深度学习在装备剩余使用寿命预测技术中的研究现状与挑战

Current research and challenges of deep learning for equipment remaining useful life prediction

有效的装备剩余使用寿命(RUL)预测有助于及时规避严重生产事故,并为视情维护提供技术支持,在现代工业中发挥着重要作用。近年来,深度学习凭借其在大数据处理和特征提取方面的独特优势与潜力,在RUL预测领域得到了广泛应用。鉴于此,综述了深度学习在装备RUL预测领域的最新研究。首先介绍几种应用于RUL预测的典型深度学习方法,并对其实现RUL预测的基本原理和建模方法进行了概述;其次,对近年来典型深度学习方法在RUL预测领域的应用和发展趋势进行了详细总结;最后,探讨了现阶段基于深度学习的RUL预测技术所面临的挑战性问题及展望。

Serious industrial accidents can be avoided in time by valid Remaining Useful Life(RUL)prediction for equipment.Besides,RUL prediction can also provide technical support for condition-based maintenance,which plays an important role in modern industry.In recent years,deep learning has been widely used in the field of RUL prediction by virtue of its unique advantages and potential in big data processing and feature extraction.In view of this,the latest research on deep learning in equipment RUL prediction was reviewed.Several typical deep learning methods applied to RUL prediction were introduced,whose basic principles and modeling methods were also described.Then,the applications and development trends of these typical deep learning methods in the field of RUL prediction in recent years were summarized in detail.The current challenges of deep learning-based RUL prediction along with relevant prospects were put forward and discussed.