A fault diagnosis method based on low signal to noise ratio vibration measurement for use in casing cutters

Abstract

In this paper, a fault diagnosis method for casing cutter was proposed, a vibration signal acquisition circuit used at high temperature environment was designed, and a casing cutter measurement model was established, including the model of the casing cutter in a trouble-free state and two other common fault states, the vibration characteristics of the model was analyzed. A fault feature enhancement model based on SNR enhancement and sparse representation, which effectively solves the fault diagnosis problem caused by the limited installation location and the limited performance of the vibration measurement at high-temperature was also designed. The MobieNet-V3- Small convolutional neural network (CNN) model is improved by reducing the basic blocks of the continuous homogeneous structure in the original model, the Squeeze and Excitation-SE structure is expanded to the global level to obtain a lightweight CNN fault recognition model. The effectiveness and efficiency of the proposed method are validated by various experiments.