钢筋混凝土框架近断层速度脉冲地震响应分析

针对滚动轴承的故障诊断问题，提出了一种基于栈式稀疏自编码网络(stacked sparse auto encoder，简称SSAE)、改进灰狼智能优化算法(improved grey wolf optimization，简称IGWO)以及支持向量机(support vector machine，简称SVM)的混合智能故障诊断模型。首先，利用栈式自编码网络强大的特征自提取能力，实现故障信号深层频谱特征的自适应学习，通过引入稀疏项约束提高特征学习的泛化性能；其次，利用改进的灰狼算法实现支持向量机的参数优化；最后，基于优化后的SVM完成对故障特征向量的分类识别。所提混合智能故障诊断模型充分结合了深度神经网络强大的特征自学习能力和支持向量机优秀的小样本分类性能，避免了手工特征提取的弊端，可对不同故障类型的振动信号实现更精准的识别。多组对比实验表明，相比传统方法，笔者所提出的模型具有更优秀的故障识别能力，诊断准确率可达98%以上。

Near-Fault Velocity Pulse Motions on Seismic Responses of the RC Frame

Aiming at fault diagnosis problems of rolling bearings, a hybrid intelligent diagnosis model is proposed based on stacked sparse auto encoders (SSAE), improved gray wolf optimization algorithm (IGWO) and support vector machine (SVM). Firstly, by making use of the excellent ability of SSAE in feature self-extraction, adaptive learning of deep frequency-domain features of fault signals can be realized. In addition, sparse penalty term is introduced to enhance the generalization. Secondly, the high-level feature vectors are taken as input to the SVM for classification and recognition, whose parameters are optimized by the IGWO algorithm. The proposed model fully combines the powerful feature self-learning ability of deep neural network and the excellent performance of SVM in classifications on small samples. Identification on vibration signals of different fault types can be achieved in a more reliable and accurate way, avoiding the drawbacks of manual feature extraction. Moreover, contrast experiments are conducted for validation. The results show that the model proposed in this paper has better performance in fault diagnosis accuracy compared with traditional methods, and the diagnosis accuracy can be over 98%.