离心式压缩机叶片作为压缩机内最重要部件，长期承受周期性振动和流动诱使激励的作用。而叶片的故障将对压缩机的运行以及现场安全可靠性有严重的影响，因此如何有效地识别压缩机叶片裂纹早期故障显得尤为重要。由于叶片裂纹故障属于低频微弱故障，通常被调制到叶片通过频率处，但是故障频率难以识别，清晰度较低。首先在叶片通过频率处进行信号滤波，然后应用Woods-Saxon and Gaussian Potential随机共振模型对特征频率进行加强，从而得到叶片裂纹故障频率。通过在叶片裂纹附近安装压力脉动传感器，利用压力脉动信号对叶片裂纹信息进行监测。实现模拟叶片裂纹的信号测试，验证了WSG随机共振模型在叶片裂纹早期故障识别中的有效性以及可靠性。同时通过应变试验进行验证此方法的有效性。
李宏坤
,
张晓雯
,
贺长波
,
徐福健
,
张学峰
. 利用随机共振的叶片裂纹微弱信息增强方法[J]. 机械工程学报, 2016
, 52(1)
: 94
-101
.
DOI: 10.3901/JME.2016.01.094
For centrifugal compressor, blades as the most important part suffer periodic vibration and flow induced excitation mechanism. Blade failure has a serious impact on the operation of the compressor as the safety and reliability of the scene. Therefore, how to effective identify compressor blade crack fault is particularly important. As the signal of blade crack failure belongs to low frequency weak fault, the fault frequency is often modulated to the blade passing frequency. It is difficult to classify the characteristic frequency. In this research, filtering at the blade passing frequency is firstly used for preprocessing. Then, the method of Woods-Saxon and Gaussian Potential stochastic resonance is applied to enhance the fault frequency demonstration. It is helpful for blade crack fault characteristic frequency determination. In this research, pressure pulsation (PP) sensors arranged in close vicinity to crack area are used to monitor the blade crack information. It can be concluded that WSGSR model is an effective tool for blade crack early fault detection on centrifugal compressor. As well, the strain experiment is carried out to verify the blade crack fault frequency.
[1] EISINGER F L. Acoustic fatigue of impellers of rotating machinery [J]. Journal of Pressure Vessel Technology，2002， 124(2)：54-160.
[2] WITEK L. Experimental crack propagation and failure analysis of the first stage compressor blade subjected to vibration [J]. Engineering Failure Analysis，2009， 16(7)：2163-2170.
[3] ELBHBAH K，SINHA J K. Vibration-based condition monitoring of rotating machines using a machine composite spectrum [J]. Journal of Sound and Vibration，2013，332(11)：2831-2845.
[4] SARAVANAN K，SEKHAR A. Crack detection in a rotor by operational deflection shape and kurtosis using laser vibrometer measurements [J]. Journal of Vibration and Control，2012，0(0)：1-13.
[5] RAO A R，DUTTA B. Vibration analysis for detecting failure of compressor blade [J]. Engineering Failure Analysis，2012，25：211-218.
[6] EGUSQUIZA E，VALERO C，HUANG X，et al. Failure investigation of a large pump-turbine runner [J]. Engineering Failure Analysis，2012，23：27-34.
[7] JOOSSE P，BLANCH M，DUTTON A，et al. Acoustic emission monitoring of small wind turbine blades [J]. Journal of Solar Energy Engineering， 2002，124(4)：
446-454.
[8] PAWAR P M，JUNG S N. Support vector machine based online composite helicopter rotor blade damage detection system [J]. Journal of Intelligent Material Systems and Structures，2008，19(10)： 1217-1228.
[9] K?NIG S，PETRY N，WAGNER N G. Aeroacoustic phenomena in high-pressure centrifugal compressors—a possible root cause for impeller failures [C/CD]// Proceedings of the 38th Turbomachinery Symposium，2009.
[10] PETRY N，BENRA F K，K?NIG S. Experimental study of acoustic resonances in the side cavities of a high-pressure centrifugal compressor excited by rotor/stator interaction [C/CD]// Proceedings of the ASME Turbo Expo 2010：Power for LandSea，and Air，Glasgow：American Society of Mechanical Engineers，2010.
[11] LENG Y G，LENG Y S，WANG T Y，et al. Numerical analysis and engineering application of large parameter stochastic resonance [J]. Journal of Sound and Vibration，2006，292(3)：788-801.
[12] BENZI R，SUTERA A，VULPIANI A. The mechanism of stochastic resonance [J]. Journal of Physics A：mathematical and general，1981，14(11)： L453- L457.
[13] HE Q，WANG J，LIU Y，et al. Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines [J]. Mechanical Systems and Signal Processing，2012，28：443-457.
[14] 林敏，黄咏梅. 调制与解调用于随机共振的微弱周期信号检测[J]. 物理学报，2006，55(7)：3277-3282. LIN Min，HUANG Yongmei，Modulation and demodulation for detecting weak periodic signal of stochastic resonance [J]. Chinese Journal of Physics， 2006，55(7)：3277-3282.
[15] TAN J，CHEN X，WANG J，et al. Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis [J]. Mechanical Systems and Signal Processing，2009， 23(3)：811-822.
[16] ZHANG H，HE Q，LU S，et al. Stochastic resonance with a joint woods-saxon and gaussian potential for bearing fault diagnosis [J]. Mathematical Problems in Engineering，2014，14：1-17.
[17] ZHANG X，HU N，HU L，et al. Multi-scale bistable stochastic resonance array：A novel weak signal detection method and application in machine fault diagnosis [J]. Science China Technological Sciences，2013，56(9)：2115-2123.
