Abstract
In order to solve the problems of high data dimension and insufficient consideration of time series correlation information, a multi-scale deep convolutional neural network and long-short-term memory (MSDCNN-LSTM) hybrid model is proposed for remaining useful life (RUL) of equipments. First, the sensor data is processed through normalization and sliding time window to obtain input samples; then multi-scale deep convolutional neural network (MSDCNN) is used to capture detailed spatial features, at the same time, time-dependent features are extracted for effective prediction combining with long short-term memory (LSTM). Experiments on simulation dataset of commercial modular aero-propulsion system show that, compared with other state-of-the-art methods, the prediction method proposed in this paper has achieved better RUL prediction results, especially for the prediction of the life of equipment with complex failure modes and operating conditions. The effect is obvious. It can be seen that the prediction method proposed in this paper is feasible and effective.
Highlights
摘 要:针对设备剩余使用寿命(RUL)预测过程中数据维度高,时间序列相关性信息难以充分考虑的 实际应用需求,提出一种多尺度深度卷积神经网络和长短时记忆网络融合( multi⁃scale deep convolu⁃ tional neural network and long short⁃term memory, MSDCNN⁃LSTM)的设备剩余寿命预测方法。 对传感 器数据进 行标准化和滑动时间窗口处理得到输入样本; 采用基于多尺度深度卷积神经网络 (MSDCNN)提取空间详细特征,采用长短时记忆网络(LSTM) 提取时间相关性特征以进行有效的预 测。 基于商用模块化航空推进系统仿真数据集的实验表明,相较于其他最新方法,文中提出的方法取 得了较好的预测结果,尤其是对于故障模式和运行条件复杂的设备寿命预测需求,该方法效果明显。
A RUL prediction method of equipments based on MSDCNN⁃LSTM
In order to solve the problems of high data dimension and insufficient consideration of time series correla⁃ tion information, a multi⁃scale deep convolutional neural network and long⁃short⁃term memory ( MSDCNN⁃LSTM) hybrid model is proposed for remaining useful life ( RUL) of equipments
Summary
摘 要:针对设备剩余使用寿命(RUL)预测过程中数据维度高,时间序列相关性信息难以充分考虑的 实际应用需求,提出一种多尺度深度卷积神经网络和长短时记忆网络融合( multi⁃scale deep convolu⁃ tional neural network and long short⁃term memory, MSDCNN⁃LSTM)的设备剩余寿命预测方法。 对传感 器数据进 行标准化和滑动时间窗口处理得到输入样本; 采用基于多尺度深度卷积神经网络 (MSDCNN)提取空间详细特征,采用长短时记忆网络(LSTM) 提取时间相关性特征以进行有效的预 测。 基于商用模块化航空推进系统仿真数据集的实验表明,相较于其他最新方法,文中提出的方法取 得了较好的预测结果,尤其是对于故障模式和运行条件复杂的设备寿命预测需求,该方法效果明显。 因此,本文提出了一种基于多尺度深度卷积神 经网 络和长短时记忆网络融合 ( multi⁃scale deep convolutional neural network and long short⁃term memory,MSDCNN⁃LSTM) 的剩余寿命预测方法来提 高预测精度。 首先通过最小-最大标准化和滑动时 间窗口处理原始传感器监测数据;然后 MSDCNN 模 型对处理后的数据进行不同尺度特征学习, LSTM 增强了设备退化期间时间序列的记忆能力,并实现 RUL 预测;最后在商用模块化航空推进系统仿真 ( commercial modular aero⁃propulsion system simula⁃ tion,C⁃MAPSS) 数据集上证明了所提出方法的有 效性。 式中: σ 表示 sigmoid 激活函数;wxc,whc,wxi ,whi ,wci , wxf ,whf ,wcf ,wxo ,who ,wco 表 示 权 重;bc ,bi ,bf ,bo 表 示
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