基于GRU神经网络的机械设备故障预测

Fault Prediction of Mechanical Equipment Based on GRU Neural Network

随着当代工业的快速发展,对机械设备的稳定运行提出了更高的要求,如何使机械设备高效有序运行,是当前各企业面临的挑战。传统的机械故障预测技术在复杂的数据场景下适用性较低,论文提出一种基于GRU神经网络的机械设备故障预测方法,对设备运行过程中产生的时序数据进行收集分析。论文使用CRITIC权重法确定影响故障发生的关键参数,通过GRU神经网络模型对设备历史数据进行预测分析。论文应用该方法,对机械设备运行的时序数据进行预测,结果显示,该方法能够提高机械设备故障预测准确率,降低人工成本,实现有效的机械设备故障预测。

With the rapid development of contemporary industry,the stable operation of machinery and equipment has put forward higher requirements.How to make the efficient and orderly operation of mechanical equipment is the challenge facing enterprises at present.The traditional mechanical fault prediction technology has low applicability in complex data scenarios.In this paper,a mechanical equipment fault prediction method based on GRU neural network is proposed to collect and analyze the time series data generated during equipment operation.In this paper,critical parameters affecting fault occurrence are determined by CRITIC weight method,and historical data of equipment is predicted and analyzed by GRU neural network model.In this paper,the method is applied to predict the time series data of mechanical equipment.The results show that the method can improve the accuracy of mechanical equipment fault prediction,reduce labor cost,and achieve effective mechanical equipment fault prediction.