Near-infrared fault detection based on stacked regularized auto-encoder network

Near-infrared spectroscopy as a fast, efficient, non-destructive, and non-polluting technology is employed in this paper to acquire process data at the molecular level to detect the process fault. Due to the high intrinsic dimension, information redundancy, multiple linear correlations between variables and high-sensitiveness of spectral data, stacked auto-encoder as an unsupervised learning method is utilized. In addition, for avoiding over fitting of these auto-encoders and making the model more general, L2 regularization term is employed. To realize the detection and classification of multi-class faults, the softmax regression is adopted as a multi-classification algorithm. Finally, the eligibility of the mentioned neural network is investigated through a crude oil desalination and dehydration process. Nonlinear dimensional reduction of spectral data with minimizing the information loss during data compression, high accuracy, and multi-class fault detection with good visualization of its decision edge are three main advantages of the proposed fault detection method comparing with existing methods.