Deep convolutional neural networks for cyclic sensor data

TL;DR

This paper compares traditional and deep learning models for sensor-based condition monitoring in industrial systems, highlighting the challenges and improvements in multi-sensor data fusion using CNN architectures.

Contribution

It introduces and evaluates a two-lane CNN model with late sensor fusion, demonstrating its effectiveness in handling diverse sensor data for predictive maintenance.

Findings

Baseline model achieves 1% error with late sensor fusion.

Single CNN model has 20.5% error due to sensor diversity.

Two-lane CNN reduces error by 33% with optimal sensor combination.

Abstract

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.