Online fault diagnosis is crucial for ensuring the safe and efficient operation of industrial processes, particularly in real-world scenarios where operation changes and new unlabeled anomaly data are continuously introduced. However, conventional batch learning and incremental learning (IL) methods rely on historical or labeled data, limiting their diagnostic performance in such dynamic conditions. To address this challenge, we propose a novel unsupervised IL framework that detects unseen faults and updates the model in real time. This approach combines out-of-distribution detection with an IL technique to handle unlabeled new data for online fault diagnosis. Out-of-distribution detection in neural networks detects new classes in the data and assigns labels accordingly. Subsequently, IL with knowledge distillation is used to continuously update the model, incorporating the newly labeled data. A transformer-based classifier, chosen for its high predictive performance and adaptability, serves as the pretrained model, facilitating real-time updates. The framework’s efficacy and accuracy in updating the model for real-world scenarios with unlabeled new data were validated using the Tennessee Eastman process and the CWRU bearing data set, demonstrating significant improvements.

中文翻译：

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用于在线故障诊断的无监督增量学习框架

在线故障诊断对于确保工业过程的安全高效运行至关重要，尤其是在不断引入作变化和新的未标记异常数据的实际场景中。然而，传统的批量学习和增量学习 （IL） 方法依赖于历史或标记数据，限制了它们在此类动态条件下的诊断性能。为了应对这一挑战，我们提出了一种新的无监督 IL 框架，它可以检测看不见的故障并实时更新模型。这种方法将分布外检测与 IL 技术相结合，以处理未标记的新数据以进行在线故障诊断。神经网络中的分布外检测可检测数据中的新类并相应地分配标签。随后，使用带有知识蒸馏的 IL 来持续更新模型，并合并新标记的数据。基于 transformer 的分类器因其高预测性能和适应性而被选中，用作预训练模型，促进实时更新。该框架在使用未标记的新数据为真实场景更新模型的有效性和准确性使用 Tennessee Eastman 流程和 CWRU 轴承数据集进行了验证，显示出显着的改进。