基于声信号的风电机齿轮箱故障诊断研究

摘要

随着风电机组装机容量的不断扩大，风电机组的故障诊断愈加重要。齿轮箱是风电机组的重要部件，恶劣的工作条件导致齿轮箱故障发生概率相对较高。近年来，声学技术已成为一种很有前途的诊断方法。由于声信号具有非接触测量和早期故障敏感的特点，在机械故障诊断领域受到广泛的关注。
　　本文在分析风电机组齿轮箱主要故障类型及其故障机理的基础上，提出相应的诊断方法。首先，对小波包变换和希尔伯特变换的原理进行了分析，并利用LabVIEW和MATLAB软件编制了相应的计算程序。然后，搭建了风力发电机组声信号的传输采集系统以及齿轮故障模拟的实验装置，分别实现了断齿20％，断齿40％，齿面点蚀等三种故障模式的故障模拟，获得了各种故障条件下的声信号。基于该声信号，利用小波包变换和希尔伯特变换结合的方法提取了声信号故障特征。
　　通过比较正常齿轮箱和故障齿轮箱的声音信号的处理结果，可以确切地识别出三种故障的故障特征。分析结果表明，利用小波包变换和希尔伯特变换方法结合的方法处理声信号可以用于齿轮故障早期检测。

目录

声明

摘要

ABSTRACT

TABLE OF CONTENTS

Chapter 1 Introduction

1．1 Research Background and Significance

1．2 Literature review

1．2．1 Fault diagnosis of Wind Turbine Gearboxes

1．2．2 Acoustic signal analysis of the fault diagnosis

1．3 Thesis organization

Chapter 2 Failure modes of Gearbox and Signal feature

Introduetion

2．1 Main failure of Gearboxes

2．1．1 The Gearboxas the fault origin

2．1．2 Main fault in Gearboxes

2．2 The Mechanism of gear noise

2．2．1 Gear mechanics

2．3 Summary

Chapter 3 The combination of Hilbert and Wavelet packet transforms method

Introduction

3．1 Analysis of gear vibration signals

3．2 The combined Hilbert and Wavelet packet transforms method

3．2．2 Wavelet packet transform

3．2．3 Spectrum Estimation through Wavelet packet tree construction

3．2．4 The combined method

3．2．5 Evaluation of the combined method

3．3 Analysis of gear acoustic signals

3．4 Summary

Chapter 4 Application of the combination method to fault diagnosis of gearbox

IntroductiOn

4．1 Data Acquisition

4．1．1 The Establishment of the Hardware Part

4．1．2 The Esmblishment of the Software Part

4．2 Experimental study

4．2．1 Description of experimental system

4．2．2 Collect acoustic signals using the actual model

4．2．3 Simulation of Gear faults

4．2．4 Results and Discussion

4．3 Summary

Conclusions and Future Research

References

Acknowledgement

Curriculum Vitae