A contactless measuring speed system of belt conveyor based on machine vision and machine learning

Abstract During the operation of the belt conveyor, measuring speed of the belt conveyor is vital to the safe and efficient operation. In the existing measuring speed system, the measurement instrument is required contacting with the surface of the belt. The contact measurement method cannot avoid the occurrence of measuring error caused by slipping on the contact surface and wear of the measurement instrument. In order to solve the problems mentioned above, a new contactless measuring speed system is proposed in this paper. The system uses the CCD camera to capture the side image of belt. The speed of belt conveyor can be obtained by measuring the regularity of image texture. The proposed measuring system can meet the requirement of measuring speed in long running process of belt conveyor. Experimental results show that the measuring accuracy indicators can reach RMSE of 0.018 m/s and MAE of 0.010 m/s.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Xiaoming Zhang,et al.  Bootstrap Sampling Based Data Cleaning and Maximum Entropy SVMs for Large Datasets , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[3]  Robert M. Hawlick Statistical and Structural Approaches to Texture , 1979 .

[4]  Daijie He,et al.  Speed control of belt conveyors during transient operation , 2016 .

[5]  B. Dam,et al.  An FPGA-based integrated signal conditioner for measurement of position, velocity and acceleration of a rotating shaft using an incremental encoder , 2016, 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI).

[6]  Daijie He,et al.  Green operations of belt conveyors by means of speed control , 2017 .

[7]  Ting-Hua Yi,et al.  Multi-point displacement monitoring of bridges using a vision-based approach , 2015 .

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[10]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[11]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[12]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  G. Lodewijks Dynamics of belt systems , 1996 .

[15]  Jun Liu,et al.  Velocity Measurement Based on Alternate M/T Method and Incremental Optical Encoder , 2011 .

[16]  Gabriel Lodewijks,et al.  Two Decades Dynamics of Belt Conveyor Systems , 2007 .

[17]  Changyun Miao,et al.  Integrative binocular vision detection method based on infrared and visible light fusion for conveyor belts longitudinal tear , 2017 .

[18]  Karl-Ragmar Riemschneider,et al.  Signal synthesis for magnetoresistive speed sensors based on field simulations combined with measured sensor characteristic diagrams , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[19]  Anthony J. Hayter,et al.  Statistical inferences for linear regression models when the covariates have functional relationships: polynomial regression , 2008 .

[20]  Dingena L. Schott,et al.  Fuzzy Controlled Energy Saving Solution for Large-Scale Belt Conveying Systems , 2012 .

[21]  Hideaki Nozato,et al.  Angular velocity calibration system with a self-calibratable rotary encoder , 2016 .

[22]  Masatoshi Ishikawa,et al.  Visual encoder: robust and precise measurement method of rotation angle via high-speed RGB vision. , 2016, Optics express.

[23]  Hans J. Grabe,et al.  Linear, nonlinear or categorical: how to treat complex associations in regression analyses? Polynomial transformations and fractional polynomials , 2013, International Journal of Public Health.

[24]  Ting-Hua Yi,et al.  Vision-based structural displacement measurement: System performance evaluation and influence factor analysis , 2016 .