Department of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China.
Heavy-Duty Intelligent Manufacturing Equipment Innovation Center of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
Sensors (Basel). 2023 Jun 26;23(13):5931. doi: 10.3390/s23135931.
This study proposes a remaining useful life (RUL) prediction method using limited degradation data with an unknown degradation model for hydraulic pumps with long service lives and no failure data in turbine control systems. The volumetric efficiency is calculated based on real-time monitoring signal data, and it is used as the degradation indicator. The optimal degradation curve is established using the degradation trajectory model, and the optimal probability distribution model is selected via the K-S test. The above process was repeated to optimize the degradation model and update parameters in different performance degradation stages of the hydraulic pump, providing quantification of the prediction uncertainty and enabling accurate online prediction of the hydraulic pump's RUL. Finally, an RUL test bench for hydraulic pumps is built for verification. The results show that the proposed method is convenient, efficient, and has low model complexity. The method enables online accurate prediction of the RUL of hydraulic pumps using only limited degradation data, with a prediction accuracy of over 85%, which meets practical application requirements.
本研究提出了一种剩余使用寿命（RUL）预测方法，用于在涡轮控制系统中具有长使用寿命和无故障数据的液压泵中，使用有限的退化数据和未知退化模型。基于实时监测信号数据计算容积效率，并将其用作退化指标。使用退化轨迹模型建立最优退化曲线，并通过 K-S 检验选择最优概率分布模型。在液压泵的不同性能退化阶段重复上述过程，以优化退化模型并更新参数，从而量化预测不确定性，并实现液压泵 RUL 的准确在线预测。最后，为了验证，建立了一个液压泵 RUL 测试台。结果表明，所提出的方法方便、高效，模型复杂度低。该方法仅使用有限的退化数据即可在线准确预测液压泵的 RUL，预测精度超过 85%，满足实际应用要求。
