ARIMA模型刀具寿命预测过程数据

依据课题模型方法建立ARIMA时间序列预测模型，对卷积神经网络预测得到的刀具磨损值序列进行分析和未来时刻的预测，对模型预测过程中的过程变量进行采集，其中包括每一轮预测中的实际寿命、预测寿命、误差上下限、以及未来若干时刻的剩余寿命预测值，在数据采集后对预测过程进行分析，用于验证刀具寿命预测准确度。Based on the modeling approach of this research, an ARIMA time series prediction model is constructed. This model is employed to analyze the tool wear value sequence predicted by the Convolutional Neural Network (CNN) and generate predictions for future time steps. Additionally, process variables generated during the model prediction procedure are collected, covering the actual remaining tool life, predicted remaining tool life, error bounds, and remaining life prediction values for several future time steps in each prediction iteration. After data collection, the prediction process is analyzed to verify the accuracy of tool remaining life prediction.

搜集汇总

背景与挑战

背景概述

该数据集包含ARIMA时间序列模型在刀具寿命预测过程中采集的过程变量，如实际寿命、预测寿命、误差上下限和剩余寿命预测值。这些数据用于分析和验证刀具寿命预测的准确度。