obtain the optimal relevant parameters such as the width and weights of RBF, and the PSCS-RBF fault diagnosis model is established. The model is applied to the diagnosis of different fault types of pumping machines and is compared and analyzed with a variety of current mainstream models. The average detection accuracy of the PSCS-RBF fault diagnosis method proposed in the article reaches 96.3%, and the measured results have high accuracy and short time, which verifies the practical value and advantages of the method.

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“…Currently, the power diagram analysis method, expert system diagnosis method, computer diagnosis method, and the neural network diagnosis method, which has become popular in recent years, are Widely used domestically and internationally [ 1 , 2 ]. The literature [ 3 , 4 ] show that the downhole power diagram analysis method is complicated and inconvenient to operate, and the surface power diagram analysis method is too dependent on theory, so there are many limitations in practical application.…”

Section: Introductionmentioning

confidence: 99%

Pumping machine fault diagnosis based on fused RDC-RBF

Li,

Raja,

et al. 2023

PLoS ONE

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At present, the fault diagnosis of pumping units in major oil fields in China is time-consuming and inefficient, and there is no universal problem for high requirements of hardware resources. In this study, a fault fusion diagnosis method of pumping unit based on improved Fourier descriptor (IDF) and rapid density clustering RBF (RDC-RBF) neural network is proposed. Firstly, the minimum inertia axis of the center of gravity of the indicator diagram is obtained. The farthest point of the intersection of the inertial axis and the contour is determined as the starting point. Then Fourier transform is performed on the contour boundary of the graph to obtain the feature vector. Then, combining with the idea of fast density clustering algorithm, the number of hidden layer neurons of RBF is determined by finding the point with the highest density and using it as the hidden layer neuron. At the same time, the characteristics of Gaussian function are introduced to ensure the activity of hidden layer neurons. Finally, through dynamic adaptive cuckoo search (DACS), the step size is automatically adjusted according to the convergence speed of the objective function of RBF, and the efficiency and accuracy of RBF in different search stages are balanced. The optimal parameters such as the width and weight of RBF are determined, and the optimal RDC-RBF fault diagnosis model is established. The model is applied to the diagnosis of different fault types of pumping units, and compared with the current mainstream models. The average detection accuracy of the fusion RDC-RBF fault diagnosis method proposed in this paper reaches 96.3%. The measured results have high accuracy and short time. At the same time, this method is currently applied to oil production sites such as Shengli Oilfield in China, which greatly reduces the human resources required for fault diagnosis of pumping units in the past.

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Unsupervised Fault Detection With a Decision Fusion Method Based on Bayesian in the Pumping Unit

Cited by 15 publications

References 33 publications

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

A Fourier descriptor and PSCS-RBF fusion method for pumping machine fault diagnosis

Pumping machine fault diagnosis based on fused RDC-RBF

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