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首页> 外文期刊>IEEE transactions on industrial informatics >Deep Hybrid State Network With Feature Reinforcement for Intelligent Fault Diagnosis of Delta 3-D Printers
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Deep Hybrid State Network With Feature Reinforcement for Intelligent Fault Diagnosis of Delta 3-D Printers

机译：深度混合状态网络，具有智能故障诊断的功能强化Δ3-D打印机

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摘要

An echo state network (ESN) is a type of recurrent neural network that is good at processing time-series data with dynamic behavior. However, the use of ESNs to enhance fault-classification accuracy continues to be challenging when the condition signals are collected by low-cost sensors. In this paper, a deep network algorithm, called a deep hybrid state network (DHSN), is proposed for fault diagnosis of three-dimensional printers using attitude data with low measurement precision. In the DHSN, the output data of a sparse auto-encoder are regarded as the abstract features of a double-structured ESN (DESN). The DESN is designed for feature reinforcement and fault recognition, wherein the first function reinforces the features and the second is used for fault classification. More specifically, feature reinforcement is developed to improve the clustering performance and replace the traditional overall feedback fine-tuning in deep models. This strategy improves learning efficiency and overcomes the vanishing-gradient problem for deep learning. The forecasting performance of the proposed approach is evaluated in experiments, and its superiority is demonstrated through comparison with other intelligent fault-diagnosis technologies.
机译：回声状态网络（ESN）是一种复发性神经网络，其擅长处理具有动态行为的时间序列数据。然而，当通过低成本传感器收集状态信号时，使用ESN以提高故障分类精度的持续挑战。本文提出了一种被称为深杂交状态网络（DHSN）的深网络算法，用于使用具有低测量精度的姿态数据的三维打印机的故障诊断。在DHSN中，稀疏自动编码器的输出数据被视为双结构ESN（DESN）的抽象特征。 DESN专为特征强钢和故障识别而设计，其中第一功能强化了特征，第二功能用于故障分类。更具体地，开发了特征强化以改善聚类性能，并更换深度模型中的传统整体反馈微调。该策略提高了学习效率，克服了深度学习的消失梯度问题。在实验中评估了所提出方法的预测性能，通过与其他智能故障诊断技术的比较来证明其优越性。

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