Channel State Information (CSI) analysis for Predictive Maintenance using Convolutional Neural Network (CNN)

With the onset of the fourth industrial revolution, predictive maintenance using wireless sensing technologies has been in high demand. This motivates to investigate the potential of WiFi CSI as a sensor for understanding the operation of machines. Since rotating motors are one of the fundamental elements in many complex machines, this paper focuses on the classification of CSI signals influenced by rotating motors at different speeds. As WiFi CSI technology is still not mature, we focus on data collection and study the sensitivity and reliability of data for this type of applications. We observe that CNNs are suitable to classify the speeds of motors and is also sensitive to speeds close to each other when operated in ideal network condition. However, in practical network conditions, unreliability of the data and the inability of CNN to classify it remains a challenge.