皮带机故障预测与维护策略探究

Exploring Belt Conveyor Fault Prediction and Maintenance Strategies

为了提升皮带机的运行效率并减少由故障导致的停机时间,针对皮带机故障预测及维护策略的关键问题进行了深入的分析。通过综合考察传统方法、机器学习技术,以及基于深度学习的先进预测方法,比较了不同方法在故障检测精度和提前预警能力方面的性能。研究表明,传统方法在处理简单故障时有效,深度学习方法在处理复杂故障模式和提前预测方面展现出显著优势。进一步探讨了结合预测技术来优化预防性和检修性维护策略的可能性,以提高设备可靠性和运维效率。分析强调了采用数据驱动和智能化维护策略的重要性,指出未来的发展趋势将集中于利用人工智能技术优化维护体系,实现皮带机的高效且可靠运行。

In order to improve the operation efficiency of the belt conveyor and reduce the downtime caused by the fault,the key problems of the fault prediction and maintenance strategy of the belt conveyor are analyzed in depth.By comprehensively investigating traditional methods,machine learning techniques,and advanced prediction methods based on deep learning,the performance of different methods in fault detection accuracy and early warning ability is compared.The research shows that the traditional method is effective in dealing with simple faults,and the deep learning method shows significant advantages in dealing with complex fault modes and early prediction.The possibility of combining predictive technology to optimize preventive and repairable maintenance strategies is further explored to improve equipment reliability and operational efficiency.The importance of adopting data-driven and intelligent maintenance strategies were emphasized,and pointed out that the future development trend will focus on using artificial intelligence technology to optimize the maintenance system and realize the efficient and reliable operation of the belt conveyor.