Hierarchical Latent Variable Extraction and Multisegment Probability Density Analysis Method for Incipient Fault Detection

The incipient fault is difficult to detect because of its small amplitude and insignificant impact, however, ignoring such fault may cause irreversible damage to the system. In this article, a hierarchical latent variable extraction and multisegment probability density analysis method is proposed to detect the incipient fault. First, three data subspaces are constructed, which are named dominant, intermediate, and residual spaces, and key latent variables which contain more offline variance or online variation information will be retained. Afterward, the expanded data distribution interval and multiple data segmentsare constructed for the probability density estimation. Based on the improved symmetric divergence index, the distribution distance between the online data and offline modeling data can be evaluated, which has achieved 95.3% and 86.8% average detection rates for the faults in numerical case and Tennessee Eastman process. Finally, a real multiphase flow facility is used to demonstrate the effectiveness of the proposed method.

Automated summary

In this article, a hierarchical latent variable extraction and multisegment probability density analysis method is proposed to detect the incipient fault. The method achieves high detection rates by constructing data subspaces and evaluating distribution distance. The effectiveness of the proposed method is demonstrated in a real-world application.