Remaining Useful Life Prediction Based on Forward Intensity

Remaining useful life (RUL) prediction is an essential tool for enhancing the reliability and resilience of systems. Most existing RUL prediction methods consider degradation-induced failures, and assume there exists a fixed failure threshold for degradation signals. In many real applications, however, it is difficult or even impossible to find such failure thresholds, making the RUL prediction a challenging task. This study addresses the challenge by exploiting the method of forward intensity, a novel tool introduced in econometrics. In particular, the historical degradation signals are treated as time-varying covariates for the forward intensity. The dynamics of unobserved future degradation signals are captured by a time-varying regression parameter in the forward intensity function, which are usually estimated using the maximum pseudolikelihood method in existing studies. To improve prediction accuracy, this study proposes a new estimation method based on smoothing splines. The proposed method not only allows RUL prediction in the absence of a fixed failure threshold, but also achieves satisfactory prediction accuracy with reasonable interpretability of covariate effects. Prediction intervals for the RUL are also developed. The effectiveness of the proposed method is validated through Monte Carlo simulations and three real-data examples. The results show that the proposed method outperforms existing methods in terms of prediction accuracy.

剩余使用寿命（Remaining Useful Life, RUL）预测是提升系统可靠性与韧性的核心工具。现有绝大多数RUL预测方法均围绕退化引发的故障展开，并假设退化信号存在固定失效阈值。然而在诸多实际应用场景中，获取此类失效阈值往往困难重重，甚至完全无法实现，这使得RUL预测成为一项极具挑战性的任务。本研究借助计量经济学领域提出的前向强度（forward intensity）方法来应对这一挑战。具体而言，研究将历史退化信号视作前向强度模型的时变协变量。前向强度函数中的时变回归参数可捕捉未观测到的未来退化信号的动态变化，现有研究通常采用伪极大似然法对该参数进行估计。为提升预测精度，本研究提出了一种基于平滑样条（smoothing splines）的新型参数估计方法。所提方法不仅可在无固定失效阈值的场景下实现RUL预测，还能在保证协变量效应具备合理可解释性的同时，获得令人满意的预测精度。本研究同时构建了RUL的预测区间。通过蒙特卡洛（Monte Carlo）模拟与三个真实数据集案例，本研究验证了所提方法的有效性。实验结果表明，所提方法在预测精度方面优于现有同类方法。

搜集汇总

背景与挑战

背景概述

该数据集提出了一种基于前向强度和平滑样条的剩余使用寿命预测新方法，无需固定故障阈值即可实现高精度预测，并通过模拟和实际案例验证了方法的优越性。