Sparsity and manifold regularized convolutional auto-encoders-based feature learning for fault detection of multivariate processes

Deep neural networks (DNNs) are popular in process monitoring for its remarkable feature extraction from data. However, the increased dimension and correlation of the process variables degrade performance of these DNNs in feature extraction of data. This paper proposes a sparsity and manifold regularized convolutional auto-encoders (SMRCAE) for fault detection of complex multivariate processes. SMRCAE can learn high-level features from the data in an unsupervised way. A sparsity-and-manifold-regularization term is integrated into the learning procedure of SMRCAE, which allows SMRCAE to perform feature selection and capture intrinsic data information. Moreover, a depthwise separable convolution (dsConv) block is used to reduce the computational cost. Two typical fault detection statistics, namely Hotelling's T-squared (T-2) and the squared prediction error (SPE), are developed on the feature space and residual space of SMRCAE, respectively. The performance of SMRCAE is evaluated on an industrial benchmark, i.e., Tennessee Eastman process (TEP) and a real process of industrial conveyor belts. The experimental results show the feasibility of SMRCAE in extracting representative features for process fault detection. The average fault detection rate of SMRCAE is 92.03% and 100% on the two cases, respectively.

Automated summary

This paper proposes a fault detection method for complex multivariate processes using sparsity and manifold regularized convolutional auto-encoders (SMRCAE) to extract features and evaluates its performance on an industrial benchmark.