基于潜在成分的时变系统损伤的概率神经网络分类

提出了一种新的时变系统健康监控的损伤分类方法。将函数级数时变自回归平滑时序模型应用于时变系统的振动信号，以估计TAR／TMA参数和革新方差。这些参数是时间的函数，将它们在以特定的基函数构成的某种函数子空间上展开得到相应的投影系数组。所估计得到的TAR／TMA参数和革新方差可进一步用来计算潜在成分（LCs），将LCs用于健康评估比原来的参数更具信息。并将LCs联合并归化为数值得到特征集，输入概率神经网络（PNN）进行损伤分类。为了评价该方法，对一个时变系统进行了仿真，以各种不同的质量和刚度减少来模拟不同的损伤类别。算例表明：该方法能够在时变系统的背景下对损伤进行归类。

DAMAGE CLASSIFICATION BY PROBABILISTIC NEURAL NETWORKS BASED ON LATENT COMPONENTS FOR TIME-VARYING SYSTEM

A new approach to damage classification for health monitoring of a time-varylng system is presented.The functional-series time-dependent auto regressive moving average (FS-TARMA) time series model is applied to the vibration signal observed in the time-varying system for estimating the TAR/TMA parameters and the in-novation variance.These parameters are the functions of the time, represented by a group of projection coeffi-cients on the certain functional subspace with specific basis functions.The estimated TAR/TMA parameters and the innovation variance are further used to calculate the latent components (LCs) as the more informative data for health monitoring evaluation, based on an eigenvalue decomposition technique.LCs are then combined and re-duced to numerical values (NVs) as feature sets, which are input to a probabilistic neural network (PNN) for the damage classification.For the evaluation of the proposed method, numerical simulations of the damage classifica-tion for a time-varying system are used, in which different classes of damage are modeled by the mass or stiffness reductions.It is demonstrated that the method can identify the damages in the course of operation and the change of parameters on the time-varying background of the system.