自适应未知故障检测与少样本持续学习用于超声金属焊接的条件监控
Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding
AI总结：
本文提出了一种自适应条件监控方法，通过分析多层感知机的隐藏层表示和统计阈值策略实现未知故障检测，并结合少样本持续学习提升焊接过程监控的适应性与准确性。
摘要：
超声金属焊接（UMW）在工业应用中广泛使用，但对工具磨损、表面污染和材料变化敏感，可能导致意外工艺故障和不满意焊缝质量。传统监控系统通常依赖于监督学习模型，假设所有故障类型已知，限制了处理之前未见过的工艺故障的能力。为了解决这一挑战，本文提出了一种自适应条件监控方法，能够实现UMW的未知故障检测和少样本持续学习。未知故障通过分析多层感知机的隐藏层表示并利用统计阈值策略进行检测。一旦检测到，未知故障类型的样本通过持续学习过程整合到现有模型中，通过选择性更新网络的最终层，使模型能够识别新故障类型的同时保留现有类别的知识。为了加快标记过程，余弦相似度变换结合聚类算法将相似的未知样本分组，从而减少手动标记的工作量。实验结果表明，使用多传感器UMW数据集时，所提出的方法在检测未见过的故障条件时达到96%的准确率，同时保持已知类别的可靠分类。在仅使用五个标记样本整合新故障类型后，更新后的模型在测试分类准确率上达到98%。这些结果表明，所提出的方法能够实现适应性监控，具有最小的重新训练成本和时间。所提出的方法为条件监控中的持续学习提供了一种可扩展的解决方案，适用于新工艺条件不断出现的情况，并可扩展到其他制造过程。
英文摘要：
Ultrasonic metal welding (UMW) is widely used in industrial applications but is sensitive to tool wear, surface contamination, and material variability, which can lead to unexpected process faults and unsatisfactory weld quality. Conventional monitoring systems typically rely on supervised learning models that assume all fault types are known in advance, limiting their ability to handle previously unseen process faults. To address this challenge, this paper proposes an adaptive condition monitoring approach that enables unknown fault detection and few-shot continual learning for UMW. Unknown faults are detected by analyzing hidden-layer representations of a multilayer perceptron and leveraging a statistical thresholding strategy. Once detected, the samples from unknown fault types are incorporated into the existing model through a continual learning procedure that selectively updates only the final layers of the network, which enables the model to recognize new fault types while preserving knowledge of existing classes. To accelerate the labeling process, cosine similarity transformation combined with a clustering algorithm groups similar unknown samples, thereby reducing manual labeling effort. Experimental results using a multi-sensor UMW dataset demonstrate that the proposed method achieves 96% accuracy in detecting unseen fault conditions while maintaining reliable classification of known classes. After incorporating a new fault type using only five labeled samples, the updated model achieves 98% testing classification accuracy. These results demonstrate that the proposed approach enables adaptive monitoring with minimal retraining cost and time. The proposed approach provides a scalable solution for continual learning in condition monitoring where new process conditions may constantly emerge over time and is extensible to other manufacturing processes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
