基于一种改进的一维卷积神经网络电机故障诊断方法

Translated title of the contribution: Motor Fault Diagnosis Method Based on an Improved One-Dimensional Convolutional Neural Network

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Fault diagnosis is essential to ensure proper operation of the motor. Convolutional neural network (CNN) has showed a better performance on diagnosing single motor faults. However, traditional CNN has limitations in dealing with different sizes of data. To solve this problem, a fault diagnosis method was proposed based on spatial pyramid pooling (SPP), one-dimensional convolutional neural network and a parameter optimization strategy. The method was arranged to make not only the network be possible to process different sizes of data, but also reduce the complexity of the network structure and the amount of computation required. The parameter optimization strategy was designed to solve the scale mismatch problem in pyramid pooling during the diagnosis process. The simulation results show that, compared with the traditional network, the proposed method can improve the robustness and generalization ability of the network structure, making the fault diagnosis more quickly and accurately for motor.

Translated title of the contributionMotor Fault Diagnosis Method Based on an Improved One-Dimensional Convolutional Neural Network
Original languageChinese (Traditional)
Pages (from-to)1088-1093
Number of pages6
JournalBeijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume40
Issue number10
DOIs
Publication statusPublished - 1 Oct 2020

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