In this paper, we develop an improved fault detection (FD) technique in order to enhance the monitoring abilities of nonlinear biological processes. Generalized likelihood ratio test (GLRT)-based kernel principal component analysis (KPCA) (called also kernel GLRT) is an effective data-driven technique for monitoring nonlinear processes. However, it is well known that the data collected from complex and multivariate processes are multiscale due to the variety of changes that could occur in process with different localization in time and frequency. Thus, to enhance the process monitoring abilities, we propose to combine the advantages of kernel GLRT and multiscale representation using wavelets by developing a multiscale kernel GLRT (MS-KGLRT) detection chart. The proposed fault detection approach is addressed so that the KPCA is used to compute the model in the feature space and the MS-KGLRT chart is applied to detect the faults. The detection performance of the new chart is studied using two examples, one using synthetic data and the other using biological process representing a Cad System in E. Coli (CSEC) model for detecting small and moderate shifts (offset or bias and drift). The MS-KGLRT chart is used to enhance fault detection of the CSEC model through monitoring some of the key variables involved in this model such as enzymes, lysine, and cadaverine.