(369g) Negative Selection Algorithm: An Artificial Immune System for Fault Diagnosis In Continuous and Batch Processes

Artificial immune system is a new artificial intelligence methodology that is increasingly attracting much attention for monitoring engineered systems. In an artificial immune system (AIS), principles and processes of the natural immune system are abstracted and applied in pattern recognition and a variety of other applications in the field of science and engineering. One popular immune-inspired principles is Negative Selection through which self-tolerant T-cell are generated, thus allowing the immune system to discriminate self proteins from foreign (non-self) ones. This principle leads to negative selection algorithm (NSA) where in a collection of spherical detectors are generated in the complementary (non-self) space and used to classify new (unseen) data as self or non-self. It is now used extensively particularly in the situations where only large amount of self (normal) samples are available but abnormal samples are either unavailable or very rare.

In this work, we propose a real-valued NSA based framework for Fault detection and identification (FDI) of chemical process in real time. The effectiveness of the proposed real-valued NSA based FDI framework is demonstrated through the online fault detection and diagnosis in two case studies ? a continuous lab-scale distillation column and a batch fermentation simulation. The results show that the proposed NSA based framework provides excellent monitoring and diagnosis performances for both these cases with (i) complete fault coverage ? all the faults studied can be readily detected and identified, (ii) high overall recognition rate (~90%), (ii) low false positive rate (~<3%), (iii) high true positive rate (~>95%), (iv) early fault detection (~10-15 samples delay) and diagnosis (~60-80 samples delay). A comparative study with other data-driven approaches will also be reported.

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