Abstract
Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency. Traditional monitoring methods often rely on fixed thresholds and empirical rules, which may not be sensitive enough to detect subtle changes in system health and predict impending failures. To address this limitation, this paper proposes, a novel Attention-based convolutional autoencoder (ABCD) for risk detection and map the risk value derive to the maintenance planning. ABCD learns the normal behavior of conductivity from historical data of a real-world industrial cooling system and reconstructs the input data, identifying anomalies that deviate from the expected patterns. The framework also employs calibration techniques to ensure the reliability of its predictions. Evaluation results demonstrate that with the attention mechanism in ABCD a 57.4% increase in performance and a reduction of false alarms by 9.37% is seen compared to without attention. The approach can effectively detect risks, the risk priority rank mapped to maintenance, providing valuable insights for cooling system designers and service personnel. Calibration error of 0.03% indicates that the model is well-calibrated and enhances model's trustworthiness, enabling informed decisions about maintenance strategies
Abstract (translated)
工业系统中的异常检测对于防止设备故障、确保风险识别和维持整体系统效率至关重要。传统的监测方法通常依赖于固定的阈值和经验规则，这些阈值可能不足以检测系统健康状况的细微变化并预测即将发生的故障。为了克服这一局限，本文提出了一种新颖的自注意力卷积自动编码器（ABCD）用于风险检测，并将其风险价值映射到维护计划。ABCD从现实工业冷却系统的 historical 数据中学习电导率的正常行为，并重构输入数据，识别出与预期模式不符的异常。框架还采用校准技术来确保其预测的可靠性。评估结果表明，与没有注意机制的 ABCD 相比，性能提高了 57.4%，虚假警报减少了 9.37%。这种方法可以有效地检测风险，将风险优先级映射到维护，为冷却系统设计和服务人员提供了宝贵的见解。校准误差为 0.03% 说明模型已经很好地校准，提高了模型的可信度，使得在维护策略方面做出知情的决策。
URL
https://arxiv.org/abs/2404.16183
