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, obtained as 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.

翻译：在复杂生产线中，建立严格且快速响应的规则以判定系统处于受控状态还是失控状态至关重要。本研究探索一种仿生方法，通过数字化模拟蚁群行为对受控/失控状态进行分类，并预测需要维护的临界状态转换。以工业薯片油炸过程为案例提供了应用场景。在每个两分钟的油炸周期中，采集包含八个温度读数的序列。每个序列被视为释放虚拟信息素的数字蚂蚁，生成基础评分。代表新蚂蚁的新序列可能增强或削弱该评分，从而产生反映系统演变状态的修正基础评分。极端温度、序列内大幅波动或突变点检测等信号会生成威胁评分，该评分将被累加至修正基础评分中。由于信息素在未被强化时会随时间自然衰减，本研究引入环境评分来反映近期系统动态，模拟真实蚁群行为。该评分根据过去一小时内收集的修正基础评分计算得出。最终通过将修正基础评分、威胁评分与环境评分相加获得总评分，作为实时系统分类及受控向失控状态转换预测的主要指标。这种受蚁群优化启发的算法为工业环境中的过程监控与预测性维护提供了自适应且可解释的框架。