Aiming at the problems of AUV of Qianlong 2 in the actual navigation process, such as multi-variable correlation, multiple fault types, large numerical variation influenced by operation status and environmental changes, and strong noise, a new principal component analysis (PCA) fault detection method based on block information extraction is proposed. Firstly, according to the multiple correlations among variables, the correlation information between variables is extracted by sliding window and correlation coefficient method. Secondly, according to the basic stable characteristics of change rate in different operating states and environments, for different types of faults, the cumulative error information of each order statistics of change rate information and change rate information is extracted separately. Finally, three sub-blocks are built based on the extracted feature information, and the PCA model is built and tested for each sub-block respectively. After de-noising by median filtering, the detected results are fused by Bayesian inference. The effectiveness of the proposed method is verified by testing the actual operation data of Qianlong 2.