Sparse one-dimensional convolutional neural network-based feature learning for fault detection and diagnosis in multivariable manufacturing processes
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-06575-6.pdf
Reference62 articles.
1. Butte VK, Tang LC (2010) Multivariate charting techniques: a review and a line-column approach. Qual Reliab Eng Int 26:443–451
2. Yu J, Zhang C, Wang S (2021) Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes. Neural Comput Appl 33:3085–3104
3. Peres FAP, Fogliatto FS (2018) Variable selection methods in multivariate statistical process control: a systematic literature review. Comput Ind Eng 115:603–619
4. Jiang Q, Huang B (2016) Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method. J Process Control 46:75–83
5. de Lázaro JMB, Moreno AP, Santiago OL, da Silva Neto AJ (2015) Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems. Comput Ind Eng 87:140–149
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