AI-based digital twins are at the leading edge of the Industry 4.0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis. Information collected from industrial assets is produced in a continuous fashion, yielding data streams that must be processed under stringent timing constraints. Such data streams are usually subject to non-stationary phenomena, causing that the data distribution of the streams may change, and thus the knowledge captured by models used for data analysis may become obsolete (leading to the so-called concept drift effect). The early detection of the change (drift) is crucial for updating the model's knowledge, which is challenging especially in scenarios where the ground truth associated to the stream data is not readily available. Among many other techniques, the estimation of the model's confidence has been timidly suggested in a few studies as a criterion for detecting drifts in unsupervised settings. The goal of this manuscript is to confirm and expose solidly the connection between the model's confidence in its output and the presence of a concept drift, showcasing it experimentally and advocating for a major consideration of uncertainty estimation in comparative studies to be reported in the future.
翻译:以AI为基础的数字双胞胎在工业4.0革命的前沿处于领先地位,这种革命在技术上通过物联网和实时数据分析得到技术授权。从工业资产中收集的信息是连续不断地生成的,产生数据流,必须在严格的时间限制下处理。这类数据流通常受到非静止现象的影响,导致流的数据分布可能发生变化,因此数据分析模型所获取的知识可能过时(导致所谓的概念漂移效应 ) 。尽早发现变化(漂移)对于更新模型的知识至关重要,特别是在与流数据相关的地面真相不易获得的情况下,这具有挑战性。除其他技术外,在几项研究中,模型信心的估计被微弱地建议为一项标准,用以检测未受监督环境中的漂移情况。该手稿的目的是确认并切实揭示模型对产出的信心与存在概念漂移之间的联系,实验性地展示该变化,并倡导在将来报告的比较研究中对不确定性的估计进行重大考虑。</s>