煤炭机电设备故障诊断与预测性维护研究与实践

Research and practice of fault diagnosis and predictive maintenance of coal mechanical and electrical equipment

煤炭机电设备的故障诊断与预测性维护是为提高设备运行可靠性、降低维护成本而进行的研究与实践。文章聚焦于利用先进的监测技术和智能算法,通过对设备运行状态、振动、温度等多维度数据的实时采集与分析,实现对设备健康状况的实时监测和故障诊断。通过维护需求评估与设备分级,建立设备性能模型,以实现对设备性能的长期预测。在系统设计上,采用模块化架构,包括数据采集、数据处理、智能故障诊断和预测性维护等功能模块,以保证系统的灵活性和可扩展性。通过智能算法集成,文章实现了对不同设备类型的多算法融合,提高了对设备多样性和复杂性的适应能力。实地实践中,通过预测性维护模块的开发与调优,成功实现了对设备性能的准确预测和维护计划的有效制定。

The fault diagnosis and predictive maintenance of coal mechanical and electrical equipment is a research and practice to improve the reliability of equipment operation and reduce the maintenance cost.This paper focuses on the use of advanced monitoring technology and intelligent algorithm to realize the real-time monitoring and fault diagnosis of the health status through the collection and analysis of the operation state,vibration and temperature.Equipment performance model is established through maintenance requirements assessment and equipment classification to achieve long-term prediction of equipment performance.In the system design,a modular architecture,including data acquisition,data processing,intelligent fault diagnosis and predictive maintenance function modules,is adopted to ensure the flexibility and scalability of the system.Through intelligent algorithm integration,this paper realizes multiple algorithm fusion for different device types,improving the ability to adapt to device diversity and complexity.In the field practice,through the development and tuning of the predictive maintenance module,the accurate prediction of the equipment performance and the effective formulation of the maintenance plan were successfully realized.