Abstract: Because CVA (canonical variate analysis) is unable to adapt the characteristics of time-varying processes， by which the normal changes of the process is easily identified as faults， it is very necessary to propose a monitoring approach for time-varying processes. The exponential weighted moving average approach was adopted to update the covariance of the past observation vectors. The most critical problem faced by recursive CVA algorithm is the high computation cost. To reduce the computation cost， the first order perturbation theory was introduced to update recursively the singular value decomposition (SVD) of the Hankel matrix. The computation cost of recursive SVD based on the first order perturbation theory is significantly less compared to the SVD. Recursive canonical variate analysis based on the first order perturbation (RCVA-FOP) was applied in the Tennessee Eastman chemical process. Simulation results indicate that the proposed method not only can effectively adapt to the normal change of time-varying processes， but also can detect two types of faults.

Key words: first order perturbation theory, CVA (canonical variate analysis), time-varying processes, fault detection