A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

Abstract

Accurate and timely prediction of remaining useful life (RUL) of a machine enables the machine to have an appropriate operation and maintenance decision. Data-driven RUL prediction methods are more attractive to researchers because they can be deployed quicker and cheaper compared to other approaches. The existing deep neural network (DNN) models proposed for the applications of RUL prediction are mostly single-path and top-down propagation. In order to improve the prognostic accuracy of the network, this paper proposes a directed acyclic graph (DAG) network that combines long short term memory (LSTM) and a convolutional neural network (CNN) to predict the RUL. Different from the existing prediction models combined with CNN and LSTM, the method proposed in this paper combines CNN and LSTM organically instead of just using CNN for feature extraction. Moreover, when a single timestamp is used as an input, padding the signals in the same training batch would affect the prediction ability of the developed model. To overcome this drawback, the proposed method generates a short-term sequence by sliding the time window (TW) with one step size. In addition, based on the degradation mechanism, the piece-wise RUL function is used instead of the traditional linear function. In the experimental test, the turbofan engine degradation simulation dataset provided by NASA is used to validate the proposed RUL prediction model. By comparing with the existing methods using the same dataset, it can be concluded that the prediction method proposed in this paper has better prediction capability. INDEX TERMS Remaining useful life prediction, long-short-term memory network, convolutional neural networks, turbofan engine.

Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion

“…The experimental results indicate that the proposed LSTM-Fusion outperforms all other approaches under RMSE metric evaluation, and comparable results of the Score value are obtained compare to the state-of-theart methods. The experimental results show that our method outperforms slightly the method proposed in [70], this suggests that our core idea of fusing the extracted features which have variable time window sizes has merit performance for capturing the local and global characteristics of the signal for the RUL prediction.…”

“…A hybrid deep learning model combining CNN and LSTM is demonstrated for machine health monitoring in [67], where a CNN is employed for local features extraction and bi-directional LSTM [68] is demonstrated and built on CNN outputs for the temporal information encoding and representation learning. Al-Dulaimi et al [69] and Li et al [70] proposed similar approaches, where instead of a series connection which proposed in [67], a hybrid network architecture by combining CNN and LSTM network in parallel manner for RUL estimation has been presented respectively. Particularly, in [70], a directed acyclic graph network architecture is employed in this hybrid design.…”

“…Al-Dulaimi et al [69] and Li et al [70] proposed similar approaches, where instead of a series connection which proposed in [67], a hybrid network architecture by combining CNN and LSTM network in parallel manner for RUL estimation has been presented respectively. Particularly, in [70], a directed acyclic graph network architecture is employed in this hybrid design. The outputs from the LSTM and CNN network are summed by elements-wise and fed into a second LSTM network, then forwarded to a fully connected layer for RUL prediction.…”

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

“…Indeed, typically AEs are used to automatically extract complex and meaningful features from raw data or to obtain more informative representations of a set of already extracted features. AEs have been applied to data gathered from several machines and industrial components, such as rolling element bearings (Jia et al, 2016;Liu et al, 2016;Lu et al, 2016;Jia et al, 2018), gearboxes (Jia et al, 2018), electrical generators (Michau et al, 2017;Michau et al, 2019), wind turbines (Yang et al, 2016), chemical industrial plants (Lv et al, 2017), induction motors (Sun et al, 2016b), air compressors (Thirukovalluru et al, 2016), hydraulic pumps (Zhu et al, 2015), transformers (Wang et al, 2016), spacecrafts (Li and Wang, 2015) and gas turbine combustors (Yan and Yu, 2019). As mentioned before, AEs are often used in combination with other classifiers, such as simple softmax classifiers (Liu et al, 2016), feed-forward neural networks (Sun et al, 2016b), RFs (Thirukovalluru et al, 2016) and SVMs (Sun et al, 2016b;Lv et al, 2017).…”

Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM

“…With the rise of deep learning (DL) [33], data-driven prediction methods are increasingly applied in various fields [34,35]. In maritime applications, Joohyun Woo [36] proposed a deep-learning-based dynamic model identification method.…”