微型机与应用
微型機與應用
미형궤여응용
MICROCOMPUTER & ITS APPLICATIONS
2014年
4期
77-80
,共4页
张永建%孔祥振%张永超%路艳春%邢龙超%张小刚
張永建%孔祥振%張永超%路豔春%邢龍超%張小剛
장영건%공상진%장영초%로염춘%형룡초%장소강
经验模态分解(EMD)%BP网络%IMF能量%故障诊断
經驗模態分解(EMD)%BP網絡%IMF能量%故障診斷
경험모태분해(EMD)%BP망락%IMF능량%고장진단
empirical mode decomposition ( EMD )%BP neutral network%IMF energy%fault diagnosis
故障轴承的振动信号是非平稳信号,传统的非平稳信号分析手段存在许多不足;BP 网络能够出色地解决传统识别模式难以解决的复杂问题。提出了经验模态分解( EMD )与 BP 神经网络相结合的滚动轴承故障诊断方法。采用 EMD 方法对振动信号进行分解,得到组成信号的多个内禀模态分量( IMF ),提取重要的 IMF 分量的能量作为信号的特征量;采用 BP 网络作为模式分类器,对轴承的故障类型进行分类。经试验数据分析证明,该方法能够准确地对轴承故障进行诊断。
故障軸承的振動信號是非平穩信號,傳統的非平穩信號分析手段存在許多不足;BP 網絡能夠齣色地解決傳統識彆模式難以解決的複雜問題。提齣瞭經驗模態分解( EMD )與 BP 神經網絡相結閤的滾動軸承故障診斷方法。採用 EMD 方法對振動信號進行分解,得到組成信號的多箇內稟模態分量( IMF ),提取重要的 IMF 分量的能量作為信號的特徵量;採用 BP 網絡作為模式分類器,對軸承的故障類型進行分類。經試驗數據分析證明,該方法能夠準確地對軸承故障進行診斷。
고장축승적진동신호시비평은신호,전통적비평은신호분석수단존재허다불족;BP 망락능구출색지해결전통식별모식난이해결적복잡문제。제출료경험모태분해( EMD )여 BP 신경망락상결합적곤동축승고장진단방법。채용 EMD 방법대진동신호진행분해,득도조성신호적다개내품모태분량( IMF ),제취중요적 IMF 분량적능량작위신호적특정량;채용 BP 망락작위모식분류기,대축승적고장류형진행분류。경시험수거분석증명,해방법능구준학지대축승고장진행진단。
The vibration signal of fault rolling bearing is nonstationary , traditional methods of analyzing the nonstationary signal have some deficiencies; BP neutral network can well solve complex problems that are difficult to be solved through traditional recognition mode . The method of rolling bearing fault diagnosis presented in this article combines with the empirical mode decompo-sition ( EMD ) and BP neural network . The EMD method is used to decompose the bearing vibration signal , multiple intrinsic mode function ( IMF ) components composed the signal are acquired , IMF energy is used to be the characteristic quantity of signal; BP net-work is adapted to be the fault mode classifier and classify the bearing fault type . The analysis of experiment data shows that the method can diagnose the bearing fault accurately .