Previously in her secondment at the University of Alberta, Nina created the continuous stirring tank heater case study. This case study can be used for testing techniques in various aspects of process systems engineering, such as system identification, fault detection and diagnosis, and controller design. With my research being about data-driven fault detection algorithms for process monitoring, this case study is of great interest to me. Although many previous works have already used this case study for testing the fault detection and diagnosis algorithms, there is potential for more practical faulty scenarios. Inspired by the discussions with Nina, I try to create two fault scenarios. The rst one develops gradually over time and the second one is to simulate a fault that may be physically induced in the experiment. The data from both healthy and faulty scenarios are collected Canonical Variate Analysis, which is a multivariate approach for modelling linear and dynamic processes, is applied to the data for fault detection. This test can be the starting point towards fault detection and prognosis for nonlinear and dynamic processes with multiple operating modes and transition periods.