Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

• Main Authors: Ansi Zhang, Honglei Wang, Shaobo Li, Yuxin Cui, Zhonghao Liu, Guanci Yang, Jianjun Hu
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Applied Sciences
• Subjects: remaining useful life; fault diagnosis; LSTM; deep learning; transfer learning; turbofan engine
• Online Access: https://www.mdpi.com/2076-3417/8/12/2416

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.