Deep convolutional neural networks for cyclic sensor data
Payman Goodarzi Yannick Robin Andreas Schütze Tizian Schneider
Predictive maintenance plays a critical role in ensuring the uninterrupted
operation of industrial systems and mitigating the potential risks associated
with system failures. This study focuses on sensor-based condition monitoring
and explores the application of deep learning techniques using a hydraulic
system testbed dataset. Our investigation involves comparing the performance of
three models: a baseline model employing conventional methods, a single CNN
model with early sensor fusion, and a two-lane CNN model (2L-CNN) with late
sensor fusion. The baseline model achieves an impressive test error rate of 1%
by employing late sensor fusion, where feature extraction is performed
individually for each sensor. However, the CNN model encounters challenges due
to the diverse sensor characteristics, resulting in an error rate of 20.5%. To
further investigate this issue, we conduct separate training for each sensor
and observe variations in accuracy. Additionally, we evaluate the performance
of the 2L-CNN model, which demonstrates significant improvement by reducing the
error rate by 33% when considering the combination of the least and most
optimal sensors. This study underscores the importance of effectively
addressing the complexities posed by multi-sensor systems in sensor-based
condition monitoring.