Detecting Production Phases Based on Sensor Values using 1D-CNNs
This work addresses predictive maintenance in Industry 4.0 by enabling phase detection from sensor data, but it appears incremental as it applies an existing method to a new dataset.
The paper tackled the problem of identifying production phases in a tempering furnace for metal heat treating using sensor data and convolutional neural networks, achieving promising accuracy.
In the context of Industry 4.0, the knowledge extraction from sensor information plays an important role. Often, information gathered from sensor values reveals meaningful insights for production levels, such as anomalies or machine states. In our use case, we identify production phases through the inspection of sensor values with the help of convolutional neural networks. The data set stems from a tempering furnace used for metal heat treating. Our supervised learning approach unveils a promising accuracy for the chosen neural network that was used for the detection of production phases. We consider solutions like shown in this work as salient pillars in the field of predictive maintenance.