轴承
軸承
축승
Bearing
2015年
11期
46-49
,共4页
颉潭成%韩一村%徐彦伟%马君达
頡潭成%韓一村%徐彥偉%馬君達
힐담성%한일촌%서언위%마군체
滚动轴承%故障诊断%小波降噪%信息融合%声发射%神经网络
滾動軸承%故障診斷%小波降譟%信息融閤%聲髮射%神經網絡
곤동축승%고장진단%소파강조%신식융합%성발사%신경망락
rolling bearing%fault diagnosis%wavelet denoising%information fusion%acoustic emission%neural network
针对传统的单一传感器检测准确率不高，诊断系统不稳定等问题，将振动和声发射2种检测方法进行融合。首先对采集到的2种信号进行小波降噪及 Hilbert 解调，得到故障信号的频域包络谱，计算其频段能量值并组成特征向量；然后利用 BP 神经网络建立多传感器的信息融合系统，选取合适的样本输入网络进行训练，直至达到所要求的误差范围；最后实现对样本轴承的故障诊断，达到了相对较高的诊断正确率。
針對傳統的單一傳感器檢測準確率不高，診斷繫統不穩定等問題，將振動和聲髮射2種檢測方法進行融閤。首先對採集到的2種信號進行小波降譟及 Hilbert 解調，得到故障信號的頻域包絡譜，計算其頻段能量值併組成特徵嚮量；然後利用 BP 神經網絡建立多傳感器的信息融閤繫統，選取閤適的樣本輸入網絡進行訓練，直至達到所要求的誤差範圍；最後實現對樣本軸承的故障診斷，達到瞭相對較高的診斷正確率。
침대전통적단일전감기검측준학솔불고，진단계통불은정등문제，장진동화성발사2충검측방법진행융합。수선대채집도적2충신호진행소파강조급 Hilbert 해조，득도고장신호적빈역포락보，계산기빈단능량치병조성특정향량；연후이용 BP 신경망락건립다전감기적신식융합계통，선취합괄적양본수입망락진행훈련，직지체도소요구적오차범위；최후실현대양본축승적고장진단，체도료상대교고적진단정학솔。
Aiming at problems about low detection accuracy and unstable diagnosis system,the vibration and acoustic emission detecting methods are fused.Firstly,the wavelet denoising and Hilbert demodulation are carried out for two signals,the envelope spectrum in frequency domain of fault signals are obtained,and the energy value for frequency band is calculated to compose feature vector.Then the BP neural network is used to establish information fusion system of multi -sensor,the appropriate sample is selected to input network for training until required error range is reached. Finally,the fault diagnosis is realized for sample bearings,and a relatively high diagnostic accuracy is reached.
