# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 1 Introduction
In addition to these approaches which use the raw input, a Vanilla LSTM neural network model has been used in [26] for RUL prediction, where a dynamic differential technology was proposed to extract inter-frame information and achieved high prediction accuracy. Moreover, an ensemble learning-based prognostic method is proposed in [27], which combines prediction results from multiple learning algorithms to get better performance. However, all these modern deep learning approaches for RUL prediction have a major limitation: they all require the availability of a large amount of training data for training these deep neural networks, while in real-world applications, it is often impossible to obtain a large number of failure progression samples. Therefore, both these deep neural network methods and traditional methods have not addressed the major challenge in data-driven prognostics: the big data of failure samples. In this paper, we propose to use transfer learning-based deep neural networks for RUL prediction. In recent years, transfer learning has made great progress in image, audio, and text processing to address the data scarcity issue by taking advantage of datasets in related domains. It is widely adopted in the situation where source data and target data are in different feature spaces or have different distributions [28; 29]. Transfer learning methods work by learning properties from source data and transfer them to the target data. The source data and target data can be different. But they should be more or less related. [PERSON] et al. [30] applied the domain separation framework of transfer learning for automatic speech recognition. [PERSON] et al. [31] applied transfer learning in object detection with improved detection performance. [PERSON] et al. [32] used transfer learning for breast cancer histology image analysis and achieved better performance than the popular handcrafted features. These studies showed that transfer learning can make use of both source data and target data to get better performance. In real-world applications of RUL prediction, the scarcity of target data is one of the major challenges in data-driven prognostics as it is often not possible to obtain large numbers of samples of failure progressions. However, we usually have access to a large amount of data of different but approximately related working conditions. In this paper, we proposed a transfer learning approach with LSTM deep neural networks for RUL prediction. It provides an effective way to address the major challenge in data-driven prognostics. Our contribution in this paper includes:  
1. We developed a bidirection LSTM recurrent neural network model for RUL prediction;
2. We proposed and demonstrated for the first time that the transfer learning-based prognostic model can boost the performance of RUL estimation by making full use of different but more or less related datasets;
3. We showed that datasets of mixed working conditions can be used to improve the performance of single working condition RUL prediction while the opposite is not true. This can give good guidance in real-world application where samples of certain working conditions are hard to obtain. This rest of this paper is organized as follows: Section 2 describes the transfer learning-based prognostic RUL prediction algorithm. Section 3 presents the experiments and results. Section 4 discusses related issues of the results and Section 5 concludes the paper.