پديد آورنده :
فاتحي، محمدرضا
عنوان :
عيب يابي هوشمند گير بكس هليكوپتر مبتني بر آناليز ارتعاشات
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
طراحي كاربردي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده مكانيك
صفحه شمار :
يازده،143ص.: مصور،جدول،نمودار
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
سعيد بهبهاني
استاد مشاور :
سعيد ضيايي راد
توصيفگر ها :
عيب يابي ارتعاشي , تبديل بسته موجك , هوشمند سازي , ماشين بردار پشتيبان
تاريخ نمايه سازي :
4/8/89
استاد داور :
حميدرضا مير دامادي، محمد دانش
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
Intelligent Fault Diagnosis of Helicopter Gearbox Based on Vibration Analysis Mohammad Reza Fatehi mr fatehi@me iut ac ir Date of Submission 2010 05 19 Department of Mechanical Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisor Saeed Behbahani behbahani@cc iut ac ir Abstract In this thesis after introducing the fault diagnosis methods based on the vibration analysis in three domain time frequency time frequency we use these techniques on the helicopter intermediate gearbox In the time domain Eigenvalues of the covariance matrix from accelerometer in three axis using Principle Component Analysis PCA Histograms of autocorrelation functions and high order statistical moments extracted as the meaningful features and these features used in the feature vector to classify the spiral bevel gear faults in the gearbox healthy worn and breakage gear In the frequency domain the symptom parameters based on the fast Fourier transform FFT extracted from the vibration signals Many kinds of symptom parameters SP or feature parameters have been defined in the pattern recognition field Here seven of them which are usually used for fault diagnosis of machinery in the frequency domain are used as meaningful features In the time frequency domain the kurtosis of subbands synthesized using wavelet packet transform used as the best features A method of detecting transients in mechanical systems by matching wavelets with associated signal is proposed leading to a development of joint time frequency scale distribution The three variables the time frequency and scale have maximized the chance for finding similar signal segments from a system under inspection The Fourier transform FT represents a signal by a family of complex exponents with infinite time duration Therefore FT is useful in identifying harmonic signals However due to its constant time and frequency resolutions it is weak in analyzing transitory signals So the time frequency analysis is more sensitive and more exact than time domain and frequency domain analysis to detect transient signals due to breakage shock pulses Also we analyze the vibration signals based on the Short Time Fourier Transform STFT and Wigner Ville Distribution WVD The results show that Daubuchi bases in the wavelet packet transform is better than morlet bases because of the orthogonality advantages of the Daubuchi bases rather than morlet bases Also the results show that Daubuchi 33 and Daubuchi 44 wavelets are the best mother wavelets to analyze the gearbox vibration signals After feature extraction we use Principle Component Analysis to decrease the dimensionality of the feature space Finally the feature vectors classify using the FeedForward Neural Network FFNN Support Vector Machine SVM K Nearest Neighbors KNN and Probability Neural Network PNN The SVM tries to orient the boundary such that the distance between the boundary and the nearest data point in each class is maximal The boundary is then placed in the middle of this margin between the two points and a hyper plane is created that separate two classes So in this
استاد راهنما :
سعيد بهبهاني
استاد مشاور :
سعيد ضيايي راد
استاد داور :
حميدرضا مير دامادي، محمد دانش
