Output-related fault detection in non-stationary processes using constructive correlative-SAE and demoting correlative-DNN

Abstract

Fault detection in non-stationary processes is a timely research topic in industrial systems. The conventional approaches based on principal component regression (PCR) and partial least-squares (PLS) cannot be loosely used for non-stationary and non-linear processes when the statistical behaviour of the measurements does not follow a Gaussian distribution with constant mean and standard deviation values. This paper introduces a new application of deep learning (DL), specifically a combination of proposed correlative stacked auto-encoder (C-SAE) and correlative deep neural networks (C-DNN) for output-related anomaly detection without complete decomposition of process variables with respect to quality output(s). With this aim, two new constructive and demoting loss functions are proposed to relatively decompose the process measurements with respect to their relevance to quality output variable(s). The loss functions are modified with the incorporation of non-linear correlation analysis and hence integrated into SAE and DNN structures to suggest a correlative SAE and DNN. The proposed C-SAE and C-DNN are integrated into a scheme with an inverted pyramid structure that enables output-related fault detection without limiting stationarity assumptions. Moreover, the proposed framework can be freely applied to both linear and non-linear processes. The performance of the proposed DL strategy is tested and validated on a non-stationary numerical example and Tennessee Eastman Process. The comparison results with recent approaches indicate the outperformance of the proposed approach for process output-related fault detection purposes.