A Real-Time Based Intelligent System for Predicting Equipment Status

In manufacturing industry, significant productivity losses arise due to equipment failures. Therefore, it is an important task to prevent the equipment from failure by monitoring each machine's sensor data in advance. However, most of the current developed systems have been only focused on monitoring the sensor data and have a difficulty in applying advanced algorithms to the real-time stream data. To address issues, we implemented an intelligent system that employs real-time streaming engine loaded with the machine learning libraries for predictive maintenance analysis. By applying a deep-learning based model to the real-time streaming data, we can provide not only trends of raw sensor data but also give an indicator representing an equipment's status in real-time. We anticipate that our system contributes to recognize the equipment's status by monitoring the indicator for productivity improvement in manufacturing industry in real-time.