【摘要】 针对齿轮在复杂运行工况下故障特征提取困难,传统故障诊断方法的识别精度易受人工提取特征的影响,以及单传感器获取信息不全面等问题,提出基于深度置信网络(DBN)与信息融合的齿轮故障诊断方法。通过多传感器信息融合技术对每个传感器采集的振动信号进行数据层融合;利用DBN进行自适应特征提取从而实现故障分类。为了避免因人为选择DBN结构参数,导致模型识别精度下降的问题,利用改进的混合蛙跳算法(ISFLA)对DBN结构参数进行优化。试验表明,与BP神经网络、未经优化的DBN以及单传感器故障诊断相比,该研究提出的信息融合及优化方法具有更高的故障识别精度。更多还原

【Abstract】 It is difficult to extract the fault features of gears under complex operation conditions. The traditional fault diagnosis and recognition accuracy are easily affected by the manual feature extraction, and the information obtained by a single sensor is not comprehensive. To solve the above problems, a gear fault diagnosis method based on deep belief networks(DBN) and information fusion was proposed in this paper. Firstly, the vibration signals collected by each sensor were fused by multi-sensor information fusion technology at the data layer, and then DBN was used for adaptive feature extraction to achieve fault classification. In order to avoid the problem of model recognition accuracy degradation caused by artificial selection of DBN structural parameters, an improved shuffled frog leaping algorithm(ISFLA) was proposed to optimize DBN structural parameters. Experiments show that the information fusion and optimization methods proposed in this paper have higher fault recognition accuracy than BP neural network, DBN, and single sensor fault diagnosis.更多还原