SVM Algorithm for Vibration Fault Diagnosis in Centrifugal Pump

Dutta, Nabanita; Palanisamy, K.; Shanmugam, P.

doi:10.32604/iasc.2023.028704

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…To validate the effectiveness of the BO-based CBAM-ResNet model constructed in this study for fault diagnosis, ResNet and CBAM-ResNet were established, and the number of network layers and parameters, such as convolution kernel sizes and step sizes, of these models were set to be the same as those of the constructed BO-based CBAM-ResNet model. These models were trained using the same samples; the diagnostic results of the models are listed in table 4.…”

Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

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Fault diagnosis for spent fuel shearing machines based on Bayesian optimization and CBAM-ResNet

Wang,

Chen,

Wang

et al. 2023

Meas. Sci. Technol.

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Spent fuel shearing machines in nuclear power plants are important equipment for the head end of spent fuel reprocessing in power reactors. Condition monitoring and fault diagnosis play important roles in ensuring the safe operation of spent fuel shearing machines, avoiding serious accidents, and reducing their maintenance time and cost. Existing research on fault diagnosis of spent fuel shearing machiness has some shortcomings: (a) the current research on fault diagnosis of shearing machines is small and diagnostic accuray is not high. The research methodology of shearing machines needs to be updated; (b) the high difficulty in obtaining fault data and the often limited and highly informative fault data for shearing machines lead to low diagnostic performance. To solve these problems, this study constructs a residual network model based on Bayesian optimization and CBAM. First, dual-channel difference method is introduced into the preprocessing of noise signals, and two data enhancements were applied to the Mel spectrograms used as inputs to the model. Second, the attention mechanism CBAM is introduced to improve the residual network to enhance the deep feature extraction ability of the network, and the Bayesian optimization algorithm is used to train the hyperparameters, such as the optimizer, and retrain the network model after obtaining the optimal hyperparameters. Finally, the feasibility and effectiveness of the proposed model are verified through experiments on the noise signals of spent fuel shearing machines. The experimental results show that the diagnostic accuracy of the constructed model is 93.67%, which is a significant improvement over the other methods.

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Section: Comparative Analysis Of Resultsmentioning

confidence: 99%

“…Currently, machine learning and deep learning are common methods used for data-driven fault diagnosis and target identification [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Deep learning theory has gradually attracted widespread attention in academia and industry.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis for spent fuel shearing machines based on Bayesian optimization and CBAM-ResNet

Wang,

Chen,

Wang

et al. 2023

Meas. Sci. Technol.

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“…By maximizing the distinction between different classes of data, LDA creates a lower-dimensional representation that enhances the classification task for subsequent algorithms. This distinct advantage of LDA—amplifying class separability—provides a foundation for algorithms such as k-nearest neighbor (KNN) and support vector machines (SVMs) to operate more effectively and make better-informed decisions in the reduced-feature space [ 46 ]. For a training dataset consisting of

and two classes

and

, a mathematical representation of the two critical properties is given in Equation (1).…”

Section: Technical Backgroundmentioning

confidence: 99%

Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis

Ullah,

Ahmad,

Kim

2024

Sensors

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This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time–frequency domains, forming a comprehensive feature pool. Recognizing that not all features adequately represent MCP conditions and can reduce classification accuracy, we propose a novel ER-LDA method. ER-LDA evaluates feature importance by calculating the explanatory ratio between interclass distance and intraclass scatteredness, facilitating the selection of discriminative features through LDA. This fusion of ER-based feature assessment and LDA yields the novel ER-LDA technique. The resulting selective feature set is then passed into a k-nearest neighbor (K-NN) algorithm for condition classification, distinguishing between normal, mechanical seal hole, mechanical seal scratch, and impeller defect states of the MCP. The proposed technique surpasses current cutting-edge techniques in fault classification.

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“…to implement a fault detection strategy in the designed induction motors under variable load conditions [11]. Dutta et al present a case study of a machine-learning (ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive [12]. These studies have improved the correct rate of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization

Chen,

Zhao,

Jia

et al. 2024

Applied Sciences

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Conventional deep learning methods for fault detection often assume that the training and the testing sets share the same fault domain spaces. However, some fault patterns are rare, and many real-world faults have not appeared in the training set. As a result, it is hard for the trained model to achieve desirable performance on the testing set. In this paper, we introduce a novel domain generalization, Load-Domain (LD) domain generalization, which is based on the analysis of the Case Western Reserve University (CWRU) bearing dataset and takes advantage of the physical information of this dataset. For this scenario, we propose a feature shift model called Feature Shift Network (FSN). FSN is trained for feature shift on adjacent source domains and finally shifts target domain features into adjacent source domain feature space to achieve the purpose of domain generalization. Furthermore, through the hybrid classification method, the generalization performance of the model on unseen target domains is effectively improved. The results on the CWRU bearing dataset demonstrate that FSN is better than the existing models in the LD domain generalization. Furthermore, we have another test on the rotated MNIST, which also shows FSN can achieve the best performance.

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SVM Algorithm for Vibration Fault Diagnosis in Centrifugal Pump

Cited by 8 publications

References 30 publications

Fault diagnosis for spent fuel shearing machines based on Bayesian optimization and CBAM-ResNet

Fault diagnosis for spent fuel shearing machines based on Bayesian optimization and CBAM-ResNet

Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis

FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization

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