Research On Embedded Mechanical Fault Diagnosis System Based On Dual Spectral Images

As the mechanical gear components of the most important gear,its working conditions related to the operation of the entire mechanical equipment.Equipment in the event of failure,will bring a very serious harm,resulting in huge economic losses.Therefore,people attach great importance to the diagnosis of mechanical equipment and research.In this paper,a gear fault diagnosis method based on image recognition is proposed,which is based on the advanced image recognition technology.In this paper,the image recognition technology is used to analyze the texture characteristics of the double-spectrum gray-scale image of the gear fault signal,and the embedded function is designed to meet the requirements of power consumption,function,volume,cost and reliability.Platform,and the preparation of Android applications on the platform through the use of BP neural network classification algorithm to identify the gear fault type.The main research contents are as follows:1.The signal filtering and noise reduction are studied,and the bispectrum gray scale of the gear vibration signal is obtained.2.The image feature extraction based on gray scale is studied as the input feature vector of classification.3.The BP neural network pattern recognition algorithm is used to classify the first kind of gray scale to identify the gear fault type.4.Understand the basic knowledge of embedded systems,master the embedded system environment to build and transplant.5.Design the module schematic,write the hardware driver and debug.6.Develop gearbox fault diagnosis system software based on Android.