Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

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Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry (2008, methodology): “Another technique suitable for fault diagnosis is discriminant PLS (Kemsley, 1996; Chiang et al., 2000).”
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (2014, background): “Moreover, with consideration of an additional class of data denoting the normal operating conditions, FDA can also be applied on industrial processes for fault detection [57]– [59].”
Data Mining and Analytics in the Process Industry: The Role of Machine Learning (2017, background): “the root causes of the fault may be located in a small part of the process or precisely to some specific sensors/actuators [26].”
Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization (2014, methodology): “In the TE process model, 20 process faults were initially defined in [34] and an additional valve fault was further introduced in [35], which can be seen in Table III.”
Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application (2013, unknown): “[30, 31].”