# Transfer Learning Strategies for Deep Learning-based PHM Algorithms
## 1 Introduction  
Prognostics and health management (PHM) is a framework that offers comprehensive yet individualized solutions for managing system health [1], including prognostics and health management. Prognostics is usually defined as the generation of current system status evaluation results through fault data or historical maintenance data collected by sensors, combined with the future working environment of the system to predict the system's remaining useful life (RUL) or end of life (EOL); health management is defined as the process of measuring, recording and monitoring the extent to which the system deviates from normal operating conditions in real time. As an engineering discipline PHM aims to provide users with an integrated view of the health state of a machine or an overall system. Diagnostics are also included in prognostics and health management [2]. In today's increasingly complex industrial systems, PHM technology provides important support for system design and development, production and manufacturing, operation, logistics support and maintenance, decommissioning and destruction. Because the big data generated by industrial systems is massive, diverse, high-speed, and variable, deep learning technology has been added to overcomethe existing problems of prognostics and diagnostics. The deep learning-based PHM technology has been used in fault diagnosis and health evaluation of motors, gearboxes, bearings, etc., and has achieved better results than traditional methods [3; 4; 5; 6; 7; 8]. The PHM algorithm is the core of PHM technology. It provides the data processing foundation for fault diagnostics, prognostics and health management, covering the entire process from data preprocessing, condition monitoring, fault diagnostics, prognostics to health management. The development and application of PHM algorithms is mostly based on proprietary systems, and deep learning PHM algorithms are no exception. This means that the deep learning PHM algorithms developed based on a certain instance are difficult to effectively reuse and generalize. For similar problems, rebuilding the algorithm from scratch means a lot of manpower, material resources and time. At the same time, if similar tasks do not have enough data support, the newly developed algorithms will have difficulty in displaying good task performance or may even fail to complete tasks. Taking rotary bearing fault diagnosis as an example, the training data of a fault diagnosis classifier may come conditions where the motor has no load, while the motor may have different load states in practical application. Although the failure mode has not changed, the target data distribution of different load states has changed, so the classifier trained by the training data (source domain) cannot be directly used for the target data (target domain). It is undoubtedly expensive to collect enough fault data available for training to reconstruct the fault diagnosis classifier, and it is difficult to directly train a satisfactory fault diagnosis classifier for the collection of a small amount of data [9]. In addition to the above cases, if the rotary bearing is replaced, the original fault diagnosis classifier will not be applicable. Even if sufficient target domain data is collected, the data reprocessing and network construction tuning will be very expensive and inefficient. To solve this challenge, a transfer learning technique, whose major research focus is how to store the knowledge gained when solving a problem and apply it to different but related problems, would be needed and desirable to reduce the amount of development and data collection costs and improve the performance of algorithms in the target domain. There are already many examples in knowledge engineering where transfer learning can be truly promising and useful, including object recognition, image classification and natural language processing [10; 11; 12]. [PERSON] [13] proposed a deep neural network for fault diagnosis-based domain adaption in 2017.