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Centrifugal pump impeller defect identification by the improved adaptive variational mode decomposition through vibration signals

Engineering Research Express, ISSN: 2631-8695, Vol: 3, Issue: 3
2021
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Article Description

Anameliorated salp swarmalgorithm(ASSA) is proposed to enhance the exploration and exploitation stages of the basic salp swarmalgorithmusing the concept of opposition based learning and position updation.These concepts not only resolve the issue of slowconvergence but also reduces the computation time and circumvent strucking in localminima.Based on ASSA, an improved adaptive variationalmode decomposition (VMD)method has been proposed to identify the impeller fault in the centrifugal pump. The optimal combinations ofVMDparameters:mode number and quadratic penalty factor, are selected adaptively to decompose the vibration signal.Ondecomposition, the sensitivemode is identified for the extraction of fault features.The basis of identification of sensitivemode is a maximumvalue of weighted kurtosis.The ranking of fault features is done by Pearson correlation coefficient (PCC). The ranked features train the extreme learningmachine (ELM)model and further, themodel is tested for fitness evaluation. The overall training accuracy of the ELMmodel is found to be 100%with 0.0012 seconds of training time.The testing accuracy was found to be 97.5%. Results obtained at twenty-three classical benchmark functions and theWilcoxon test validate the efficiency and superiority of the proposed ASSA algorithmin the diagnosis of centrifugal pump impeller faults.

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