A method for intelligent fault diagnosis of coal mine safety monitoring system equipment

Abstract

In view of the diversity of equipment failure causes in the field application of coal mine safety monitoring system at the present stage, for some deep-level equipment failure causes, field technicians do not have the ability to conduct in-depth analysis, an intelligent diagnosis method of equipment failure based on intelligent diagnosis model base is proposed. This method first implements an intelligent diagnostic model library, which models various analysis methods collected by collecting indicator data and analysis methods required by technical experts for daily analysis and problem-solving. Then, various models are abstracted into machine programming languages for implementation, forming an intelligent diagnostic model library for equipment; secondly, based on the pre-installed software and hardware probes of the equipment, combined with an intelligent diagnostic model library, a comprehensive analysis of the causes of equipment faults is modeled, and the self-analysis, self-diagnosis, and self-repair functions of sub stations, sensors, power outage controllers, and other equipment are completed; finally, through a series of intelligent diagnostic front-end UI interaction methods such as device automatic topology diagrams, users are provided with convenient diagnostic operations and graphical and graphical data analysis and display of relevant information. While improving the stability and reliability of the coal mine safety monitoring system, it is possible to empower users and assist in the intelligent development of reducing personnel and increasing efficiency.

Keywords

• safety monitoring system
• diagnostic probe
• intelligent diagnosis model
• equipment fault
• data aware