Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and operational changes must be detected and managed, triggering rapid model adaptation mechanisms. This article presents an adaptive learning approach based on a dendritic cell algorithm for drift detection and a deep neural network model that dynamically adapts to new operational conditions. A kernel density estimator with drift-based bandwidth is used to generate synthetic data for a faster adaptation, focusing on fine-tuning the lowest neural layers. Experimental results over a real-world industrial problem shed light on the outperforming behavior of the proposed approach when compared to other drift detectors and classification models.

中文翻译：

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用于变化条件下工业预测的自适应树突状细胞深度学习方法


工业预测是指根据物联网传感器收集的数据对工业资产的故障进行预测。预测模型可能会遇到概念漂移的不良影响，即出现影响随时间收集的数据的非平稳现象。因此，从数据中学习到的故障模式就变得过时了。为了克服这个问题，必须检测和管理环境和操作变化，从而触发快速模型适应机制。本文提出了一种基于用于漂移检测的树突细胞算法和动态适应新操作条件的深度神经网络模型的自适应学习方法。具有基于漂移的带宽的内核密度估计器用于生成合成数据以实现更快的适应，重点是微调最低神经层。与其他漂移检测器和分类模型相比，针对现实世界工业问题的实验结果揭示了所提出的方法的优异行为。