A fault diagnosis framework for chemical processes is proposed, which combines K-means synthetic minority oversampling techniquewith conditional adversarial domain adaptation to address issues such as feature coupling caused by the temporal dependence of multivariate sensing data, data distribution shift caused by changes in operating conditions, and imbalanced sample data in chemical processes. Firstly, the original one-dimensional data is converted into multiple two-dimensional time window data using time window segmentation technology. Within these windows, the Kmeans SMOTE method is used to expand the minority class fault samples. The expanded samples can retain the complete temporal fault features, and this algorithm can also reduce the number of generated noise samples; then, domain adaptation techniques are used to align the feature distributions of the source domain and the target domain, reducing the distribution differences between the two and enabling the fault diagnosis model trained on the source domain to effectively identify fault categories under new operating conditions; finally, diagnostic experiments were conducted using fault data from the Tennessee Eastman process, and the effectiveness of the proposed method was validated by comparing its diagnostic rates with models such as CDAN, DANN, and JDA.