# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 1 Introduction  
Recently, fault diagnosis and health management, including diagnosis and prognosis approaches, have been actively researched [1; 2; 3; 4; 5]. Fault diagnosis and health management techniques are widely applied in diverse areas such as manufacturing, aerospace, automotive, power generation, and transportation [6; 7; 8; 9]. Diagnostics is the process of identification of a failure. Prognostics is an engineering discipline focusing on the prediction of the time when a system or a component fails to perform its intended function. In diagnostics, once degradation is detected, unscheduled maintenance should be performed to prevent the consequences of failure. In prognostics, maintenance preparation could be performed when the system is up and running, since the time to failure is known early enough. A major application of prognostics is estimating remaining useful life (RUL) of systems. It is also called remaining service or residual life estimation [10]. Prognostic methods can be grouped into two major categories: data-driven methods, and physics-based methods. The former requires sufficient samples that are run until faults or failures are detected, whereas the latter require an understanding of the physics of system failure progression [11]. In recent years, the data-driven approaches for remaining useful life (RUL) studies [12; 13; 14; 15; 16] have been attracting a lot of attention due to their avoidance of dependency on the theoreticalunderstanding of complex systems. However, a major challenge in data-driven prognostics is that it is often impossible to obtain a large number of samples for failure progression, which is costly and labor demanding [11]. This situation can arise for several reasons: (1) industry systems are not allowed to run until failure due to the consequences, especially for critical systems and failures; (2) most of the electro-mechanical failures occur slowly and follow a degradation path such that failure degradation of a system might take months or even years [17]. Several methods have been used to address this challenge. The first approach is accelerated aging by running the system in a lab with extreme loads, increased speed or using imitations of real components which are made by vulnerable materials, so that a failure progresses faster than normal [14; 15; 16]. Another approach is introducing unnatural failure progression by using exponential degradation to model regular failure progression [18]. Both methods have their own strengths and weaknesses with the capability to represent the failure degradation to a certain level. However, in real-world applications where the condition is very different from the lab environment, these methods are difficult to apply and to obtain good estimation performance. Recently, deep learning has been shown to achieve impressive results in areas such as computer vision, image and video processing, speech, and natural language processing [19]. Deep learning methods have also been applied to fault diagnosis [20; 21; 22] and the RUL estimation problem. For the RUL estimation problem, a Convolutional Neural Network (CNN) is applied on a sliding window with multi-times weight and failures height in [23]. However, a major limitation of this work is that the sequence information is not fully considered in the CNN model. To address this issue, a Long Short-Term Memory (LSTM) model was proposed for RUL estimation [24], where the model can make full use of the sensor sequence information and performs much better than the CNN model. Following this success, Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) have been designed in [25] for RUL prediction, where firstly a CNN is first used to extract robust and informative local features from the sequential input, and then a bi-directional LSTM is introduced to encode temporal information.