To address the high false alarm rate and delayed fault detection in traditional principal component analysis (PCA) methods for industrial process fault detection, this paper proposes an iterative modeling-based sliding window PCA fault detection method. To improve detection real-time performance, an iterative approach is used during the modeling process to progressively remove outlier samples from the PCA model data, optimizing the PCA model. To reduce the false alarm rate, a sliding observation window is employed to count the number of outlier samples, and a second confidence limit is constructed for fault detection. To enhance fault detection accuracy, a composite statistic is used as the detection index, which considers anomalies in both the principal component direction and the residual space. To validate the effectiveness of the proposed method, simulation experiments were conducted using numerical examples and the Tennessee-Eastman (TE) process. In the numerical examples, the fault detection accuracy reached 89.20% with a false alarm rate of 1.33%. For the TE process, the fault detection accuracy reached 99.39% with a false alarm rate of 3.12%.