Fault diagnosis and self-healing for smart manufacturing: a review
Manufacturing systems are becoming more sophisticated and expensive, particularly with
the development of the intelligent industry. The complexity of the architecture and concept of …
the development of the intelligent industry. The complexity of the architecture and concept of …
Fault diagnosis for small samples based on attention mechanism
Aiming at the application of deep learning in fault diagnosis, mechanical rotating equipment
components are prone to failure under complex working environment, and the industrial big …
components are prone to failure under complex working environment, and the industrial big …
Intelligent condition monitoring of industrial plants: An overview of methodologies and uncertainty management strategies
Condition monitoring plays a significant role in the safety and reliability of modern industrial
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
[HTML][HTML] Exploring global attention mechanism on fault detection and diagnosis for complex engineering processes
Considering about slow drift and complicated relationships among process variables
caused by corrosion, fatigue, and so on in complex chemical engineering processes, an …
caused by corrosion, fatigue, and so on in complex chemical engineering processes, an …
Bayesian optimization and channel-fusion-based convolutional autoencoder network for fault diagnosis of rotating machinery
L Zou, KJ Zhuang, A Zhou, J Hu - Engineering Structures, 2023 - Elsevier
Deep learning methods are essential for the application of data driven technologies on fault
diagnosis of rotating machinery. However, the generalization and performance of deep …
diagnosis of rotating machinery. However, the generalization and performance of deep …
An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples
Aiming at limitations in fully exploiting the temporal correlation features of the original
signals, expensive cost in parameter tuning, and difficulties in obtaining sufficient training …
signals, expensive cost in parameter tuning, and difficulties in obtaining sufficient training …
Uncertainty utilization in fault detection using Bayesian deep learning
Up to now, extensive literature on the usage of deep learning in manufacturing can be
found. Though, actual usage of deep learning in manufacturing sites is somehow restrained …
found. Though, actual usage of deep learning in manufacturing sites is somehow restrained …
Intelligent fault diagnosis of rolling bearings using efficient and lightweight ResNet networks based on an attention mechanism (September 2022)
M Chang, D Yao, J Yang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Focusing on the problems of complex structure and low feature extraction efficiency that
exist in some traditional neural network algorithms, an improved convolutional neural …
exist in some traditional neural network algorithms, an improved convolutional neural …
[PDF][PDF] 基于改进卷积双向门控循环网络的轴承故障诊断
张昌凡， 刘佳峰， 何静， 刘建华 - 电子测量与仪器学报, 2021 - jemi.cnjournals.com
针对传统深度学习方法没有充分利用轴承信号的时序特点, 以及难以处理动态数据的问题,
提出一种基于改进卷积双向门控循环神经网络的轴承故障智能诊断方法. 采用卷积神经网络从 …
提出一种基于改进卷积双向门控循环神经网络的轴承故障智能诊断方法. 采用卷积神经网络从 …
A lightweight and explainable data-driven scheme for fault detection of aerospace sensors
Compared with traditional model-based fault detection and classification (FDC) methods,
deep neural networks (DNNs) prove to be more accurate for aerospace sensors. An …
deep neural networks (DNNs) prove to be more accurate for aerospace sensors. An …