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Aiming at the characteristics of multimodal industrial processes, A fault detection method based on Time-Space Nearest Neighborhood Standardization and Robust AutoEncoder (TSNS-RAE) is proposed. TSNS processes data by considering both temporal and spatial neighbors of samples, thus eliminating data dynamics and multimodal features； Compared with ordinary autoencoders, robust autoencoders improve the noise resistance and robustness of the model, and have better ability to extract nonlinear features. The TSNS-RAE model divides the original data space into model space and residual space, and selects SPE statistics of residual space as monitoring statistics. Numerical cases and penicillin experiments are used to verify the feasibility of TSNS-RAE.