Fault detection of uncertain chemical processes using interval partial least squares-based generalized likelihood ratio test

Fault detection (FD) is essential for monitoring various chemical processes. Many chemical processes can be described by input-output models. Partial least squares (PLS) method is one of the most popular statistical approaches used for modeling and monitoring chemical processes. In many situations, measured process data can exhibit some level of uncertainty. In such cases, expressing the data in interval form can be useful. Therefore, this work addresses the problem of fault detection of uncertain chemical processes using interval input-output PLS-based generalized likelihood ratio test (GLRT). The proposed novel approach helps extend the applicability of GLRT to uncertain processes represented by interval-valued input-output data. In the developed approach, the modeling phase is performed using PLS and then the GLRT chart is applied to the interval residuals for fault detection. To evaluate the fault detection abilities of the proposed PLS-based interval valued GLRT approach, two examples are used: a simulated example and a distillation column example. The performance of the proposed technique is evaluated in terms of the missed detection and false alarms rates.