Meta-Learning-Based LSTM-Autoencoder for Low-Data Anomaly Detection in Retrofitted CNC Machine Using Multi-Machine Datasets

Abstract

In recent manufacturing environments, the use of digitally retrofitted equipment has grown substantially, yet this trend also amplifies the challenge of ensuring stable operation through effective anomaly detection. Retrofitted systems suffer from two critical obstacles: a severe scarcity of labeled data and substantial variability in operational patterns across machines and products. To overcome these issues, this study introduces a novel anomaly detection framework that integrates Model-Agnostic Meta-Learning (MAML) with a Long Short-Term Memory Autoencoder (LSTM-Autoencoder) under a multi-machine-based task formulation. By constructing meta-tasks from time-series datasets collected on multiple five-axis computer numerical control (CNC) machines, our method enables rapid adaptation to unseen machines and production scenarios with only a few training examples. The experimental results demonstrate that, even under data-scarce conditions, the proposed model achieves an accuracy of 98.02% and an F1-score of 94.74%, representing improvements of 4.2 percentage points in accuracy and 16.9 percentage points in F1-score over conventional transfer learning approaches. Furthermore, in cross-validation on entirely new machine data, our framework outperforms existing models by 18.1% in accuracy, evidencing superior generalization capability. These findings suggest that the proposed multi-machine-based Model-Agnostic Meta-Learning Long Short-Term Memory Autoencoder (MAML LSTM-Autoencoder) can significantly enhance operational efficiency and reduce maintenance costs in retrofitted manufacturing equipment, thereby improving overall productivity and paving the way for real-time industrial deployment.