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.

翻译：预测性维护在确保工业系统不间断运行并减轻系统故障相关潜在风险中发挥着关键作用。本研究聚焦于基于传感器的状态监测，并利用液压系统测试平台数据集探索了深度学习技术的应用。我们的研究涉及比较三种模型的性能：采用传统方法的基线模型、采用早期传感器融合的单流CNN模型，以及采用后期传感器融合的双流CNN模型（2L-CNN）。基线模型通过后期传感器融合，即对每个传感器单独进行特征提取，实现了令人瞩目的1%测试错误率。然而，由于传感器特性各异，CNN模型面临挑战，错误率达到了20.5%。为深入探究此问题，我们对每个传感器进行了单独训练，并观察到准确率存在差异。此外，我们还评估了2L-CNN模型的性能，该模型在考虑最差与最优传感器组合时，将错误率降低了33%，展现了显著改进。本研究强调了在基于传感器的状态监测中有效应对多传感器系统复杂性的重要性。