电子测量与仪器学报
電子測量與儀器學報
전자측량여의기학보
Journal of Electronic Measurement and Instrumentation
2015年
8期
1114-1120
,共7页
轴承振动%小波去噪%能量特征%故障诊断
軸承振動%小波去譟%能量特徵%故障診斷
축승진동%소파거조%능량특정%고장진단
bearing vibration%wavelet denoising%energy feature%fault diagnosis
转子系统和轴承是旋转机械中的关键零部件,其长期处于高速、满负荷运行极易出现故障。基于振动信号处理的诊断方法具有可在线、实时诊断的特点,针对频谱分析对非线性振动信号故障特征提取的不足,研究小波包对振动信号进行特征提取。由于传统软、硬阈值量化方法在阈值处分别存在恒定偏差和不连续的问题,设计了一种参数可调的改进连续函数对阈值进行量化。系统首先对振动信号进行小波包分解与去噪,然后采用小波包能量特征提取方法完成对旋转机械的转子不平衡故障、不对中故障、转子动静碰摩故障进行有效诊断。测试结果表明,轴承出现不同故障时,通过小波包分解后不同子带能量的不同,可用模式识别方法有效进行故障识别。
轉子繫統和軸承是鏇轉機械中的關鍵零部件,其長期處于高速、滿負荷運行極易齣現故障。基于振動信號處理的診斷方法具有可在線、實時診斷的特點,針對頻譜分析對非線性振動信號故障特徵提取的不足,研究小波包對振動信號進行特徵提取。由于傳統軟、硬閾值量化方法在閾值處分彆存在恆定偏差和不連續的問題,設計瞭一種參數可調的改進連續函數對閾值進行量化。繫統首先對振動信號進行小波包分解與去譟,然後採用小波包能量特徵提取方法完成對鏇轉機械的轉子不平衡故障、不對中故障、轉子動靜踫摩故障進行有效診斷。測試結果錶明,軸承齣現不同故障時,通過小波包分解後不同子帶能量的不同,可用模式識彆方法有效進行故障識彆。
전자계통화축승시선전궤계중적관건령부건,기장기처우고속、만부하운행겁역출현고장。기우진동신호처리적진단방법구유가재선、실시진단적특점,침대빈보분석대비선성진동신호고장특정제취적불족,연구소파포대진동신호진행특정제취。유우전통연、경역치양화방법재역치처분별존재항정편차화불련속적문제,설계료일충삼수가조적개진련속함수대역치진행양화。계통수선대진동신호진행소파포분해여거조,연후채용소파포능량특정제취방법완성대선전궤계적전자불평형고장、불대중고장、전자동정팽마고장진행유효진단。측시결과표명,축승출현불동고장시,통과소파포분해후불동자대능량적불동,가용모식식별방법유효진행고장식별。
Rotor and bearing are the key part of rotating machinery, its long-term operation in high-speed and full-load appears fault easily.The diagnostic method based on vibration signal processing has the characteristics of diag-nosing online and real-time.Aiming at the shortage of extracting fault feature of the nonlinear vibration signal, the feature extraction of vibration signal by using wavelet packet is researched.Considering that the traditional soft and hard threshold quantization methods would have constant deviation and be discontinuous in the threshold, an im-proved continuous function whose parameters were adjustable to quantify the threshold was designed.First, the sys-tem makes wavelet packet decomposition and denoising to vibration signal.Then, it diagnoses the faults of rotating machinery effectively, including rotor unbalance fault, misalignment fault and rotor movement rubbing fault, by the way of extracting feature of wavelet packet energy.The test results show that when the bearing shows different fail-ure, pattern recognition method could be used to identify the fault effectively based on the fact that energy vary from one sub-band to another after the decomposition of wavelet packet.