发表机构
University of Patras（帕特雷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于隐马尔可夫模型的滚动轴承剩余使用寿命估计方法，利用振动信号，通过AR滤波、包络分析和PCA构建退化指标，并采用HMM建模故障演变，在训练与目标数据差异显著时仍能提供保守的RUL估计。
AI 中文摘要
本可行性研究提出了一种基于隐马尔可夫模型（HMM）的滚动轴承剩余使用寿命（RUL）估计方法，适用于训练振动信号与目标轴承振动信号存在显著差异的情况。首先，利用初始运行期间的振动加速度测量数据（此时包括所考虑轴承在内的所有部件均处于健康状态）建立自回归（AR）模型以捕捉机械动力学特性。随后，使用该AR模型对新采集的故障机械振动信号进行滤波，并对残差信号进行典型的包络分析，以识别与故障相关的重复频率。将这些频率的幅值与AR残差的统计特征通过主成分分析（PCA）进行融合，构建一个对轴承退化敏感的状态指标（CI）。基于健康状态的训练数据设定阈值，当CI超过该阈值时即宣布故障。一旦检测到故障，其演变过程被建模为一系列连续的、不同的状态，这些状态通过K-Means聚类进行识别。估计一个具有连续观测密度的左-右HMM来表示故障演变过程，从而预测RUL。该基于HMM的RUL估计方法使用两个名义上相同轴承的两次运行至失效实验的有限振动测量数据进行训练，而RUL估计性能则在同类型的第三个轴承上进行评估。尽管训练所用振动数据与目标轴承的生命周期存在显著差异，RUL估计结果表明所提出的基于HMM的方法具有足够但保守的性能。
英文摘要
This feasibility study presents a Hidden Markov Model (HMM)-based methodology for the Remaining Useful Life (RUL) estimation of rolling element bearings when the training vibration signals differ significantly from those of the target bearing. An AutoRegressive (AR) model is first employed to capture the machinery dynamics using vibration acceleration measurements from an initial operating period where all components including the considered bearing are still under healthy condition. The AR model is subsequently used to filter a newly acquired vibration signal from the faulty machinery, and typical Envelope Analysis is performed on the residual signal to identify fault-related repetition frequencies. The amplitudes of these frequencies, combined with statistical features of the AR residuals are fused through Principal Component Analysis to construct a sensitive to bearing degradation Condition Indicator (CI). Based on training data from the healthy state, a threshold is established, and a fault is declared when the CI exceeds this threshold. Once a fault is detected, its progression is modelled as a sequence of consecutive, distinct states, which are identified using K-Means clustering. A left-right HMM with continuous observation densities is estimated to represent the fault progression and thus predict the RUL. The HMM-based RUL estimation methodology is trained using vibration measurements from a limited number of two run-to-failure experiments with two nominally identical bearings, while RUL estimation performance is assessed on a third bearing of the same type. Despite the significant differences between the vibration data used for training and the life cycles of the training bearings with respect to the target bearing, the RUL estimation results indicate an adequate but conservative performance of the postulated HMM-based methodology.
Comments14 pages, 10 figures. Accepted for publication in the Proceedings of the 10th International Conference on Risk Analysis (ICRA10), Patras, Greece, 2025
