A probability model can be developed by probabilistic partial least squares (PPLS) under the conditions that both principal components and errors satisfy Gaussian distribution. However

the expectation and variance of the Gaussian distribution is susceptible to outliers. As a result

the model is not robust for the real industrial process. This paper improves the robustness of the PPLS model based on the assumption that the raw data satisfy T distribution rather than Gaussian distribution. By adjusting the freedom degree of T distribution

the proposed robust probabilistic partial least squares (RPPLS) model can overcome the shortcomings of PPLS model. Furthermore

on the basis of RPPLS model

two monitoring indicators GT2 and GSPE are proposed to monitor the process state and the model changes

respectively. Comparing the monitoring performance in the TE process based on PPLS and RPPLS shows that RPPLS is more effective than PPLS in terms of the fault accuracy and the missing alarm rate.