This study focuses on developing a nonlinear fault identification approach whose goal is to find the fault variables after a fault is detected. For nonlinear processes monitoring, it is a challenging problem to identify the fault variables since the monitoring statistics are usually an implicit function with respect to the process variables. In such a case, a traditional contribution plot-based fault identification approach that decomposes the monitoring statistics into the summation of variable contributions will be invalid. To solve the issue mentioned above, in this paper, a new nonlinear processes monitoring technique including fault detection and identification approaches is proposed based on kernel independent component analysis (KICA). On the basis of KICA, two monitoring statistics are defined for fault detection, and the contribution of each variable to the monitoring statistics is defined for fault variable identification. After a fault is detected, one can use the proposed fault variable identification method to judge which variable has the largest impact on the abnormal event and demonstrate identification. At last, the pulverized coal fired boiler process (PCFBP) is taken to evaluate the validity and effectiveness of the proposed approaches. Experiment results show that the proposed methods have satisfactory monitoring performance in the application to PCFBP.