An artificial immune system for adaptive fault detection, diagnosis and recovery

Abstract

Listen

Translate article

The human immune system provides rich metaphors for adaptive pattern recognition. Fault detection and diagnosis in chemical processes is commonly formulated as a pattern recognition problem. However, conventional methods for fault diagnosis often do not have a mechanism to adapt and learn as the process changes over time. In this paper, we propose an Artificial Immune System (AIS) framework that endows learning to statistical process monitoring techniques such as Principal component analysis. The proposed AIS framework also provides a direct means to incorporate recovery actions after a failure has been detected and diagnosed. We demonstrate the efficacy of the proposed framework using a simulated binary distillation column case study.