多维非线性特征重构与融合的复杂产品工期预测方法

Novel Product Duration Prediction Method of Complicated Product Based on Multi-Dimensional Nonlinear Feature Reconstruction and Fusion

制造企业复杂产品零部件种类众多、加工和装配工序复杂,质量、工艺、设备等各类数据呈现多维度、多尺度、多噪声等特点,工期的关键影响特征提取难度大,预测精度难以保证。针对上述问题,提出一种多维非线性特征重构与融合的复杂产品工期预测方法,首先提出基于集成堆栈式自编码器的多维非线性特征重构与融合方法并构建相应模型,建立特征间的复杂关联耦合关系,形成工期关键因素特征池;基于深度学习算法构建多维非线性重构与融合的复杂产品工期预测模型,实现复杂产品工期的准确预测。选取某企业断路器和3D打印产品为对象进行工期预测的应用验证和对比分析,本方法的均方根误差平均值为1.28,平均绝对百分比误差平均值为3.01%,与未进行特征重构融合的方法,以及支持向量机、神经网络等方法相比,在精度方面均有提升,方均根误差至少降低了约10.87%,平均绝对百分比误差至少降低了约7.74%,证明所提方法的有效性和实用性。

There are so many species of components for complicated products in manufacturing enterprise,which has complicated machining and assembly processes.And the quality,process,equipment and other types of data has multi-dimensional,multi-scale,multi-noise characteristics,which makes it difficult to accurately predict the duration.To address the issues mentioned above,one kind of complicated product duration prediction method based on multi-dimensional nonlinear feature reconstruction and fusion is proposed.First,multi-dimensional nonlinear feature reconstruction and fusion using integrated Stacked Auto Encoder is performed and the corresponding model is constructed,and a complicated correlation coupling connection between features is built to provide a feature pool comprising critical duration factors.Second,complicated product duration prediction model based on multi-dimensional nonlinear feature reconstruction is built to accomplish the complicated product duration prediction.Finally,the proposed model is applied in enterprises to predict the circuit breakers and 3D printed products’duration.The mean root mean square error of this method reached 1.28,and the mean absolute percentage error reached 3.01%,which is higher than the accuracy of the method without feature reconstruction and fusion,support vector machine,neural network,and other methods.The root mean square error decreased by at least 10.87%,the mean absolute percentage error decreased by at least 7.74%,demonstrating the usefulness and applicability of this method.