College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumqi 830017, China.
Sensors (Basel). 2023 Apr 8;23(8):3827. doi: 10.3390/s23083827.
Gearboxes are one of the most widely used speed and power transfer elements in rotating machinery. Highly accurate compound fault diagnosis of gearboxes is of great significance for the safe and reliable operation of rotating machinery systems. However, traditional compound fault diagnosis methods treat compound faults as an independent fault mode in the diagnosis process and cannot decouple them into multiple single faults. To address this problem, this paper proposes a gearbox compound fault diagnosis method. First, a multiscale convolutional neural network (MSCNN) is used as a feature learning model, which can effectively mine the compound fault information from vibration signals. Then, an improved hybrid attention module, named the channel-space attention module (CSAM), is proposed. It is embedded into the MSCNN to assign weights to multiscale features for enhancing the feature differentiation processing ability of the MSCNN. The new neural network is named CSAM-MSCNN. Finally, a multilabel classifier is used to output single or multiple labels for recognizing single or compound faults. The effectiveness of the method was verified with two gearbox datasets. The results show that the method possesses higher accuracy and stability than other models for gearbox compound fault diagnosis.
齿轮箱是旋转机械中应用最广泛的速度和动力传递元件之一。对齿轮箱进行高度准确的复合故障诊断对于旋转机械系统的安全可靠运行具有重要意义。然而，传统的复合故障诊断方法在诊断过程中将复合故障视为独立的故障模式，无法将其分解为多个单一故障。针对这一问题，本文提出了一种齿轮箱复合故障诊断方法。首先，使用多尺度卷积神经网络（MSCNN）作为特征学习模型，能够有效地从振动信号中挖掘复合故障信息。然后，提出了一种改进的混合注意力模块，称为通道-空间注意力模块（CSAM），将其嵌入到 MSCNN 中，为多尺度特征分配权重，以增强 MSCNN 的特征区分处理能力。新的神经网络命名为 CSAM-MSCNN。最后，使用多标签分类器输出单个或多个标签，以识别单个或复合故障。使用两个齿轮箱数据集验证了该方法的有效性。结果表明，该方法在齿轮箱复合故障诊断方面的准确性和稳定性均高于其他模型。
