Inspection of circuit waveform measurements is essential to ensure electronic product standards but is often labor-intensive, especially in large-scale manufacturing. This article presents an automated waveform anomaly detection methodology based on a deep learning driven converting and correction framework, specifically designed to enhance intelligent manufacturing by enabling real-time inspection control, reducing yield losses, and integrating with AI-driven production systems. Comparative experiments against four state-of-the-art methods, including FastFlow, PatchCore, VT-ADL, and Dinomaly, demonstrate that the proposed approach attains a 96.66% F1-score across multiple anomaly categories. A key innovation is the mutual transformation mechanism, designed to preserve signal integrity while preventing direct replication in correlated waveforms. By integrating a Long Short-Term Memory (LSTM) network with a U-Net architecture, the method simultaneously captures spatial and temporal dependencies, and an attention block further enhances focus on key waveform features. A correction model iteratively refines the transformed waveforms, further enhancing detection accuracy. Evaluations across various error types, such as trigger error (99.72%), overcurrent (99.77%), and color change (99.99%), highlight the robustness of this system. This framework offers an efficient solution for waveform anomaly detection, making it particularly well-suited for high-volume electronic product testing and quality assurance in intelligent manufacturing environments, supporting Industry 4.0/5.0 goals of automation and predictive maintenance.
Hsu et al. (2026) studied this question.