Abstract: With the development of science and technology as well as the advancement of production technology, contemporary equipment is increasingly developing towards large-scale, complex, automated and intelligent direction. In order to ensure the safety and reliability of equipment, the remaining useful life (RUL) prediction technology has received widespread attention and been widely used. Traditional statistical data-driven methods are obviously influenced by the choice of models. Machine learning has powerful data processing ability, and does not need exact physical models and prior knowledge of experts. Therefore, machine learning has a broad application prospect in the field of RUL prediction. In view of this, the RUL prediction methods based on machine learning are analyzed and expounded in detail. According to the depth of machine learning model structure, it is divided into shallow machine learning methods and deep learning methods. At the same time, the development branches and research status of each method are sorted out, and the corresponding advantages and disadvantages are summarized. Finally, the future research directions of RUL prediction methods based on machine learning are discussed.

Key words: deep learning, machine learning, neural network, remaining useful life prediction, support vector machine