TWIN-ADAPT: Continuous Learning for Digital Twin-Enabled Online Anomaly Classification in IoT-Driven Smart Labs

Future Internet Pub Date : 2024-07-04 DOI:10.3390/fi16070239
Ragini Gupta, Beitong Tian, Yaohui Wang, Klara Nahrstedt
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Abstract

In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor aging, software updates and the introduction of new processes or equipment can lead to dynamic and non-stationary data distributions in evolving data streams. This phenomenon, known as concept drift, poses a substantial challenge for traditional data-driven digital twin static machine learning (ML) models for anomaly detection and classification. Subsequently, the drift in normal and anomalous data distributions over time causes the model performance to decay, resulting in high false alarm rates and missed anomalies. To address this issue, we present TWIN-ADAPT, a continuous learning model within a digital twin framework designed to dynamically update and optimize its anomaly classification algorithm in response to changing data conditions. This model is evaluated against state-of-the-art concept drift adaptation models and tested under simulated drift scenarios using diverse noise distributions to mimic real-world distribution shift in anomalies. TWIN-ADAPT is applied to three critical CPS datasets of Smart Manufacturing Labs (also known as “Cleanrooms”): Fumehood, Lithography Unit and Vacuum Pump. The evaluation results demonstrate that TWIN-ADAPT’s continual learning model for optimized and adaptive anomaly classification achieves a high accuracy and F1 score of 96.97% and 0.97, respectively, on the Fumehood CPS dataset, showing an average performance improvement of 0.57% over the offline model. For the Lithography and Vacuum Pump datasets, TWIN-ADAPT achieves an average accuracy of 69.26% and 71.92%, respectively, with performance improvements of 75.60% and 10.42% over the offline model. These significant improvements highlight the efficacy of TWIN-ADAPT’s adaptive capabilities. Additionally, TWIN-ADAPT shows a very competitive performance when compared with other benchmark drift adaptation algorithms. This performance demonstrates TWIN-ADAPT’s robustness across different modalities and datasets, confirming its suitability for any IoT-driven CPS framework managing diverse data distributions in real time streams. Its adaptability and effectiveness make it a versatile tool for dynamic industrial settings.
TWIN-ADAPT:物联网驱动的智能实验室中数字孪生在线异常分类的持续学习
在集成了物联网(IoT)技术和网络物理系统(CPS)的科学半导体实验室(通常称为洁净室)快速发展的环境中,包括操作变化、传感器老化、软件更新和引入新工艺或设备在内的一些因素会导致不断变化的数据流中出现动态和非稳态数据分布。这种现象被称为概念漂移,对用于异常检测和分类的传统数据驱动数字孪生静态机器学习(ML)模型构成了巨大挑战。随着时间的推移,正常数据和异常数据分布的漂移会导致模型性能下降,从而导致高误报率和漏报异常情况。为了解决这个问题,我们提出了 TWIN-ADAPT,这是一个数字孪生框架内的持续学习模型,旨在根据不断变化的数据条件动态更新和优化其异常分类算法。我们根据最先进的概念漂移适应模型对该模型进行了评估,并在模拟漂移场景下使用不同的噪声分布进行了测试,以模拟真实世界中异常分布的变化。TWIN-ADAPT 应用于智能制造实验室(也称为 "洁净室")的三个关键 CPS 数据集:通风、光刻装置和真空泵。评估结果表明,在 Fumehood CPS 数据集上,TWIN-ADAPT 用于优化和自适应异常分类的持续学习模型实现了较高的准确率和 F1 分数,分别为 96.97% 和 0.97,与离线模型相比,平均性能提高了 0.57%。在光刻和真空泵数据集上,TWIN-ADAPT 的平均准确率分别为 69.26% 和 71.92%,比离线模型的性能分别提高了 75.60% 和 10.42%。这些重大改进凸显了 TWIN-ADAPT 自适应能力的功效。此外,与其他基准漂移自适应算法相比,TWIN-ADAPT 的性能极具竞争力。这种性能证明了 TWIN-ADAPT 在不同模式和数据集上的鲁棒性,证实了它适用于任何物联网驱动的 CPS 框架,管理实时流中的各种数据分布。它的适应性和有效性使其成为动态工业环境中的多功能工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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