Early Failure Diagnosis of Scraper Conveyor Gearboxes Based on DS Evidence Theory and Multimodal Data Fusion

A scraper conveyor is one of the important equipment to ensure reliable, efficient and stable mining and transportation of coal. As the most important transmission system of the scraper conveyor, the gearbox takes the role of transmitting power and torque. Due to the influence of working conditions in underground coal mines, the gear transmission system is often subject to the impact of nonuniform large loads, which is very prone to failures, and affected by environmental interference, it is difficult to detect the early abnormal signals of the scraper conveyor gearbox in the conventional industrial scenarios of fault monitoring methods. To ensure the stability and reliability of its work, this paper carries out the research on the multi-parameter fusion of gearbox early fault diagnosis method under strong background noise interference. Aiming at the problem that the change of fluid physical and chemical characteristic parameters can reflect the early health condition of the gear transmission system and the single vibration signal is difficult to be extracted under the strong background noise, a model based on the fluid physical and chemical characteristic parameters and vibration signals is constructed by utilizing the RBF neural network and the Random Forest algorithm, and the body of evidence of the two models is fused at the decision-making level through the DS evidence theory, which forms the fluid-vibration multi-parameter fusion judgment of the early fault diagnosis method of scraper conveyor gearbox. Through comparison, it is found that compared with the fusion methods, such as high-dimensional variational self-encoder, and single diagnosis methods, such as the Random Forest Algorithm, the method researched in this paper is more suitable for the early fault warning of the scraper conveyor gearbox of the well coal mine, and the experimental validation finds that the average accuracy rate of the early fault recognition can be up to 96.6%.