Detection of poor controller tuning with Gramian Angular Field (GAF) and StackAutoencoder (SAE).

Efficient control loop performance is pivotal in process industries to ensure optimal production, maintain product quality, and adhere to regulatory standards. Poorly tuned controllers can disrupt these objectives, necessitating accurate detection methods. This paper introduces a novel approach for detecting poor controller tuning through advanced techniques: the Gramian Angular Field (GAF) and Stack Auto-Encoder (SAE). Unlike manual methods, this automated system promptly identifies poorly tuned controllers, offering real-time monitoring and timely alerts to operators. The proposed methodology is substantiated through two case studies: the ISDB dataset and the pulp and paper dataset. The outcomes illustrate that the proposed approach correctly determines the appropriate outcome for the majority of the analyzed control loops across diverse industries. • New method detects poorly tuned controllers via Gramian angular field and SAE. • PV and OP images help SAE distinguish poor tuning from other oscillation causes. • Transfer learning has been used to improve methodology's effectiveness. • Tested on benchmark control loops, yielding accurate verdicts for most cases. [ABSTRACT FROM AUTHOR]