Research On Abnormal Detection Of Roller Based On Video Data

In mine transportation,belt conveyors are widely used transportation equipment in the coal industry.Various abnormalities may occur during operation of belt conveyors,which brings hidden dangers to coal mine production and worker safety.As the belt conveyor with the largest number of components,the idler rollers account for about 1/3 of the total abnormalities caused by the abnormality of the idler rollers.In this paper,through in-depth analysis of the types and causes of the abnormality of the roller,in view of the existing problems in the abnormal detection scheme,an automatic detection method for the abnormality of the roller is designed.The main work is as follows.Aiming at the problems existing in the existing idler detection methods,an abnormal detection method for idler rollers based on video data is proposed.The sound-based roller abnormal detection method,the stuck roller cannot effectively detect the fault signal,and the missed detection is serious;based on the pressure,temperature,and voltage detection methods that require a large number of sensors for data collection,the installation and maintenance costs of the sensors are too high.The detection method based on video data mainly reflects the movement state of the idler according to the speed of the idler.The inspection robot is used to shoot the idler video,and the linear speed of the idler is estimated from the video,and the estimated idler speed is compared with the belt speed to realize the support Roller abnormality detection,the core of this algorithm is to estimate the linear speed of roller rotation from the collected video.Aiming at the problem that it is not easy to directly measure the rotation frequency of the idler when using the linear velocity formula to calculate the idler speed,a Fast Fourier Transform(Fast Fourier Transform)time-frequency analysis method is proposed to calculate the rotation frequency of the idler.First,by extracting the gray texture features of the roller in the continuous video frames,the roller's periodic rotation change process is transformed into a series of periodic digital sequences that change with time.Then,use the fast Fourier transform to transform the digital sequence from the time domain to the frequency domain to obtain the corresponding spectrogram,and determine the rotation frequency of the roller according to the spectrogram.Aiming at the problem of inaccurate calculation of the final idler speed caused by the accumulation of errors when calculating the idler speed step by step,it is proposed to use a 3D convolution network to detect the idler speed end-to-end,and then judge according to the difference between the idler speed and the belt speed The status of the idler can realize the abnormal detection of the idler.In the experiment,a 3D convolutional network was used to extract the spatio-temporal features of the rollers in the video,and the speed of the rollers to be measured was classified according to the extracted feature results.Create a roller image data set for deep learning,and use a variety of data set expansion methods to expand the size of the original data set to 10 times the original size,which improves the detection accuracy of the final model.Finally,five evaluation indicators were used to evaluate the 3D idler speed detection model,and the model evaluation results met the experimental expectations.Video-based idler abnormality detection realizes automatic detection of idler abnormalities,reduces the workload of inspection workers,and finds abnormal idlers in time.Compared with the detection method that requires a large number of sensors and the abnormal detection method of rollers based on sound signal characteristics,the video-based detection method reduces the difficulty of sensor installation and maintenance,improves the detection accuracy of jammed rollers,and determines the location of abnormal rollers.Reduce the difficulty and workload of secondary positioning of abnormal rollers during maintenance.