人类的听觉系统在处理声音信号这类非平稳、非线性信号上具备优异的识别能力及抗噪能力。依据振动与声音信号同源性的特点，提出了一种基于听觉显著性信号的数据降噪、典型特征提取和自组织映射（self-organizing feature map, SOM）网络相结合进行发电机组主轴承性能评估的方法。首先，利用Gammatone滤波器组构建人的耳蜗模型对原始振动信号进行识别，并剔除噪声信号。其次，通过模拟人耳的听觉注意机制获得显著帧和显著通道的典型特征构建特征空间。最后，将构建的特征空间分为训练样本和测试样本两部分，利用SOM网络实现发电机组主轴承的性能评估。试验结果表明，所提出的性能评估方法能够精准地识别噪声信号并构建特征空间，可有效评估发电机组主轴承的性能，为其视情维修提供依据。

Human auditory system has excellent recognition ability and anti-noise ability in processing non-stationary and nonlinear signals such as sound signals. Based on the homology of vibration and sound signals, a method based on auditory saliency signal data denoising, typical feature extraction and self-organizing feature map (SOM) network was proposed to evaluate the performance of generator set main bearing. Firstly, the human cochlea model was constructed by using a Gammatone filter bank to identify the original vibration signal and eliminate the noise signal. Secondly, the typical features of salient frames and salient channels were obtained by simulating the auditory attention mechanism of human ears, and then the feature space was constructed. Finally, the constructed feature space was divided into training samples and test samples, and the SOM network was used to realize the performance evaluation of the generator set main bearing. The experimental results show that the performance evaluation method proposed can accurately identify the noise signal and construct the feature space, which can effectively evaluate the performance of the generator set main bearing and provide a basis for its condition-based maintenance.