Abstract
A non-invasive method for detecting valves suffering from stiction using multi-layer feed-forward neural networks (NN) is proposed, via a simple class-based diagnosis. The proposed Stiction Detection Network (SDN) uses a transformation of PV (process variable) and OP (controller output) operational data. Verification of the proposed SDN model’s detection accuracy is done through cross-validation with generated samples and benchmarking with various industrial loops. The industrial loop benchmark predictions of the proposed SDN method has a combined accuracy of 78% (75% in predicting stiction, and 81% for non-stiction) in predicting loop condition, matching capabilities of other established methods in accurately predicting realistic industrial loops suffering from stiction, while also being applicable to all types of oscillatory control signals.
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