摘要:针对特定机械设备构建数据驱动的故障诊断模型缺乏泛化能力，而轴承作为各型机械的共有核心部件，对其健康状态的判定对不同机械的衍生故障分析具有普适性意义.提出了一种基于1D-CNN（one-dimensional convolution neural network）联合特征提取的轴承健康监测与故障诊断算法.算法首先对轴承原始振动信号进行分区裁剪，裁剪获得的信号分区作为特征学习空间并行输入1D-CNN中，以提取各工况下的代表性特征域.为了避免对故障重叠信息的处理，优先使用对健康状态敏感的特征域构建轴承健康状态判别模型，若健康状态判别模型识别轴承未处于健康状态，特征域将与原始信号联合重构，通过耦合自动编码器开展故障模式判定.使用凯斯西储大学（Case Western Reserve University）的轴承数据开展实验，结果表明，该算法继承了深层学习模型的准确性和鲁棒性，具有较高的故障诊断精度和较低的诊断时延.



Abstract:Data-driven fault diagnosis models for specific mechanical equipment lack generalization capabilities. As a core component of various types of machinery, the health status of bearings makes sense in analyzing derivative failures of different machinery. This study proposes a bearing health monitoring and fault diagnosis algorithm based on 1D-CNN (one-dimensional convolution neural network) joint feature extraction. The algorithm first partitions the original vibration signal of the bearing in segmentations. The signal segmentations are used as feature learning spaces and input into the 1D-CNN in parallel to extract the representative feature domain under each working condition. To avoid processing overlapping information generated by faults, a bearing health status discriminant model is built in advance based on the feature domain sensitive to health status. If the health model recognizes that the bearing is not in a healthy state, the feature domain will be reconstructed jointly with the original signal and coupled with an automatic encoder for failure mode classification. Bearing data provided by Case Western Reserve University are used to carry out experiments. Experimental results demonstrate that the proposed algorithm inherits the accuracy and robustness of the deep learning model, and has higher diagnosis accuracy and lower time delay.



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