基于深度学习的故障检测方法

针对传统故障诊断方法中特征提取技术难度大、故障样本获取困难等问题，在深度学习计算框架下提出了一种半监督训练的故障检测方法，利用深度信念网络中的受限波茨曼机堆栈结构实现了数据高层特征的自动提取，结合支持向量数据描述方法实现了异常数据检测，只需利用正常工况的数据样本进行网络训练和模型拟合，无需故障样本数据，也无需人工干预进行信号特征提取，即能实现对故障数据进行的实时检测和判别。经采用标准轴承实验数据的三组故障数据进行验证，故障识别率达到100%，具有很强的工程应用价值。  

基于退化数据和DBN算法的IGBT健康参数预测方法

绝缘栅双极型晶体管(IGBT)等电子元器件被广泛用于运输和能源部门,其健康状态对于设备安全和有效至关重要；在对IGBT的结构和损伤机制分析基础上,结合NASA艾姆斯中心开展的IGBT加速退化试验,选择集电极-发射极关断峰值电压作为失效特征参数,提出了一种基于深度信念网络的预测模型对其进行分析和预测；以Levenberg-Marquardt(LM)算法模型作为对比,实验结果显示文章提出的三隐藏层DBN模型相比于LM模型有更好的预测性能和更高的预测精度。  

Sound quality prediction of vehicle interior noise using deep belief networks

The sound quality of vehicle interior noise strongly influences passengers’ psychological and physiological perceptions. To predict the sound quality of interior noise, a vehicle road test with four compact cars has been conducted. All recorded interior noise signals have been denoised via a discrete wavelet transform (DWT) denoising procedure and subsequently evaluated subjectively through the anchor semantic differential (ASD) test by a jury. In addition, a novel prediction method, namely, regression-based deep belief networks (DBNs), which substitute the support vector regression (SVR) layer for the linear softmax classification layer at the top of the general DBN’s structure, has been proposed to predict the interior sound quality. The parameter selection of the DBN model has been compared and studied using a grid search. In addition, four conventional machine-learning-based methods have been introduced to enable a comparison of the performance with the newly developed DBNs. Furthermore, the feature fusion ability of DBNs has been studied by varying the amount of information that the dataset offers. The results show the following: (1) The accuracy and robustness of the proposed DBN-based sound quality prediction approach are better than those of the 4 other referenced methods. (2) The multiple-feature fusing process can strongly affect the prediction performance. (3) Finally, the unsupervised pre-training process of the DBNs can enhance the information fusing ability. Finally, the newly proposed regression-based DBN approach may be extended to address other vehicle noises in the future.