基于机器学习的散料输送自动化优化研究

Research on Optimization of Bulk Material Transportation Automation Based on Machine Learning

散料输送系统在工业领域有着广泛应用,但由于某些领域的操作环境复杂多变,往往会对输送系统的工作效率和质量提出更高要求。随着机器学习算法的进步,机器学习可实现自动优化复杂的系统。基于此背景,研究采用机器学习的方法自动化优化散料输送系统。结果表明,基于机器学习的散料输送自动化优化研究具有重要的实际应用价值,能够自动预测和诊断设备故障,且诊断时间短,应用性能高,提升了散料输送设备的可靠性。

Bulk material conveying systems are widely used in many industrial fields,but due to the complex and variable operating environment in certain fields,higher requirements are often placed on the efficiency and quality of the conveying system.With the advancement of machine learning algorithms,they can automatically optimize such complex systems.Based on this background,this study adopts machine learning methods to optimize the automation of bulk material conveying systems.The results indicate that the research on machine learning based optimization of bulk material conveying automation has important practical application value.It can automatically predict and diagnose equipment faults,with short diagnosis time and high application performance,further improving the reliability of bulk material conveying equipment.