This paper presents a prognostic method for RUL (remaining useful life) prediction based on EMD (empirical mode decomposition)-ESN (echo state network). The combination method adopts EMD to decompose the multisensor time series into a bunch of IMFs (intrinsic mode functions), which are then predicted by ESNs, and the outputs of each ESN are summarized to obtain the final prediction value. The EMD can decompose the original data into simpler portions and during the decomposition process, much noise is filtered out and the subsequent prediction is much easier. The ESN is a relatively new type of RNN (recurrent neural network), which substitutes the hidden layers with a reservoir remaining unchanged during the training phase. The characteristic makes the training time of ESN is much shorter than traditional RNN. The proposed method is applied to the turbofan engine datasets and is compared with LSTM (Long Short-Term Memory) and ESN. Extensive experimental results show that the prediction performance and efficiency are much improved by the proposed method.

中文翻译：

---

基于EMD-ESN的RUL多传感器预测

本文提出了一种基于EMD（经验模态分解）-ESN（回波状态网络）的RUL（剩余使用寿命）预测方法。组合方法采用EMD将多传感器时间序列分解为一堆IMF（本征模式函数），然后由ESN对其进行预测，然后汇总每个ESN的输出以获得最终预测值。EMD可以将原始数据分解为更简单的部分，并且在分解过程中，可以滤除很多噪声，并且随后的预测要容易得多。ESN是一种相对较新的RNN（递归神经网络），它用训练期间保持不变的储层替换隐藏层。该特性使得ESN的训练时间比传统的RNN要短得多。将该方法应用于涡扇发动机数据集，并与LSTM（长期短期记忆）和ESN进行了比较。大量的实验结果表明，该方法可以大大提高预测性能。