Wavelet Analysis Used In Micro-gear Fault Diagnosis And Research

Mechanical micro-gears are the most important machinery parts in the machinery equipments. If we can monitor and diagnose the running gear without delay, then the running gear of potential failures can be detected, the sudden shutdown caused by the accident loss can be reduced, the business benefits can be effectively improved. In this article it mainly aimed at the local gear fault features. Beginning from the gear fault vibration theory and wavelet analysis theory, we mainly research the micro-gear vibration signal in decomposing and fault diagnosis in mechanical micro-gears. These specific works are following:The first chapter described the purpose and practical significance of this topic, development status and trends of equipment fault diagnosis, mechanical gear research wavelet-based fault diagnosis and the main issues this article researched.The second chapter outlines the micro-vibration mechanism of gears and describes the micro-gear failures occurred in several common forms, as well as the modulation characteristics of gear vibration signals.The third chapter describes the basic theory of wavelet analysis, wavelet transform-efinition,characteristics.The wavelet analysis can effectively analyse the non-stationary signals.The fourth chapter in order to enable reduces the vibration signals interferenced by the noise, research the wavelet packet theory and make a corresponding improvement. Through a large number of simulation experiments, affection of signal noise after reduced will be compared with signal noise reduction method.The fifth chapter in fault diagnosis in order to find a better fault diagnosis results; this article proposed two kinds of fault diagnosis technology. They are improved wavelet packet transform with characteristic frequency of gear fault diagnosis and wavelet packet combining BP neural network fault diagnosis. In these two fault diagnosis, feature vector extraction is the key and in this respect, we have a certain study. Finally, simulation results show the two methods to arrive at appropriate conclusions. The result of this article is based on the actual collection of the mechanical characteristics of micro-gear fault signal. The actual simulation results show that the analysis method presented in this article applied to gear fault diagnosis, and have considerable value in engineering.