School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China.
College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2021 Jun 11;21(12):4043. doi: 10.3390/s21124043.
It is important for equipment to operate safely and reliably so that the working state of mechanical parts pushes forward an immense influence. Therefore, in order to enhance the dependability and security of mechanical equipment, to accurately predict the changing trend of mechanical components in advance plays a significant role. This paper introduces a novel condition prediction method, named error fusion of hybrid neural networks (EFHNN), by combining the error fusion of multiple sparse auto-encoders with convolutional neural networks for predicting the mechanical condition. First, to improve prediction accuracy, we can use the error fusion of multiple sparse auto-encoders to collect multi-feature information, and obtain a trend curve representing machine condition as well as a threshold line that can indicate the beginning of mechanical failure by computing the square prediction error (). Then, convolutional neural networks predict the state of the machine according to the original data when the value exceeds the threshold line. It can be seen from this result that the EFHNN method in the prediction of mechanical fault time series is available and superior.
设备安全可靠运行非常重要，因此机械部件的工作状态具有巨大的推动作用。为了提高机械设备的可靠性和安全性，准确预测机械部件的变化趋势具有重要意义。本文提出了一种新的状态预测方法，即混合神经网络的误差融合（EFHNN），通过将多个稀疏自编码器的误差融合与卷积神经网络相结合，用于预测机械状态。首先，为了提高预测精度，可以使用多个稀疏自编码器的误差融合来收集多特征信息，并通过计算平方预测误差（）来获得表示机器状态的趋势曲线以及指示机械故障开始的阈值线。然后，当值超过阈值线时，卷积神经网络根据原始数据预测机器的状态。可以看出，EFHNN 方法在机械故障时间序列的预测中是可行的，并且具有优越性。
