Most incidents in complex systems such as process plants are not-chance events and weak signals emerge for a long time before incidents occur. It is necessary to identify and respond to the weak signals as early as possible for preventing incidents. However, in the era of Industry 4.0, recognizing weak signals from the abundance of data is challenging. Since the terminology “weak signal” was not precisely defined, the study first proposed a formal definition of “weak signal” and discussed its characteristics. Additionally, a framework was developed to address the challenges of observing, evaluating, and responding to weak signals in complex systems. The framework first utilized Function Resonance Analysis Method (FRAM) to determine the information to be collected for observing weak signals. Then, the relevance of weak signals and the corresponding responses were evaluated by utilizing Balanced Random Forest (BRF), Weighted Random Forest (WRF) and Decision Tree (DT) classification models. The case study of a hypothetical batch process showed great potentials for applying the framework in real operations. Based on the potential weak signals that were indicated by FRAM, probabilities of temperature deviations in the process were predicted with high accuracy by the optimal BRF model. Underlying weak signals and the corresponding responses to reduce the probabilities were identified from the DT.

过程工厂等复杂系统中的大多数事件都是偶然事件，并且在事件发生之前很长一段时间内都会出现微弱的信号。有必要尽早识别和响应微弱信号，以防止发生事故。然而，在工业 4.0 时代，从大量数据中识别微弱信号具有挑战性。由于术语“弱信号”没有准确定义，该研究首先提出了“弱信号”的正式定义并讨论了其特征。此外，还开发了一个框架来解决在复杂系统中观察、评估和响应弱信号的挑战。该框架首先利用函数共振分析方法 (FRAM) 来确定要收集的用于观察弱信号的信息。然后，通过利用平衡随机森林 (BRF)、加权随机森林 (WRF) 和决策树 (DT) 分类模型评估弱信号和相应响应的相关性。假设批处理的案例研究显示了将该框架应用于实际操作的巨大潜力。基于 FRAM 指示的潜在弱信号，优化 BRF 模型高精度地预测了过程中的温度偏差概率。从 DT 中识别出潜在的弱信号和相应的降低概率的响应。假设批处理的案例研究显示了将该框架应用于实际操作的巨大潜力。基于 FRAM 指示的潜在弱信号，优化 BRF 模型高精度地预测了过程中的温度偏差概率。从 DT 中识别出潜在的弱信号和相应的降低概率的响应。假设批处理的案例研究显示了将该框架应用于实际操作的巨大潜力。基于 FRAM 指示的潜在弱信号，优化 BRF 模型高精度地预测了过程中的温度偏差概率。从 DT 中识别出潜在的弱信号和相应的降低概率的响应。