Research On Ferrographic Image Intelligent Classification And Abnormal Detection Of Gearbox Wear Debris

In the process of operation,mechanical equipment will often be shut down due to overload operation or long-term lack of effective maintenance,which will bring huge economic losses and security risks to enterprises.As the core component of mechanical transmission,gearbox is in urgent need of monitoring and fault diagnosis in its operation process.Most of the condition monitoring and fault diagnosis technologies are based on the vibration signals in the process of equipment operation,and determine the equipment status and fault types by analyzing the signal frequency characteristics under different states.However,for the equipment wear fault,it is more intuitive and accurate to study the wear product abrasive directly than the vibration signal.Wear debris analysis is generally divided into wear debris recognition based on typical ferrographic image features and residual life prediction based on debris concentration.In this paper,the classification and anomaly detection of wear debris ferrographic image are deeply studied.Convolution neural network is used to build the classification model of ferrographic image based on single wear debris and the target detection model of ferrographic image based on multi wear debris,so as to realize the intelligent classification of wear debris ferrographic image and the intelligent detection of abnormal wear debris.Firstly,the ferrographic images of the wear debris are obtained from the oil to be measured by online and offline methods.According to the classification of wear debris and the detection of abnormal wear debris,the appropriate image data set of ferrography is selected and the image is labeled.The wear debris obtained in this paper are divided into chain wear debris,cutting abrasive debris,fatigue wear debris,spherical wear debris and serious sliding wear debris according to the morphology characteristics.The data set used to classify the problem contains four types of wear debris except spherical ones,and the debris used to detect abnormal problems do not contain chain abrasive particles produced by normal wear.In addition,the detection of mud in lubricating oil is considered in the detection problem.In order to solve the problem of insufficient image sample,a virtual abrasive iron spectrum image based on similarity was designed,and corresponding data sets were constructed for different problems,which laid the foundation for deep learning.Secondly,the optimal classification model of wear debris ferrographic image based on convolution neural network is studied.In order to solve the problem of small number of wear debris image samples,a transfer learning method based on virtual image as source data is proposed.With AI Studio as the development platform,using PaddlePaddle deep learning framework,the basic model of convolution neural network based on AlexNet is constructed.The influence of different parameters on the classification accuracy of the model is studied by using parameter migration method.The optimal parameter combination to achieve the best classification effect is sought within the reasonable control range.The accuracy rate of classification was 93.8%.At the same time,the output feature map of each convolution layer is visualized to intuitively analyze the feature extraction process of convolution neural network in the process of model training,and the results of wear debris classification are characterized by clustering algorithm.Finally,aiming at the ferrographic image of wear debris in the mine gearbox under real working conditions,an intelligent detection model of abnormal wear debris based on two-level transfer learning is proposed.In the process of detection,oil sludge is considered as the interference source.Based on the single-stage object detection algorithm of YOLOv3,the basic model is constructed by using the self-designed ferrographic image data set of mixed wear debris.Then,starting from the transfer learning source data,different virtual abnormal wear debris data sets are used for comparative study,and more appropriate data is selected for migration learning.Finally,the practicability of the above research is verified by using the ferrographic image of the wear debris in the mine gearbox.Two level transfer learning is used to optimize the model,and the error sources before and after optimization are analyzed,which proves the effectiveness of the results.After two-level transfer learning,the average detection accuracy of the model in the verification set is 86.1%,and the average recall rate is 95.8%.In general,in this paper,the ferrographic image of wear debris is taken as the research object,and the depth transfer learning is taken as the research method.Aiming at the problem of classification and detection of wear debris in mine gearbox,the virtual image data is used as the source data.The intelligent classification model of wear debris ferrographic image based on virtual image and deep transfer learning and the abnormal wear debris detection model based on two-level transfer learning are constructed.According to the two-level transfer learning mode of "virtual data?public data?measured data",the intelligent detection of abnormal wear debris in mine gearbox is realized,and the detection accuracy is improved by 44.5%compared with the basic model.