Enhanced Anomaly Detection in Manufacturing Processes Through Hybrid Deep Learning Techniques

Smart factory systems have been introduced to prevent the decline in overall equipment effectiveness caused by the presence of defects within factory manufacturing equipment. In this context, it is crucial to predict process downtime using manufacturing equipment data and take preemptive actions. However, anomaly detection models for preemptive actions have limitations in labeling anomaly alarms. Moreover, from real-time data collection to model deployment, it needs to consider the model-based service implementation for stakeholders. Our research develops a hybrid deep learning-based anomaly detection model that does not require data labeling. The advantage of this model stems from its ability to identify anomalous patterns via the reconstruction of the sequential progression inherent in the data. According to our experimental findings, the proposed hybrid model demonstrated superior efficacy compared to alternative anomaly detection algorithms. By preemptively predicting downtime within the manufacturing process, it contributes to enhanced production efficiency. Furthermore, we develop an anomaly detection system based on a real-time service framework from the data collection step to improve the activation of smart factories. This research contributes to the literature on the monitoring and management of anomaly detection in smart factories. It also has practical implications for the manufacturing industry by recommending efficiency measures for smart factories to reduce downtime in manufacturing processes and improve product quality.