Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT

ALTobi, Maamar Ali Saud; Bevan, Geraint; Wallace, Peter G.; Harrison, David K.; Ramachandran, K. P.

doi:10.1016/j.jestch.2019.01.005

Cited by 74 publications

(46 citation statements)

References 10 publications

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“…It has the advantages of simple operations, a small amount of calculations and a faster operating speed. Altobi et al [27] use the genetic algorithm to optimize the two parameters of a neural network (the numbers of neurons and hidden layers), which makes the optimized prediction model more accurate. Using the genetic algorithm to optimize parameters C and  can not only shorten the training time of the model, but also avoid over fitting and under fitting problems in the training process, and improve the prediction accuracy.…”

Section: B Genetic Algorithm Modelmentioning

confidence: 99%

Pressure Signal Prediction of Aviation Hydraulic Pumps Based on Phase Space Reconstruction and Support Vector Machine

Wang

et al. 2021

IEEE Access

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Section: B Genetic Algorithm Modelmentioning

confidence: 99%

Pressure Signal Prediction of Aviation Hydraulic Pumps Based on Phase Space Reconstruction and Support Vector Machine

Wang

et al. 2021

IEEE Access

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“…By the process of PCA, the dimension of NFIs data can be reduced, which facilitates the subsequent calculation of SVM. To evaluate the accuracy of the trained SVM classifier, the confusion matrix is introduced, which is used to compare the classification results with the actual measured values, and presented in a matrix form as listed in Table . It can be seen from Table that the obtained SVM classifier used in this study is able to identify the nonlinear model within high accuracy.…”

Section: Structural Dynamic Nonlinear Model Identificationmentioning

confidence: 99%

Structural dynamic nonlinear model and parameter identification based on the stiffness and damping marginal curves

Wang

Ding

Ren

et al. 2020

Struct Control Health Monit

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Summary This paper presents a novel structural dynamic nonlinear model and parameter identification method based on the stiffness and damping marginal curves. The stiffness and damping marginal curves are first extracted from the structural dynamic responses. Then, nine nonlinear feature indices (NFIs) are defined based on the marginal curves to describe the characteristics of various nonlinear models. To reduce the dimension of NFIs and facilitate the subsequent calculation of the support vector machine (SVM), the principal component analysis (PCA) is implemented. Afterwards, the NFIs processed by PCA are employed as the training samples to SVM classifier, which is used to identify the structural nonlinear model. According to the identified nonlinear model corresponding to the type of nonlinearity, the parameters of the nonlinear model are further identified by the nonlinear least square method. The numerical results of a single degree of freedom (SDOF) system and a cantilever beam structure demonstrate that the proposed method can effectively identify both nonlinear model and corresponding parameters under various excitations even considering the noise effect. Subsequently, the proposed method is also verified by a benchmark system subjected to a semisinusoidal pulse load and a beam bridge structure subjected to an earthquake load. The proposed method is also applied to the experimental test of a bolted joint cantilever beam subjected to a random excitation in the laboratory. Both numerical and test results demonstrate that the proposed method can identify both nonlinear model and corresponding parameters within good accuracy and robustness.

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“…Kumar et al introduced a new divergence function into the cost function, thereby reducing the complexity of the hidden layers, and finally the accuracy of diagnosis of centrifugal pump component defects was raised by 3.2% [ 30 ]. Al-Tubi et al used genetic algorithms to adjust hidden layers of support vector machines to achieve fault diagnosis of centrifugal pumps [ 31 ]. Siano et al combined fast Fourier transform with an artificial neural network to achieve the online detection of pump cavitation [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

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Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

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Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT

Cited by 74 publications

References 10 publications

Pressure Signal Prediction of Aviation Hydraulic Pumps Based on Phase Space Reconstruction and Support Vector Machine

Pressure Signal Prediction of Aviation Hydraulic Pumps Based on Phase Space Reconstruction and Support Vector Machine

Structural dynamic nonlinear model and parameter identification based on the stiffness and damping marginal curves

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

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