Remaining useful life (RUL) prediction is an advanced technique for system maintenance scheduling. Most of existing RUL prediction methods are only interested in the precision of RUL estimation; the adverse impact of over-estimated RUL on maintenance scheduling is not of concern. In this work, an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level. The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends. Then, the latent structure between the degradation features and the RUL labels is modeled by a support vector regression (SVR) model and a long short-term memory (LSTM) network, respectively. To enhance the prediction robustness and increase its marginal utility, the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters. By designing a cost function with penalty mechanism, the three parameters are determined using a modified grey wolf optimization algorithm. In addition, a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method. Verification is done using an aero-engine data set from NASA. The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

中文翻译：

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预测维护的规避风险的剩余使用寿命估计

剩余使用寿命（RUL）预测是一项用于系统维护计划的高级技术。现有的大多数RUL预测方法仅对RUL估计的精度感兴趣。高估RUL对维护计划的不利影响是无关紧要的。在这项工作中，开发了一种具有风险规避适应性的RUL估计方法，该方法可以在保持合理的低估水平的同时减少过高的估计率。所提出的方法包括退化特征选择模块，以获得反映系统退化趋势的关键特征。然后，分别通过支持向量回归（SVR）模型和长短期记忆（LSTM）网络对退化特征和RUL标签之间的潜在结构进行建模。为了增强预测的鲁棒性并增加其边际效用，SVR模型和LSTM模型集成在一起，通过三个连接参数生成混合模型。通过设计具有惩罚机制的成本函数，使用改进的灰太狼优化算法确定三个参数。另外，提出了一种成本度量来测量这种规避风险的预测性维护方法的收益。验证是使用来自NASA的航空发动机数据集完成的。结果表明了所提出的RUL估计方法和预测性维护策略的可行性和有效性。提出了一种成本度量来衡量这种规避风险的预测性维护方法的收益。验证是使用来自NASA的航空发动机数据集完成的。结果表明了所提出的RUL估计方法和预测性维护策略的可行性和有效性。提出了一种成本度量来衡量这种规避风险的预测性维护方法的收益。验证是使用来自NASA的航空发动机数据集完成的。结果表明了所提出的RUL估计方法和预测性维护策略的可行性和有效性。