A mathematical casing cutting model and operation parameters optimization of a large-diameter deepwater hydraulic cutter

Xiao, Shihong; Zhang, Fujuan; Zheng, Liancun

doi:10.1016/j.petrol.2017.11.054

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Recently, deep learning technology has been widely used in fault identification based on vibration measurement, for example: (a) application of the spectral kurtosis technique to select the frequency bandwidth, which contains the fault characteristics, for fault detection of rolling bearings [8]; (b) a fault diagnosis method based on variational pattern decomposition and an improved convolutional neural network (CNN) [9]; (c) a fault diagnosis method based on multivariate singular spectral decomposition and improved Kolmogorov complexity [10], which has shown good performance in fault diagnosis [11,12]. However, most studies have focused on how to optimize the model [10][11][12] and little attention has been paid to two problems caused by the limited mounting position of acceleration sensors in engineering applications, namely: (a) incomplete observation of the phenomenon [13] and (b) the low signal to noise ratio (SNR) of the acquired acceleration signals [14]. The impact of these problems on machine learning-based fault diagnosis methods has not yet seen much research.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Liu

Tao³

et al. 2022

Meas. Sci. Technol.

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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.

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