| dc.description.abstract | With the vigorous development of Industry 4.0, the manufacturing industry is actively adopting modern technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, Cloud Computing, robotics, and automation to transform factories into smart manufacturing facilities. By integrating automation equipment and combining it with AI, smart factories are established to collect real-time data through sensors installed on product components and equipment, predict the likelihood and timing of equipment failures, achieve predictive maintenance. In traditional preventive maintenance models, factories typically perform equipment maintenance based on fixed time intervals or usage time, which has many disadvantages such as high maintenance costs, resource waste, and the inability to adjust in real time. Adopting predictive maintenance can avoid excessive downtime and detect anomalies for maintenance in advance, thereby improving the stability of equipment operation. The key to smart manufacturing is to achieve high-quality products, and reliable equipment, which will be important factors for success in the global market for the manufacturing industry.
This study focuses on the data collected from the sensors of a coating process company, aiming at prognostics and health management (PHM) for equipment anomaly detection. A predictive anomaly detection classification model is established using Long Short-Term Memory (LSTM) AutoEncoder. The results show that the model achieves important evaluation indicators such as accuracy rate of 99.96%, recall rate of 100%, and F2-Score of 96.7%, demonstrating excellent overall performance. Additionally, the Health Index calculation serves as a machine health management mechanism. Furthermore, a predictive model is established using 1D Convolutional Neural Network (1D CNN) combined with LSTM. By analyzing historical data to identify signs of anomalies and using different hyperparameters and sliding windows for performance comparison, the optimal model is evaluated. This study uses 15 seconds of time series data to predict the main speed value at the 20th second in the future. The final results show that the equipment can detect an abnormal speed drop 14 seconds in advance, with the model′s performance achieving R2of 96% and MSE of 2.2. Based on these results, it can be used to assess equipment health, schedule maintenance in advance, boost production efficiency, and optimize overall operations, achieving predictive maintenance goals. | en_US |