Remaining useful life (RUL) prediction has attracted more and more attention in recent years because of its significance in predictive maintenance. The degradation processes of systems from the same population are generally different from one another due to their various operational conditions and health states. This behavior is defined as unit-to-unit variability (UtUV), which brings difficulty to RUL prediction. To handle this problem, this paper develops a Wiener-process-model (WPM)-based method for RUL prediction with the consideration of the UtUV. In this method, an age- and state-dependent WPM is specially designed to describe the various degradation processes of different units. A unit maximum likelihood estimation (UMLE) algorithm is proposed to estimate the UtUV parameter according to the measurements of training units, without any restriction to the distribution pattern of the parameter. The UtUV parameter is further updated via particle filtering (PF) according to the measurements of the testing unit. In the particle updating process, a fuzzy resampling algorithm is developed to handle the sample impoverishment problem of PF. With the updated parameter, the RUL is predicted through a degradation process simulation algorithm. The effectiveness of the proposed method is verified through a simulation study and a turbofan engine degradation dataset.

中文翻译：

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考虑单元间变异性的基于维纳过程模型的剩余使用寿命预测方法


近年来，剩余使用寿命（RUL）预测因其在预测性维护中的重要性而受到越来越多的关注。由于不同的运行条件和健康状态，来自同一群体的系统的退化过程通常彼此不同。这种行为被定义为单元间变异性（UtUV），这给 RUL 预测带来了困难。为了解决这个问题，本文开发了一种基于维纳过程模型（WPM）的 RUL 预测方法，并考虑了 UtUV。在该方法中，专门设计了与年龄和状态相关的WPM来描述不同单元的各种退化过程。提出了一种单位最大似然估计（UMLE）算法，根据训练单元的测量来估计UtUV参数，而对参数的分布模式没有任何限制。根据测试单元的测量结果，通过粒子过滤（PF）进一步更新 UtUV 参数。在粒子更新过程中，提出了模糊重采样算法来处理PF的样本匮乏问题。使用更新的参数，通过退化过程模拟算法预测 RUL。通过仿真研究和涡扇发动机退化数据集验证了所提出方法的有效性。