Process variables can be classified into three stages: normal operation, incipient fault, and significant fault stage. A two-step incipient fault detection strategy was proposed for monitoring the complex industrial process. The first step aims at the significant fault detection using the traditional multivariate statistical process monitoring methods. Then a method combining the wavelet analysis with the residual evaluation was carried out for monitoring the incipient fault. Wavelet analysis aims at extracting the incipient fault features from process noise. The residual generation is optimization based on the robustness and sensitivity index, which can be realized directly using the test data. An improved kernel density estimation based on signal to noise ratio is proposed to adaptively determine the detection threshold. The proposed incipient fault detection scheme is tested on a numerical example and the Tennessee Eastman process. Compared to other traditional fault detection methods, good monitoring performances, such as higher fault detection rate and lower false alarm rate, are obtained.