Sound-Based Improved DenseNet Conveyor Belt Longitudinal Tear Detection

Abstract

At present, conveyor belt tearing is the most serious fault of belt conveyor, causing the greatest loss. This paper aims to overcome the issues of poor accuracy, low precision, and poor real-time performance in the longitudinal tear detection of the conveyor belt of the belt conveyor. Specifically, this paper presents a method of longitudinal tear detection of conveyor belt based on the sound signal. According to the recognition of the tearing sound signal, the longitudinal tearing of conveyor belt can be detected. A dynamic MFCC feature extraction method is proposed to extract the sound signal feature. An improved DenseNet neural network model is designed, which is used to classify the longitudinal sound of the belt conveyor to realize the longitudinal tearing detection of the conveyor belt.The experimental results demonstrate that the method in this paper achieves the sound detection of the longitudinal tear of the conveyor belt, and the average accuracy of the longitudinal tear detection of the conveyor belt of the belt conveyor reaches 95.42%, which satisfies the requirements of the longitudinal tear detection of the conveyor belt of the belt conveyor.Applying this method to the longitudinal tearing detection of conveyor belt can solve the shortcomings of existing methods and realize the detection of the longitudinal tearing fault of conveyor belt.

Keywords

• Belt conveyor
• longitudinal tear detection
• sound
• dynamic MFCC
• improved DenseNet