Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.

As the 4th industrial revolution emerges, the implementation of smart factories are essential in the manufacturing industry. However, 80% of small and medium-sized enterprises that have introduced smart factories remain at the basic level. In addition, in root industries such as injection molding, PLC and HMI software are used to implement functions that simply show operation data aggregated by facilities in real time. This has limitations for managers to make decisions related to product production other than viewing data. This study presents a method for upgrading the level of smart factories to suit the reality of small and medium-sized enterprises. By monitoring the data collected from the facility, it is possible to determine whether there is an abnormal situation by proposing an appropriate algorithm for meaningful decision-making, and an alarm sounds when the process is out of control. In this study, the function of HMI has been expanded to check the failure frequency rate, facility time operation rate, average time between failures, and average time between failures based on facility operation signals. For the injection molding industry, an HMI prototype including the extended function proposed in this study was implemented. This is expected to provide a foundation for SMEs that do not have sufficient IT capabilities to advance to the middle level of smart factories without making large investments.