State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.
National Engineering Laboratory for System Safety and Operation Assurance of Urban Rail Transit, Guangzhou 510000, China.
Sensors (Basel). 2018 May 24;18(6):1705. doi: 10.3390/s18061705.
Bogies are crucial for the safe operation of rail transit systems and usually work under uncertain and variable operating conditions. However, the diagnosis of bogie faults under variable conditions has barely been discussed until now. Thus, it is valuable to develop effective methods to deal with variable conditions. Besides, considering that the normal data for training are much more than the faulty data in practice, there is another problem in that only a small amount of data is available that includes faults. Concerning these issues, this paper proposes two new algorithms: (1) A novel feature parameter named spectral kurtosis entropy (SKE) is proposed based on the protrugram. The SKE not only avoids the manual post-processing of the protrugram but also has strong robustness to the operating conditions and parameter configurations, which have been validated by a simulation experiment in this paper. In this paper, the SKE, in conjunction with variational mode decomposition (VMD), is employed for feature extraction under variable conditions. (2) A new learning algorithm named weighted self-adaptive evolutionary extreme learning machine (WSaE-ELM) is proposed. WSaE-ELM gives each sample an extra sample weight to rebalance the training data and optimizes these weights along with the parameters of hidden neurons by means of the self-adaptive differential evolution algorithm. Finally, the hybrid method based on VMD, SKE, and WSaE-ELM is verified by using the vibration signals gathered from real bogies with speed variations. It is demonstrated that the proposed method of bogie fault diagnosis outperforms the conventional methods by up to 4.42% and 6.22%, respectively, in percentages of accuracy under variable conditions.
转向架对于轨道运输系统的安全运行至关重要，通常在不确定和多变的运行条件下工作。然而，直到现在，在多变条件下对转向架故障进行诊断还鲜有讨论。因此，开发处理多变条件的有效方法是很有价值的。此外，考虑到实际训练中正常数据远远多于故障数据，还有另一个问题，即只有少量包含故障的数据可用。针对这些问题，本文提出了两种新算法：（1）基于突起图提出了一种新的特征参数，即谱峭度摘（SKE）。SKE 不仅避免了突起图的手动后处理，而且对运行条件和参数配置具有很强的鲁棒性，本文通过仿真实验验证了这一点。本文采用 SKE 与变分模态分解（VMD）相结合的方法，在多变条件下进行特征提取。（2）提出了一种新的学习算法，称为加权自适应进化极限学习机（WSaE-ELM）。WSaE-ELM 为每个样本赋予一个额外的样本权重，以重新平衡训练数据，并通过自适应差分进化算法优化这些权重和隐藏神经元的参数。最后，通过使用具有速度变化的真实转向架采集的振动信号对基于 VMD、SKE 和 WSaE-ELM 的混合方法进行了验证。结果表明，所提出的转向架故障诊断方法在多变条件下的准确率比传统方法分别提高了 4.42%和 6.22%。
