Deep Convolutional Neural Network with Deconvolution and a Deep Autoencoder for Fault Detection and Diagnosis

ACS Omega ◽

10.1021/acsomega.1c06607 ◽

2022 ◽

Author(s):

Yasuhiro Kanno ◽

Hiromasa Kaneko

Keyword(s):

Neural Network ◽

Fault Detection ◽

Convolutional Neural Network ◽

Fault Detection And Diagnosis ◽

Deep Convolutional Neural Network ◽

Detection And Diagnosis

Download Full-text

Fault detection and diagnosis for heat source system using convolutional neural network with imaged faulty behavior data

Science and Technology for the Built Environment ◽

10.1080/23744731.2019.1651619 ◽

2019 ◽

Vol 26 (1) ◽

pp. 52-60 ◽

Author(s):

Shohei Miyata ◽

Jongyeon Lim ◽

Yasunori Akashi ◽

Yasuhiro Kuwahara ◽

Katsuhiko Tanaka

Keyword(s):

Neural Network ◽

Fault Detection ◽

Heat Source ◽

Convolutional Neural Network ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

Download Full-text

Fault detection and diagnosis for reactive distillation based on convolutional neural network

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2020.107172 ◽

2021 ◽

Vol 145 ◽

pp. 107172

Author(s):

Xiaolong Ge ◽

Beibei Wang ◽

Xinchuang Yang ◽

Yu Pan ◽

Botan Liu ◽

...

Keyword(s):

Neural Network ◽

Fault Detection ◽

Convolutional Neural Network ◽

Reactive Distillation ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

Download Full-text

Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing

Journal of Intelligent Manufacturing ◽

10.1007/s10845-020-01591-0 ◽

2020 ◽

Author(s):

Chia-Yu Hsu ◽

Wei-Chen Liu

Keyword(s):

Neural Network ◽

Time Series ◽

Fault Detection ◽

Empirical Study ◽

Convolutional Neural Network ◽

Semiconductor Manufacturing ◽

Fault Detection And Diagnosis ◽

Multiple Time ◽

Multiple Time Series ◽

Detection And Diagnosis

Download Full-text

Fault Detection and Diagnosis of Chillers with S&D Convolutional Neural Network

2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) ◽

10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00141 ◽

2020 ◽

Author(s):

Xueteng Sun ◽

Ke Yan ◽

Xiaokang Zhou

Keyword(s):

Neural Network ◽

Fault Detection ◽

Convolutional Neural Network ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

Download Full-text

Model-based Fault Detection and Diagnosis for HVAC Systems Using Convolutional Neural Network

Proceedings of Building Simulation 2019: 16th Conference of IBPSA ◽

10.26868/25222708.2019.210311 ◽

2020 ◽

Author(s):

Shohei Miyata ◽

Yasunori Akashi ◽

Jongyeon Lim ◽

Yasuhiro Kuwahara ◽

Katsuhiko Tanaka

Keyword(s):

Neural Network ◽

Fault Detection ◽

Convolutional Neural Network ◽

Hvac Systems ◽

Fault Detection And Diagnosis ◽

Model Based ◽

Detection And Diagnosis

Download Full-text

Sparse one-dimensional convolutional neural network-based feature learning for fault detection and diagnosis in multivariable manufacturing processes

Neural Computing and Applications ◽

10.1007/s00521-021-06575-6 ◽

2021 ◽

Author(s):

Jianbo Yu ◽

Chengyi Zhang ◽

Shijin Wang

Keyword(s):

Neural Network ◽

Fault Detection ◽

Convolutional Neural Network ◽

Feature Learning ◽

Fault Detection And Diagnosis ◽

Manufacturing Processes ◽

One Dimensional ◽

Detection And Diagnosis

Download Full-text

One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via visualization

ISA Transactions ◽

10.1016/j.isatra.2021.04.042 ◽

2021 ◽

Author(s):

Shumei Chen ◽

Jianbo Yu ◽

Shijin Wang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Detection ◽

Convolutional Neural Network ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

One Dimensional ◽

Detection And Diagnosis

Download Full-text

Wind turbine fault detection and diagnosis using LSTM neural network

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188709 ◽

2020 ◽

Author(s):

Taoran Yang ◽

Jing Teng ◽

Changling Li ◽

Yizhan Feng

Keyword(s):

Neural Network ◽

Fault Detection ◽

Wind Turbine ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

Download Full-text

A hybrid neural network model for rule generation and its application to process fault detection and diagnosis

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2006.06.007 ◽

2007 ◽

Vol 20 (2) ◽

pp. 203-213 ◽

Author(s):

S.C. Tan ◽

C.P. Lim ◽

M.V.C. Rao

Keyword(s):

Neural Network ◽

Fault Detection ◽

Network Model ◽

Neural Network Model ◽

Fault Detection And Diagnosis ◽

Rule Generation ◽

Hybrid Neural Network ◽

Detection And Diagnosis

Download Full-text

Fault Detection and Diagnosis with A Novel Source-Aware Autoencoder and Deep Residual Neural Network

Neurocomputing ◽

10.1016/j.neucom.2021.11.067 ◽

2021 ◽

Author(s):

Nima Amini ◽

Qinqin Zhu

Keyword(s):

Neural Network ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Detection And Diagnosis

Download Full-text