A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

By Shen Yin, Steven X. Ding, Adel Haghani, Haiyang Hao, Ping Zhang. 2012

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Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application (2013, background): “…Application Kaixiang Peng,1 Kai Zhang,1 and Gang Li2 1 Key Laboratory for Advanced Control of Iron and Steel Process, School of Automation and Electrical Engineering, University of Science and Technology of Beijing, Beijing 100083, China 2Department of Automation, TNList, Tsinghua University,…”
Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis (2015, methodology): “In the second block, i.e., IDV(5), IDV(10), IDV(16), and IDV(19-20), IPLS provides a significantly higher FDRs compared with PLS and T-PLS.”
Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis (2015, methodology): “The feature extraction techniques in [28] were PCA, partial least squares, independent component analysis, Fisher discriminant analysis, and subspace-aided approach.”
Sequential Fault Diagnosis Based on LSTM Neural Network (2018, background): “[2] provided a comparison study on different FE algorithms for process monitoring and fault diagnosis.”