With the popularity of Supervisory Information System (SIS), Supervisory Control and Data Acquisition (SCADA) system and Internet of Things (IoT) sensors, can we easily obtain abundant sensor data in manufacturing? If we can analyze the possible anomaly casualties among the IoT sensor data, we could save manufacturing maintenance costs and prevent further damages. This project applies different data science approaches on anomaly detection/prediction and causality analysis. We implemented the project in Jupyter Notebooks by leveraging the ability of Python’s VAR libraries.
Built With
- jupyter
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