Power Signal Classification with Combinational Spectrogram-based CNN for Embedded System Health Management

This paper addresses the problem of the embedded system health management for high-speed flight systems. Especially, we focus the variation of power signals used in embedded systems because the electrical degeneration is strongly related to the power levels and frequencies. If the power signals can be classified into normal status and abnormal status, the sudden electrical degeneration of embedded systems can be successfully detected. The conventional threshold-based classification which has been used in aerospace and defense fields cannot find out the hidden anomaly within the thresholds. This paper proposes an accurate power signal classification method using combinational spectrogram-based convolutional neural networks (CNN). The power signals are combined with eigenvalues and converted to spectrogram which can analyze them on time and frequency domain simultaneously. Then, the CNN for power signal classification is trained and validated using the combinational spectrograms. Inference results showed that the proposed method can accurately classify the power signals into normal status and abnormal status.