Institute of Vibration and Noise, Naval University of Engineering, Wuhan 430033, China.
Naval Key Laboratory of Ship Vibration and Noise, Naval University of Engineering, Wuhan 430033, China.
Sensors (Basel). 2023 Feb 21;23(5):2395. doi: 10.3390/s23052395.
The internal gear pump is simple in structure, small in size and light in weight. It is an important basic component that supports the development of hydraulic system with low noise. However, its working environment is harsh and complex, and there are hidden risks related to reliability and exposure of acoustic characteristics over the long term. In order to meet the requirements of reliability and low noise, it is very necessary to make models with strong theoretical value and practical significant to accurately monitor health and predict the remaininglife of the internal gear pump. This paper proposed a multi-channel internal gear pump health status management model based on Robust-ResNet. Robust-ResNet is an optimized ResNet model based on a step factor h in the Eulerian approach to enhance the robustness of the ResNet model. This model was a two-stage deep learning model that classified the current health status of internal gear pumps, and also predicted the remaining useful life (RUL) of internal gear pumps. The model was tested in an internal gear pump dataset collected by the authors. The model was also proven to be useful in the rolling bearing data from Case Western Reserve University (CWRU). The accuracy results of health status classification model were 99.96% and 99.94% in the two datasets. The accuracy of RUL prediction stage in the self-collected dataset was 99.53%. The results demonstrated that the proposed model achieved the best performance compared to other deep learning models and previous studies. The proposed method was also proven to have high inference speed; it could also achieve real-time monitoring of gear health management. This paper provides an extremely effective deep learning model for internal gear pump health management with great application value.
内齿轮泵结构简单、体积小、重量轻，是支撑低噪声液压系统发展的重要基础元件。然而，其工作环境恶劣复杂，存在可靠性相关的隐藏风险和长期声学特性暴露的风险。为了满足可靠性和低噪声的要求，非常有必要建立具有较强理论价值和实际意义的模型，以准确监测健康状况并预测内齿轮泵的剩余寿命。本文提出了一种基于 Robust-ResNet 的多通道内齿轮泵健康状态管理模型。Robust-ResNet 是基于 Euler 方法中的步长因子 h 对 ResNet 模型进行优化的模型，增强了 ResNet 模型的鲁棒性。该模型是一个两阶段深度学习模型，对内齿轮泵的当前健康状况进行分类，同时对内齿轮泵的剩余使用寿命（RUL）进行预测。该模型在作者收集的内齿轮泵数据集上进行了测试，并在凯斯西储大学（Case Western Reserve University，CWRU）的滚动轴承数据上进行了验证。在这两个数据集上，健康状况分类模型的准确率分别达到了 99.96%和 99.94%。在自行收集的数据集上，RUL 预测阶段的准确率为 99.53%。结果表明，与其他深度学习模型和以往的研究相比，所提出的模型取得了最佳的性能。所提出的方法还被证明具有较高的推理速度，可以实现齿轮健康管理的实时监测。本文为内齿轮泵健康管理提供了一种极其有效的深度学习模型，具有很大的应用价值。
