In industrial process fault monitoring, it is very important to collect accurate data, but in the actual process, there are often various noises that are difficult to eliminate in the collected data due to sensor accuracy, measurement errors, or human factors. Existing statistical process monitoring methods often ignore the problem of data noise. To solve this problem, a sliding window wavelet denoising-global local preserving projections (SWWD-GLPP) process monitoring method is proposed. In the offline stage, the wavelet denoising method is used to denoise the offline data, and then, the GLPP method is used for offline modeling, and then, the control limit is obtained by the kernel density estimation method. In the online phase, the sliding window wavelet denoising method is used to denoise the online data in real time. Then, use the model of the GLPP method to find the statistics, compare them with the control limit, judge the fault situation, and finally, use the contribution graph method to determine the variable that caused the fault, so as to diagnose the fault. This article uses a numerical case to illustrate the effectiveness of the algorithm, using the Tennessee Eastman (TE) process to compare the traditional principal component analysis (PCA) and GLPP methods to further prove the effectiveness and superiority of the method.