A Variational Bayesian Inference-Based Robust Dissimilarity Analytics Model for Industrial Fault Detection

Abstract

Due to various reasons, outliers, ambient noise and missing data inevitably exist in the industrial processes, and thus the robustness is important when establishing monitoring models. In this study, a robust dissimilarity analytics model (RDAM) is established with Laplace distribution to detect process anomalies in noisy environment. Because of the heavy-tailed characteristic of Laplace distribution, the proposed RDAM method is more robust to ambient noise and outliers when compared to Gaussian distribution-based models. Besides, the missing data problem is also considered and solved in the model development procedure. Using the variational Bayesian inference, the model parameters and latent variables of the RDAM model can be estimated. After that, a monitoring strategy is designed based on the obtained results with both static and dynamic statistics. By this means, both the static deviation of the current sample and the temporal correlation within the process data can be effectively revealed. A simulated example and a real low-pressure heater process are adopted to illustrate the performance of the proposed RDAM method. Specifically, the proposed RDAM method is robust to the ambient noise and missing values, and it has better detection sensitivity for the process anomalies than the selected comparison methods.