Standardized monitoring data.

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. Comparative experimental analysis indicates that the proposed method can effectively identify the health status of the scraper conveyor, achieving an accuracy rate of 98.9%. This method provides an effective means and technical support for the subsequent health management of scraper conveyors in coal mining fully mechanized workfaces.

针对当前刮板输送机健康评估受专家知识制约，且设备退化模型构建难度较高的难题，本研究提出了一种基于一维卷积神经网络（1DCNN，one-dimensional convolutional neural networks）的刮板输送机健康状态评估方法。该方法以表征刮板输送机不同健康状态的四路预处理监测信号作为输入源。通过构建的一维卷积神经网络模型对数据进行多次变换，从数据中提取有效特征，并建立输入数据与设备健康状态的映射关系，从而实现刮板输送机的健康状态识别。对比实验分析表明，所提方法可有效识别刮板输送机的健康状态，识别准确率达98.9%。该方法为煤矿综采工作面刮板输送机的后续健康管理提供了有效手段与技术支撑。