Research on state perception of scraper conveyor based on one-dimensional convolutional neural network.

ABSTRACT: Addressing the challenges of current scraper conveyor health assessments being influenced by expert knowledge and the relative difficulty in establishing degradation models for equipment, this study proposed a method for assessing the health status of scraper conveyors based on one-dimensional convolutional neural networks (1DCNN). The approach utilizes four preprocessed monitoring signals representing different health states of the scraper conveyor as input sources. Through multiple transformations of the data using a constructed one-dimensional convolutional neural network model, it extracts effective features from the data and establishes a mapping relationship between input data and equipment health status. This enables the recognition of the health status of the scraper conveyor. Comp

SUBMITTER: Lu J 

PROVIDER: S-EPMC11488722 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature