用振动数据检测工业泵故障，模型轻量可适配新泵。
ECNN: A Low-complex, Adjustable CNN for Industrial Pump Monitoring Using Vibration Data
- 设计轻量可调卷积网络，适合边缘设备部署
- 仅需少量正常数据即可适配未知泵型
- 结合阈值法提升精度，优于传统统计与经典CNN
工业泵在制造、能源和水处理等领域至关重要，其故障可能导致重大经济损失与安全风险。本文提出一种新型增强型卷积神经网络（ECNN），基于加速度传感器采集的振动数据预测工业泵故障。该模型注重低复杂度设计，便于在计算资源有限的边缘设备上运行，通过详细的设计空间探索，在复杂度与准确率之间取得平衡。为适应未知泵型，算法引入仅需少量正常数据即可确定的泵特定参数。最后，将ECNN与阈值方法结合，进一步提升性能并满足实际应用需求。实验表明，该联合方法在准确性上显著优于传统统计方法和经典CNN。本工作提供了一种低复杂度、可调、基于CNN的异常检测方案，结合经典方法实现高精度的工业泵故障预警。
原文摘要 · Abstract (English)
Industrial pumps are essential components in various sectors, such as manufacturing, energy production, and water treatment, where their failures can cause significant financial and safety risks. Anomaly detection can be used to reduce those risks and increase reliability. In this work, we propose a novel enhanced convolutional neural network (ECNN) to predict the failure of an industrial pump based on the vibration data captured by an acceleration sensor. The convolutional neural network (CNN) is designed with a focus on low complexity to enable its implementation on edge devices with limited computational resources. Therefore, a detailed design space exploration is performed to find a topology satisfying the trade-off between complexity and accuracy. Moreover, to allow for adaptation to unknown pumps, our algorithm features a pump-specific parameter that can be determined by a small set of normal data samples. Finally, we combine the ECNN with a threshold approach to further increase the performance and satisfy the application requirements. As a result, our combined approach significantly outperforms a traditional statistical approach and a classical CNN in terms of accuracy. To summarize, this work provides a novel, low-complex, CNN-based algorithm that is enhanced by classical methods to offer high accuracy for anomaly detection of industrial pumps.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。