基于多尺度工况增强网络及Informer的设备剩余寿命预测

Device RUL prediction based on multi-scale work condition enhancement network and Informer

摘要:

设备RUL预测在提高设备可靠性、安全性、降低维护成本等方面具有重要意义。通过提前发现设备的健康状态和潜在故障，RUL预测有助于降低突发故障风险、延长设备寿命，提高工作效率，确保任务正常运行。然而在面对设备越来越复杂，采集到的传感器数据维度越来越高，传统方法和某些深度学习方法在处理特征关系、长时间序列数据和挖掘重要传感器数据方面存在限制。为了提高预测准确性，提出一种基于MWCEN结合Informer的混合模型——MWCEN-Informer，MWCEN通过动态工况编码算法对设备时序数据进行工况编码，对设备传感器进行一维多尺度混合卷积充分提取特征信息，使用多分支通道注意力机制增强有效特征，增强后的传感器数据输入Informer用于分析设备传感器时序数据的关联性，以实现更准确的设备RUL预测。以基于C-MAPSS的通用涡扇发动机数据集进行验证，结果表明，该模型在四个子集上的RMSE平均减少了5.5%，S-Score平均减少了4.7%，能有效提高设备在复杂工况和复杂故障下的RUL预测精度。

Abstract:

Device RUL prediction is important in improving device reliability, safety, and reducing maintenance costs. By discovering the health status and potential faults of device in advance, RUL prediction helps reduce the risk of sudden failure, extend device life, improve work efficiency, and ensure normal operation of tasks. However, in the face of the increasing complexity of device and the increasing dimensionality of collected sensor data, traditional methods and certain deep learning methods have limitations in handling feature relationships, long time series data and mining important sensor data. In order to improve the prediction accuracy, a hybrid model based on MWCEN combined with Informer - MWCEN-Informer is proposed. MWCEN encodes the device time series data with dynamic work condition coding algorithm, fully extracts the feature information by one-dimensional multi-scale hybrid convolution of the device sensor information, the effective features are enhanced using the multi-branch channel attention mechanism, and the enhanced sensor data are input into Informer for analysing the correlation of the device sensor timing data to achieve more accurate device RUL prediction. Validation is carried out with a generic turbofan engine data set based on C-MAPSS, and the results show that the model reduces the RMSE by an average of 5.5% and the S-Score by an average of 4.7% on the four subsets, which effectively improves the RUL prediction accuracy of the device under complex operating conditions and complex faults.