| dc.description.abstract | In recent years, with the progress of the industrial level, under the revolution of Industry 4.0, the traditional manufacturing and production methods have gradually transformed into smart factory. Therefore, companies from various countries have introduced smart factory technologies such as Industrial Internet of Things (IIOT), Big Data Analysis, Sensor and Artificial Intelligence (AI). Use the technology of equipment integration AI to achieve accurate, fast, time-saving and labor-saving goals, in order to stand out in the fierce competition.
One of the keys to achieving smart factory is to reduce the risks of equipment downtime and unplanned maintenance. The equipment collects data through sensors, and at the same time uses predictive maintenance technology to achieve early prediction of abnormal conditions or shutdowns, thereby improving the equipment efficiency of the overall production line.
This study takes the coating machine of company A as an example, and uses the data collected by its sensors as the source of data. The data includes variables such as tension, torque, speed and current. Since the data is time series data, this study uses Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods for anomaly detection, and by using different unsaturated excitation functions to model and analyze. The experimental results show that the 24 models built in this research all have extremely high accuracy, and the recall rate reaches 100%. The LSTM model with the following hyperparameter settings is the best: the activation function is Leaky ReLU, the number of hidden layers is 2, the number of neurons is 128. The accuracy rate of this model is 99.77%, the specificity is 99.76%, and the F1-Score is 82.86%. Compared with the actual tension abnormal record, the model can effectively predict the abnormal condition of the machine 12 seconds before the abnormality occurs, which helps to reduce unplanned abnormalities or shutdowns of equipment, thereby reducing the cost of the overall production line. | en_US |