There is an urgent need for industrial manufacturing to fully integrate with emerging technologies to build enterprise core competitiveness. Currently, existing methods have difficulty meeting the high-precision and stability practical requirements with diversified industrial products. In this study, a PMAE (Parallel Multiple AutoEncoders, PMAE) model is proposed, which is designed with parallel multiple encoders based on the AutoEncoder framework. It uses the network of parallel multiple encoders with different encoder structures to obtain latent features that have rich and precise semantic information. A consistency objective function is proposed to make the PMAE network converge stably and rapidly, which allows the aggregated latent features to be simultaneously reconstructed and adaptively classified. Compared with the state-of-the-art methods on the NEU-CLS, Data-Crack, and DAGM2007 datasets, our method achieves the most stable performance and the highest accuracy in different defect detection tasks.