Linear Discriminant Analysis and Back Propagation Neural Network Cooperative Diagnosis Method for Multiple Faults of ComplexEquipment Bearings

doi:10.3969/j.issn.1000-1093.2017.08.024

Abstract

Abstract: The fault diagnosis accuracy of bearing for complex equipment is not high due to the structural complexity of complex equipment and the poor working environment. A method of multiple bearing fault diagnosis based on linear discriminant analysis (LDA) and BP neural network is presented. A linear discriminant analysis is utilized for the linear dimension reduction of the dimensionless bearing multiple fault index, which is taken as an indicator of fault data. Lagrange extremum method is used to obtain an optimal projection vector. The bearing multiple fault data is projected on a category most likely to distinguished direction. The projected samples are used as the input samples of BP neural network and the test network. The simulation experiment of a certain large rotating machinery units shows that the proposed method can effectively reduce the dimensional mapping of multi-fault, achieve better classification, and has good validity and practicability. Key

Key words: mechanics, bearingmultiplefaultdiagnosis, Lagrangianextremummethod, lineardiscriminantanalysis, BPneuralnetwork

CLC Number:

TH133.33⁺1

HUANG Da-rong, CHEN Chang-sha, SUN Guo-xi, ZHAO Ling，MI Bo. Linear Discriminant Analysis and Back Propagation Neural Network Cooperative Diagnosis Method for Multiple Faults of ComplexEquipment Bearings[J]. Acta Armamentarii, 2017, 38(8): 1649-1657.

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第38卷
第8期2017 年8月兵工学报ACTA
ARMAMENTARIIVol.38No.8Aug.2017

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