Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction  
[PERSON]  
1 College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University,  
Hsinchu City 300, Taiwan; z  
methods. These methods often lack practical considerations and system integration, ignoring factors such as multi-machine or multi-production line deployment, model retraining, optimization, and systematic integration. Consequently, their applicability in real factory environments is hindered. Many studies have been conducted in the field of PHM. [PERSON] et al. proposed that machine-learning techniques could be used to predict wafer defects with a random forest classifier, achieving an accuracy of over 93.62% [14]. This predictive maintenance approach enhanced the semiconductor manufacturing productivity. [PERSON] et al. introduced synthetic data generation techniques that combined two missing value imputation methods and feature selection techniques [15]. This approach achieved an accuracy ranging from 99.5% to 100% when paired with the proposed machine-learning (ML) methods. [PERSON] et al. introduced a novel deep AE (DAE) method that fused discriminative information with a gradient descent optimization approach [16]. This technique enhanced the numerical stability of the model in cases with limited training data. [PERSON] et al. presented a series of highly compact deep convolutional AE network architectures that reduced the model size while maintaining a detection accuracy comparable to that of structures with over four million parameters [17]. [PERSON] et al. proposed an ML method for modeling and detecting anomalies during the operation of rotating machinery. This ML approach learned and generalized based on the fault severity to generate threshold values for anomaly detection [18]. A DAE model specifically designed for factory scenarios involving chillers was introduced that effectively distinguished between normal and abnormal vibration signals based on reconstruction differences [19]. Additionally, meta-learning was employed to improve the accuracy of the new sensor models with limited vibration data. For the new sensor model with fewer vibration data, the accuracy increased by about 33.50%. However, this method is mainly oriented to anomaly detection; it has not yet considered model retraining, anomaly prediction, lightweight models, edge computing, and integration with a complete intelligent management system. Considering the aforementioned issues, this study proposes a Pruning Quantized Unsupervised Meta-learning DegradingNet System (PQUM-DNS) to address the real-world conditions of practical factories. This approach integrates five key features based on the actual needs of factories as illustrated in Figure 1.  
1. Intelligent Equipment Management System (Sections 3.1 and 4.1)  
This system includes automated methods for vibration signal sensing, data transmission, data preprocessing, model training and retraining, anomaly detection, and prediction. Visual results are presented through dashboards and alert notifications are sent to onsite personnel and managers to facilitate timely problem solutions. 1. Meta-learning for Rapid Training of Anomaly Detection and Prediction Models across Multiple Machines (Sections 3.2 and 4.2)  
Figure 1: Overview of the proposed Intelligent Equipment Management System with related techniques highlighted in this paper. This approach rapidly establishes models for new machines or production lines with limited data by leveraging meta-learning and unsupervised learning through AEs, thereby achieving anomaly detection and prediction objectives. Meta-learning Adaptive Model Retraining (Sections 3.3 and 4.3)  
Machine-specific models are adaptively retrained by employing meta-learning, quickly adjusting to the slow-changing characteristics of the machine over time and enabling long-term anomaly detection and prediction.