Abstract
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.
Highlights
Fault diagnosis and health management, including diagnosis and prognosis approaches, have been actively researched [1,2,3,4,5]
We developed a bidirection Long Short-Term Memory (LSTM) recurrent neural network model for remaining useful life (RUL) prediction; We proposed and demonstrated for the first time that the transfer learning-based prognostic model can boost the performance of RUL estimation by making full use of different but more or less related datasets; We showed that datasets of mixed working conditions can be used to improve the performance of single working condition RUL prediction while the opposite is not true
To verify the performance of our Bi-directional Long Short-Term Memory (BLSTM)-based transfer learning algorithm for RUL estimation, we conducted a series of experiments on the C-MAPSS Datasets
Summary
Fault diagnosis and health management, including diagnosis and prognosis approaches, have been actively researched [1,2,3,4,5]. Fault diagnosis and health management techniques are widely applied in diverse areas such as manufacturing, aerospace, automotive, power generation, and transportation [6,7,8,9]. Prognostics is an engineering discipline focusing on the prediction of the time when a system or a component fails to perform its intended function. Once degradation is detected, unscheduled maintenance should be performed to prevent the consequences of failure. Maintenance preparation could be performed when the system is up and running, since the time to failure is known early enough
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