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Recognition of various defect patterns exhibited in discrete manufacturing processes can significantly reduce the diagnostic processes, and increase manufacturing process stability and quality. Thus the effective recognizers are in great demand to improve the performance of process pattern recognition (PPR). Deep learning has been widely applied in image and visual analysis with great successes. However, the application of deep learning in feature learning for process control is still few. This paper presents an effective and reliable deep learning method known as stacked denoising autoencoder (SDAE) for PPR in manufacturing processes. This paper will concentrate on developing an SDAE model to learn effective features from the process signals and then implementing an effective PPR through a deep network architecture. Feature visualization is also performed to explicitly present the feature representation …