National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel). 2019 Nov 28;19(23):5222. doi: 10.3390/s19235222.
Due to the existence of multiple rotating parts in the planetary gearbox-such as the sun gear, planet gears, planet carriers, and its unique planetary motion, etc.-the vibration signals generated under multiple fault conditions are time-varying and nonstable, thus making fault diagnosis difficult. In order to solve the problem of planetary gearbox composite fault diagnosis, an improved particle swarm optimization variational mode decomposition (IPVMD) and improved convolutional neural network (I-CNN) are proposed. The method takes as input the spectrum of the original vibration signal that contains rich information. First, the automatic feature extraction of signal spectrum is performed by I-CNN, while a classifier is used to diagnose the fault modes. Second, the composite fault signal is decomposed into multiple single fault signals by adaptive variational mode, and the signal is decomposed as a model input to diagnose the single fault component. Finally, a complete intelligent diagnosis of planetary gearboxes is conducted. Through experimental verification, the composite fault diagnosis method combining IPVMD and I-CNN will diagnose the composite fault and effectively diagnose the sub-fault included in the composite fault.
由于行星齿轮箱中存在多个旋转部件，如太阳齿轮、行星齿轮、行星架，以及其独特的行星运动等，因此在多种故障条件下产生的振动信号是时变和不稳定的，从而使得故障诊断变得困难。为了解决行星齿轮箱复合故障诊断问题，提出了一种改进的粒子群优化变分模态分解（IPVMD）和改进的卷积神经网络（I-CNN）方法。该方法以包含丰富信息的原始振动信号的频谱作为输入。首先，通过 I-CNN 对信号频谱进行自动特征提取，同时使用分类器对故障模式进行诊断。其次，自适应变分模式将复合故障信号分解为多个单一故障信号，将信号分解为模型输入，以诊断单一故障分量。最后，对行星齿轮箱进行完整的智能诊断。通过实验验证，结合 IPVMD 和 I-CNN 的复合故障诊断方法能够诊断复合故障，并有效地诊断复合故障中包含的子故障。
