基于机器学习的设备剩余寿命预测方法综述

Review of Machine Learning Based Remaining Useful Life Prediction Methods for Equipment

随着科学技术的发展和生产工艺的进步,当代设备日益朝着大型化、复杂化、自动化以及智能化方向发展。为保障设备安全性与可靠性,剩余寿命(Remaining useful life,RUL)预测技术受到了普遍关注,同时得到了广泛应用。传统的统计数据驱动方法受模型的选择影响明显,而机器学习具有强大的数据处理能力,并且无需确切的物理模型和专家先验知识,因而机器学习在剩余寿命预测领域表现出了广阔的应用前景。鉴于此,详细分析和阐述了基于机器学习的设备剩余寿命预测方法。根据机器学习模型结构的深度,将其分为基于浅层机器学习的方法和基于深度学习的方法。同时疏理了每类方法的发展分支与研究现状,并且总结了相应的优势和缺点,最后探讨了基于机器学习的剩余寿命预测方法的未来研究方向。

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.