Condition Assessment of Production Separators using Neuro-Fuzzy Tools for Signal Validation and Diagnosis

Abstract

This paper describes a sub-activity within the CORD Production Separator project focusing on applying neural network-based signal validation and diagnosis tools for abnormality detection and condition assessment of the internal equipment of production separators in the petroleum industry. The tools have been successfully applied for the monitoring of various processes within the nuclear industry, and perform their functions by learning how various process signals are correlated. PEANO will validate and calibrate a sensor by determining any deviations from the expected correlations to other sensors, while ALADDIN recognizes a fault by its associated time-based “signature” in the combined data matrix.