Condition monitoring for helicopter main gearbox based on wavelet packet transform and wavelet neural network

This paper describes a conditon monitoring systems for helicopter main gearbox using wavelet packet transform (WPT) and wavelet neural network (WNN). According to the fault characteristics of main gearbox, a fault diagnosis method that combining WPT and WNN with threshold is proposed. First the noise is removed from vibration signals, then the denoising signals are decomposed by WPT, extract standard deviation coefficients of each level as the input of WNN, the learning rates and momentum factors are used to adjust the network, the method of batch training is applied and it can diagnose fault quickly, which can monitor the condition of main gearbox. Theoretical and practical application shows that this method is effective and feasible, its diagnostic speed is rapid and result is accuracy, which provides a new technical reference for the development of helicopter fault diagnostic systems.

[1]  Jonathan A. Keller,et al.  Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis , 2006 .

[2]  Hong Pan,et al.  Efficient Object Recognition Using Boundary Representation and Wavelet Neural Network , 2008, IEEE Transactions on Neural Networks.

[3]  J.G. Zhang,et al.  Fault Bearing Identification Based on Wavelet Packet Transform Technique and Artificial Neural Network , 2010, 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization.

[4]  Hai-bin Yu,et al.  ModifiedMoretWavelet NeuralNetworks for Fault Detection , 2005 .

[5]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[6]  Darryll J. Pines,et al.  A review of vibration-based techniques for helicopter transmission diagnostics , 2005 .

[7]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[8]  Li Zhang,et al.  Fault Diagnosis of Aerospace Rolling Bearings Based on Improved Wavelet-Neural Network , 2006, 2007 Chinese Control Conference.

[9]  Jian-Da Wu,et al.  An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network , 2009, Expert Syst. Appl..

[10]  K. I. Ramachandran,et al.  A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box , 2008, Expert Syst. Appl..

[11]  Jian-Da Wu,et al.  An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network , 2009, Expert Syst. Appl..

[12]  Zhimin Du,et al.  Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network , 2009 .

[13]  Zwe-Lee Gaing,et al.  Wavelet-based neural network for power disturbance recognition and classification , 2004, IEEE Transactions on Power Delivery.

[14]  Bo Hu,et al.  Wavelet neural network based fault diagnosis of asynchronous motor , 2009, 2009 Chinese Control and Decision Conference.

[15]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[16]  Chen Guo,et al.  Ship roll stabilization using supervision control based on inverse model wavelet neural network , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[17]  J. Rafiee,et al.  INTELLIGENT CONDITION MONITORING OF A GEARBOX USING ARTIFICIAL NEURAL NETWORK , 2007 .

[18]  Jian-Da Wu,et al.  Investigation of engine fault diagnosis using discrete wavelet transform and neural network , 2008, Expert Syst. Appl..