Partial Least Squares (PLS) as the traditional data-driven method has the problem of poor performance of multi-modal data fault detection. In order to solve the problem, a new fault detection method was proposed, which called PLS based on Local Neighborhood Standardization (LNS) (LNS-PLS). Firstly, the original data was Gaussized by LNS method. On this basis, the monitoring model of PLS was established, and the control limits of T ² and Squared Prediction Error (SPE) were determined. Secondly, the test data was also standardized by the LNS, and then the PLS monitoring indicators of test data were calculated for process monitoring and fault detection, which solved the problem of unable to deal with multi-modal by PLS. The proposed method was applied to numerical examples and penicillin production process, and its test results were compared with those of Principal Component Analysis (PCA), K Nearest Neighbors ( KNN) and PLS. The experimental results show that, the proposed method is superior to PLS, KNN and PCA in fault detection. The proposed method has high accuracy in classification and multi-modal process fault detection.