大数据在设备健康预测和备件补货中的应用

摘要

提出了一种设备健康预测和库存优化方法.使用自编码器提取监测信号特征,基于深度神经网络模型进行时序预测,构建设备健康度指标;采用统计分布判定和参数拟合的预测方法实现库存优化;最后,根据设备健康状态与备件数量实现生产主动预警.实例结果表明,该方法预测精度高于LSTM算法,可对设备故障进行精确预警,且备件库存优化模型的可靠性高达90.4％,可有效减少备件库存.%A equipment health prediction and inventory optimization method was proposed herein.Firstly, a self-encoder was used to extract the features of monitoring signals.Based on that, a deep neural network model was used to predict the time series outcomes, and a equipment health indicator was also constructed.Secondly, a statistical distribution and parameter fitting prediction methods was used to achieve inventory optimization.Finally, the system provided active warnings for productions based on the information about the device health status and the number of spare parts.Example results show that the prediction accuracy of this method is higher than that of LSTM algorithm, which may accurately predict equipment failure.Early warning, and the reliability of the spare parts inventory optimization model is of 90.4％, which may effectively reduce spare parts inventory.