中文摘要：

人类听觉系统具有优良的非平稳信号分析能力,在听觉系统中,由耳蜗基底膜对信号进行类似于带通滤波的时频分解,并由内毛细胞、传入神经和听觉中枢的神经网络对时频分解结果逐步进行特征信息提取和压缩。鉴于此,参照Wang-Brown模型,建立一种可描述信号时频结构特征的听觉模型,该模型包括基底膜、内毛细胞、中级听觉和听觉中枢等子模型,听觉中枢模型由单层听神经振荡网络构成。略去Wang-Brown模型中随机项和侧抑制项,简化内毛细胞模型,设计听神经元的活跃准则和神经元间的联接方式。信号经基底膜、内毛细胞和中级听觉模型处理后,由听神经振荡网络进行信息综合,使得信号中时频结构相似的区域所对应的听神经元进行同步振荡,从而可利用同步振荡神经元的分布情况描述信号的时频结构。进行故障转子升降速试验和风力发电增速机稳速运行试验,试验所得信号的分析结果表明,所建模型能够有效描述信号的时频结构特征及其变化情况,对信号的瞬态变化较为敏感,且数据量相对较小,易于智能识别。

英文摘要：

The human auditory system possesses excellent capability to analysis non-stationary signal.In auditory system,before a signal is recognized by the auditory cortex,it is sequentially processed by the basilar membrane,which can be seen as a bandpass filterbank,and other elements in auditory system.Therefore,to describe the structure features of signal in time-frequency space,an auditory model is proposed based on Wang-Brown model and the auditory nerve fiber oscillatory network with single layer.This model consists of basilar membrane,inner hair cells,middle auditory stage and auditory cortex,and the auditory cortex model is a single layer auditory nerve fiber oscillatory network.According to the characteristic of mechanical vibration signal,the random term and lateral inhibitor in Wang-Brown model are ignored,and the inner hair cells model is simplified.Furthermore,the active rule of neuron and the connection mode between neurons are designed.In proposed model,the oscillatory network synthesizes the output of the preceding submodels.The oscillation of neurons corresponding to similar time-frequency structure is synchronized.Therefore the distribution of synchronized neurons is utilized to describe the time-frequency structure feature of the analyzed signal.The proposed model is evaluated by using the run-up vibration signals of a rotor with different fault and of a gear box used on wind turbine.The results show that the proposed model can effectively describe the structure features and evolvement of a signal with low data quantity,and is sensitive to the instantaneous change of the signal.Then the model is convenient to be applied in intelligent recognition.