Currently, various components of smart factories are actively applied in the manufacturing industry. In this trend, many excellent real-time cases of applying various process data to big data analysis models are being published. However, big data analysis models are having difficulty in popularizing due to constraints such as a huge amount of resource allocation, relatively long learning time, and expert fine-tuning. In this study, we propose a Functional Data Explorer that can analyze trace data of various processes and equipment. The technique can model the collected data based on basis functions and returns functional values as results. The analysis was performed based on a semiconductor chamber sensor, and it was confirmed that it is more effective in anomaly detection based on basic statistics such as average and deviation. It is expected that the convenience of real-time data analysis will be improved through the analysis technique of this study.
