13 citations · 15 across the 5 of their papers we have counts for
5 papers
TDANet: A Novel Temporal Denoise Convolutional Neural Network With Attention for Fault Diagnosis
Zhongzhi Li, Rong Fan, Jingqi Tu +3
Fault diagnosis plays a crucial role in maintaining the operational integrity of mechanical systems, preventing significant losses due to unexpected failures. As intelligent manufa…
Scalable and reliable deep transfer learning for intelligent fault detection via multi-scale neural processes embedded with knowledge
Zhongzhi Li, Jingqi Tu, Jiacheng Zhu +2
Deep transfer learning (DTL) is a fundamental method in the field of Intelligent Fault Detection (IFD). It aims to mitigate the degradation of method performance that arises from t…
Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network
Zhongzhi Li, Yunmei Zhao, Jinyi Ma +2
Compared with traditional model-based fault detection and classification (FDC) methods, deep neural networks (DNN) prove to be effective for the aerospace sensors FDC problems. How…
Augmented Imagefication: A Data-driven Fault Detection Method for Aircraft Air Data Sensors
Hang Zhao, Jinyi Ma, Zhongzhi Li +2
In this paper, a novel data-driven approach named Augmented Imagefication for Fault detection (FD) of aircraft air data sensors (ADS) is proposed. Exemplifying the FD problem of ai…
Mcity Data Collection for Automated Vehicles Study
Yiqun Dong, Yuanxin Zhong, Wenbo Yu +5
The main goal of this paper is to introduce the data collection effort at Mcity targeting automated vehicle development. We captured a comprehensive set of data from a set of perce…