Failure analysis of ball bearing in centrifugal pump using envelope and demodulation techniques

Mishra, Lingaraj; Kumar, Mrityunjay; Chandrawanshi, M.L.

doi:10.1016/j.matpr.2022.02.252

Cited by 3 publications

(1 citation statement)

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“…The power spectral density and wavelet transform algorithm have been used to examine the cavitation noise characteristics in centrifugal pump [21]. Mishra et al investigated failure analysis of ball bearing in pump with envelope and demodulation techniques [22]. Kumar et al found a deep learning algorithm for identification of inlet pipe blockage level in centrifugal pump when the level speed over a range using multi-source data [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Fault identification of centrifugal pump using WGAN-GP method with unbalanced datasets based on kinematics simulation and experimental case

Li,

Chu,

Sun

et al. 2024

Meas. Sci. Technol.

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In practical engineering applications, the accuracy and stability of fault identification for centrifugal pump will be significantly reduced due to unbalanced distribution between normal and fault datasets, i.e., the number of normal working samples is far more than the fault samples. To alleviate this bottleneck issue, this paper explores the fault identification of centrifugal pump based on Wasserstein generative adversarial network with gradient penalty (WGAN-GP) through combining kinematics simulation and experimental case. Specifically, ideal unbalanced vibration datasets from failure patterns such as damaged impeller of centrifugal pump are simulated and collected by prototype ADAMS software, then the unbalanced vibration signals are transformed into 2D grey-scale images. Furtherly, the generated grey-scale image datasets are feed into the original grey-scale image dataset as new datasets for training when the Nash equilibrium of the WGAN-GP model is reached. Eventually, the fault patterns of centrifugal pump are identified using confusion matrix graph. Meanwhile, another public dataset of centrifugal pump is employed for verifying the accuracy of the WGAN-GP model. Results indicate that fault identification accuracies with 95.07% and 98.0% of both kinematics simulation and experimental case are obtained, respectively, and the issues of unbalanced distribution and insufficient dataset can be overcome effectively.

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Section: Literature Reviewmentioning

confidence: 99%