Process monitoring based on Geodesic Distance Statistic(GDS) is proposed in this article for that fault monitoring method based on Euclidean distance of k Nearest Neighbors (kNN) could not fully reflect the complex characteristics between data with multiple conditions. To start

the batch process data is expanded and standardized by the batch direction. Principal Component Analysis (PCA) is utilized for data dimensionality reduction. Next

Get empowered adjacency matrix in the reduced space. Improved Dijkstra (IDijkstra) algorithm is proposed based on Dijkstra algorithm for easier implement. It can better characterize the actual shortest distance of the nonlinear data and reflect the local neighborhood relations between batch data. Meanwhile

statistics Dα based on α power of Geodesic distance which could reduce the deviation of distance from the edge of the training data is structured for fault monitoring compared with D2 based on quadratic sum of Euclidean distance. Finally

the effectiveness of the proposed algorithm is verified by applying it in numerical simulation and industry examples.