摘要
矿用传感器是煤矿安全生产过程中最重要的设备之一,其智能化应用已经成为了煤矿行业传感类设备的发展趋势。由于各类矿用传感器的工作环境恶劣和长期运行,关键部件不可避免产生疲劳损坏,导致传感器的故障率相对较高;针对性介绍了故障自诊断技术在煤矿用传感层设备智能化应用中的研究现状和发展趋势,提出一种具有故障自诊断功能的矿用传感器的设计方法。该方法采用实时采集供电电源、模拟信号、数字信号等特征数据,构建了故障诊断的优先级识别模型,设计了故障自诊断技术的矿用传感器,增强了传感器的工作的可靠性,提升了传感器的智能化应用水平。传感器具备智能自诊断功能,对于保障煤矿生产安全和提高生产效率具有重要意义。
Mining sensors are one of the most important equipment in the safety production process of coal mines,and their intelligent application has become the development trend of sensing equipment in the coal mining industry.Due to the harsh working environment and long-term operation of various mining sensors,fatigue damage to key components is inevitable,resulting in a relatively high failure rate of sensors.This article introduces the research status and development trend of fault self-diagnosis technology in the intelligent application of sensor layer equipment in coal mines,and proposes a design method for mining sensors with fault self-diagnosis function.This method uses real-time collection of characteristic data such as power supply,analog signals,and digital signals to construct a priority recognition model for fault diagnosis.A mining sensor with fault self-diagnosis technology is designed,which enhances the reliability of the sensor’s operation and improves the intelligent application level of the sensor.Sensors have intelligent self-diagnosis function,which is of great significance for ensuring coal mine production safety and improving production efficiency.
作者
梁光清
LIANG Guangqing(China Coal Technology Engineering Group Chongqing Research Institute,Chongqing 400039,China;State Key Laboratory of the Gas Disaster Detecting,Preventing and Emergency Controlling,Chongqing 400037,China)
出处
《自动化与仪表》
2024年第2期123-125,143,共4页
Automation & Instrumentation
基金
中国煤炭科工集团科技创新重点项目(2022-TD-ZD001)
中煤科工集团重庆研究院有限公司创新引导项目(2022YBXM06)
中煤科工集团重庆研究院有限公司创新引导项目(2023YBXM01)。
关键词
矿用传感器
故障自诊断
智能化
信号特征提取
故障识别
mining sensors
fault self-diagnosis
intelligent
signal feature extraction
fault identification
