复合故障退化情形下基于自适应非线性状态估计的装备状态监测健康指标

doi:10.3901/JME.2025.03.119

摘要/Abstract

摘要： 现代机械装备长期服役于恶劣多变的工况下，难免故障类型多连级发生进而形成复合故障情形的退化过程，传统健康指标通常聚焦于单一故障退化情形下的状态监测，并不适用于该情形下的装备状态监测。针对以上问题，提出了一种复合故障退化情形下基于自适应非线性状态估计(Adaptive nonlinear state estimation，ANSE)的装备状态监测健康指标(Health indicator，HI)构建方法，包含初期故障瞬态检测和早期故障精准定位两部分。首先，基于希尔伯特奇异值分解算法(Hilbert singular value decomposition，Hilbert-SVD)构建奇异值特征序列作为状态监测模型的输入；然后，利用非线性状态估计(Nonlinear state estimation，NSE)重构误差特性构建HI以放大初始故障样本和正常样本之间的差异程度；其次，为了自适应瞬态检测初期故障，引入峰值超阈值(Peaks over threshold，POT)算法构建ANSE模型实现健康阈值动态更新；最后，对初始故障样本进行共振稀疏分解和最大相关峭度反卷积(Resonance-based sparse signal decomposition and maximum correlation kurtosis deconvolution，RSSD-MCKD)的特征提取，利用功率谱解析故障特征频率实现故障部位精准定位。将所提方法在两个复合故障退化情形下的轴承数据集进行有效性和鲁棒性验证，在风机主轴承数据集上进行工程验证，分析试验结果和对比结果，所提方法能更好检测装备的初期故障，同时还能表征其整个退化过程，表明了所提方法具有较强的状态监测能力。

关键词: 状态监测, 健康指标, 非线性状态估计, 自适应健康阈值, 故障特征频率

Abstract: Modern machinery and equipment have been in service for a long time under harsh and changeable working conditions and it is inevitable that multiple fault types will occur, resulting in a degradation process of compound fault conditions. Traditional health indicators usually focus on the condition monitoring of a single fault degradation situation and are not suitable for equipment condition monitoring in this situation. To solve the above problems, a new health indicator construction method based on adaptive nonlinear state estimation (ANSE) for equipment condition monitoring under coupled fault degradation is proposed, which includes two parts: initial fault transient detection and early fault accurate location. First, a singular value feature sequence is constructed based on Hilbert singular value decomposition (Hilbert-SVD) as input to the state monitoring model. Then, the nonlinear state estimation (NSE) reconstruction error characteristic is used to construct the HI to amplify the difference between the initial fault sample and the normal sample. After that, in order to self-adapt transient detection of initial faults, ANSE model is constructed by introducing peak overthreshold (POT) algorithm to realize dynamic update of health threshold. Finally, resonance based sparse signal decomposition and maximum correlation kurtosis deconvolution (RSSD-MCKD) feature extraction, using power spectrum analysis of fault characteristic frequency to achieve accurate fault location. The effectiveness and robustness of the proposed method are verified in the bearing data set of two coupled fault degradation cases and the engineering verification is carried out in one fan main bearing data set. By analyzing the test results and comparing the results, the proposed method can better detect the initial faults of the equipment and also characterize the entire degradation process, indicating that the proposed method has strong condition monitoring ability.

Key words: condition monitoring, health indicator, nonlinear state estimation, dynamic health threshold, fault characteristic frequency

中图分类号:

TH17
TN911

裴雪武, 李新宇, 高亮, 高艺平, 陈志敏. 复合故障退化情形下基于自适应非线性状态估计的装备状态监测健康指标[J]. 机械工程学报, 2025, 61(3): 119-129.

PEI Xuewu, LI Xinyu, GAO Liang, GAO Yiping, CHEN Zhimin. Health Indicator of Equipment Condition Monitoring Based on Adaptive Nonlinear State Estimation Under Compound Fault Degradation[J]. Journal of Mechanical Engineering, 2025, 61(3): 119-129.

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