Knowledge Distillation-enabled Multi-stage Incremental Learning for Online Process Monitoring in Advanced Manufacturing

Shi, Zhangyue; Li, Yuxuan; Liu, Chenang

doi:10.1109/icdmw58026.2022.00154

Cited by 7 publications

(1 citation statement)

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“…The ability of deep auto-encoder networks to learn hierarchical features from unlabelled data is taken advantage of in incremental learning [18]. Shi et al [60] developed a multi-stage incremental learning approach based on knowledge distillation for online process monitoring. In a machine learning-based process monitoring application, in-situ monitoring and dynamic decision making are shown to be capable only via incremental learning of new anomalies or intrusion attacks.…”

Section: Incremental Learning In Manufacturingmentioning

confidence: 99%

A Survey of Incremental Deep Learning for Defect Detection in Manufacturing

Mohandas,

Southern,

O’Connell

et al. 2024

BDCC

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Deep learning based visual cognition has greatly improved the accuracy of defect detection, reducing processing times and increasing product throughput across a variety of manufacturing use cases. There is however a continuing need for rigorous procedures to dynamically update model-based detection methods that use sequential streaming during the training phase. This paper reviews how new process, training or validation information is rigorously incorporated in real time when detection exceptions arise during inspection. In particular, consideration is given to how new tasks, classes or decision pathways are added to existing models or datasets in a controlled fashion. An analysis of studies from the incremental learning literature is presented, where the emphasis is on the mitigation of process complexity challenges such as, catastrophic forgetting. Further, practical implementation issues that are known to affect the complexity of deep learning model architecture, including memory allocation for incoming sequential data or incremental learning accuracy, is considered. The paper highlights case study results and methods that have been used to successfully mitigate such real-time manufacturing challenges.

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Section: Incremental Learning In Manufacturingmentioning

confidence: 99%