Active Data-sharing Framework for Enhancing Anomaly Detection in Advanced Manufacturing Systems with Distribution Mismatch

Core Concepts

The core message of this work is to develop an Active Data-sharing (ADs) framework that can simultaneously select the most informative data samples for anomaly detection and mitigate the impact of distribution mismatch when sharing data across multiple manufacturing processes.

Abstract

The paper presents an Active Data-sharing (ADs) framework to address the challenges of data scarcity, limited annotation budget, and distribution mismatch when sharing data across multiple manufacturing processes for building machine learning models.
The key highlights are:

The ADs framework integrates the architectures of contrastive learning (CL) and active learning (AL) to simultaneously select the most informative data samples for the downstream anomaly detection task and mitigate the impact of distribution mismatch.

A novel acquisition function is developed that combines the informativeness score from uncertainty sampling and the similarity score from contrastive learning. This allows selecting data samples that are both highly informative and closely match the target data distribution.

The effectiveness of the ADs framework is evaluated on real-world in-situ monitoring data collected from three 3D printing machines, two of which have identical specifications while the third is different. The results show that the proposed method can outperform benchmark approaches by requiring only 26% of labeled training data while achieving better anomaly detection accuracy.

Theoretical analysis is provided to prove the Pareto optimality of the joint acquisition function used in the ADs framework, ensuring the selected data samples simultaneously satisfy both objectives of informativeness and distribution similarity.

Stats

The monitoring data was collected from three 3D printing machines, two of which have identical specifications (S1 and S2) while the third is different (L1).

Quotes

"The objective of this paper is to develop an intelligent data-sharing framework. It is designed to simultaneously select the most informative data points benefiting the downstream tasks and mitigate the impact of low-quality data."
"The results demonstrated that our proposed method outperforms the benchmark methods by only requiring 26% of labeled training samples. In addition, all selected data samples are from machines with similar conditions, while the data from the different machines are prevented from misleading the training."

Key Insights Distilled From

ADs

by Yue Zhao,Yux... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00572.pdf

Deeper Inquiries

How can the proposed ADs framework be extended to handle more than two target distributions in the data-sharing scenario

To extend the ADs framework to handle more than two target distributions in the data-sharing scenario, the contrastive learning approach can be modified to incorporate multiple target distributions. This can be achieved by training the contrastive model with additional positive pairs representing the different target distributions. By expanding the training data to include samples from all target distributions, the model can learn to differentiate between the various distributions and provide similarity scores accordingly. Additionally, the joint query strategy can be adjusted to consider the similarity scores across multiple distributions when selecting samples for annotation. This way, the framework can effectively handle data-sharing scenarios with multiple target distributions by ensuring that only samples from the desired distributions are selected for downstream tasks.

What are the potential limitations of the contrastive learning approach used in ADs for identifying distribution mismatch, and how can it be further improved

While contrastive learning is effective in identifying distribution mismatch by learning similarity features, it may have limitations in scenarios where the distributions are highly complex or overlapping. In such cases, the model may struggle to accurately distinguish between different distributions, leading to misclassification of samples. To address this limitation, the contrastive learning approach used in ADs can be further improved by incorporating more advanced distance metrics or embedding techniques that can better capture the nuances of the data distributions. Additionally, ensemble methods or hybrid models combining contrastive learning with other approaches like clustering or generative modeling can enhance the model's ability to identify distribution mismatch in complex datasets.

What other downstream tasks beyond anomaly detection could benefit from the ADs framework, and how would the framework need to be adapted for those applications

The ADs framework can benefit a wide range of downstream tasks beyond anomaly detection by adapting its methodology to suit the specific requirements of each task. For tasks like image classification, object detection, or natural language processing, the framework can be tailored to focus on selecting samples that are most informative for the particular task at hand. For image classification, the similarity scores can be based on visual features extracted from the images, while the uncertainty scores can reflect the model's confidence in class predictions. By customizing the feature extraction and scoring mechanisms, the ADs framework can be adapted to various downstream tasks, ensuring that only high-quality and relevant data is shared and annotated for improved model performance.