A Digital Pheromone-Based Approach for In/Out-of-Control Classification
Pedro Pestana, M. Fátima Brilhante
TL;DR
The paper addresses rapid InC/OutC classification and forecasting in complex production lines by leveraging a bio-inspired digital pheromone framework, applied to potato chip frying data. It introduces a four-score architecture—Base Score ($BS$), Modified Base Score ($MBS$), Threat Score ($ThS$), and Environmental Score ($ES$)—to compute a Total Score ($TS$ that drives real-time classification and maintenance forecasting). The method is demonstrated on 2-minute frying cycles with 8 temperature readings each, using explicit equations for each score (e.g., $BS = (max/min) \times (N_{184↑} + N_{188↑} + N_{192↑} - N_{180↓})/2$, etc.) and change-point considerations to capture regime shifts. Findings indicate strong sensitivity and specificity in classification with alarms triggering maintenance events, while acknowledging noise and startup-phase effects as limitations and highlighting the approach’s interpretability and adaptability for SQC in industrial settings.
Abstract
In complex production lines, it is essential to have strict, fast-acting rules to determine whether the system is In Control (InC) or Out of Control (OutC). This study explores a bio-inspired method that digitally mimics ant colony behavior to classify InC/OutC states and forecast imminent transitions requiring maintenance. A case study on industrial potato chip frying provides the application context. During each two-minute frying cycle, sequences of eight temperature readings are collected. Each sequence is treated as a digital ant depositing virtual pheromones, generating a Base Score. New sequences, representing new ants, can either reinforce or weaken this score, leading to a Modified Base Score that reflects the system's evolving condition. Signals such as extreme temperatures, large variations within a sequence, or the detection of change-points contribute to a Threat Score, which is added to the Modified Base Score. Since pheromones naturally decay over time unless reinforced, an Environmental Score is incorporated to reflect recent system dynamics, imitating real ant behavior. This score is calculated from the Modified Base Scores collected over the past hour. The resulting Total Score - the sum of the Modified Base Score, Threat Score, and Environmental Score - is used as the main indicator for real-time system classification and forecasting of transitions from InC to OutC. This ant colony optimization-inspired approach provides an adaptive and interpretable framework for process monitoring and predictive maintenance in industrial environments.