基于深度学习的锻模智能诊断

Haw-Ching Yang, Chun-Hong Zheng, Yu-Zhong Chen, Chien-Ming Tseng, Y. Kao
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引用次数: 1

摘要

在长期加工过程中,当试图从载荷-行程信号中提取特征时,估计冷锻模的状态是具有挑战性的。为了解决这一问题,本文提出了一种基于自编码器和支持向量机的诊断学习模型,用于从负载-冲击信号中区分模具状态。利用自编码器提取编码特征作为支持向量机的输入,对模具状态进行聚类。通过学习模型对当前模具状态的及时指示,该系统有望实现减少模具维修时间的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Diagnosis of Forging Die based on Deep Learning
Estimating the states of a cold-forging die is challenging when trying to extract the features from load-stroke signals during the long-term process. To solve this problem, this paper proposes a diagnosis learning model with an autoencoder and a support vector machine to distinguish the die states from the load-stoke signals. The autoencoder is utilized to extract the encoded features that serve as the input of the support vector machine to cluster die states. With timely indication of the current die state estimated by the learning model, the proposed die diagnosis system is promising to achieve the goal of reducing time for die maintenance.
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