Knowledge addition for improving the transfer learning from the laboratory to identify defects of hydraulic machinery

Abstract

This work proposes a solution to improve transfer learning from laboratory environment to real-world hydraulic machinery (centrifugal pump) for the effective identification of defects. The proposed method involves collecting vibration data, normalizing and computing Fast Fourier Transform (FFT) of the data, and adding attributes by combining the FFT of healthy real-world machinery with the FFT of laboratory machinery. The attribute addition is performed in the frequency domain to address phase lag issues in time-domain data. The resulting signal is transformed back to the time domain, and the envelope spectrum is obtained. An approximation model is constructed using the envelope spectrum and refined using data from industrial machinery. The refined model is then employed to identify defects in real machinery, specifically the centrifugal pump. The proposed knowledge addition-based transfer learning achieves an accuracy of 93%, which is 51% higher than the accuracy attained by the domain-adversarial neural network method.