Background:
Remaining useful life (RUL) estimation is the central mission to the complex
systems’ prognostics and health management. During last decades, numbers of developments and
applications of the RUL estimation have proliferated.
Objective:
As one of the most popular approaches, stochastic process-based approach has been widely
used for characterizing the degradation trajectories and estimating RULs. This paper aimed at
reviewing the latest methods and patents on this topic.
Methods:
The review is concentrated on four common stochastic processes for degradation modelling
and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov
chain.
Results:
After a briefly review of these four models, we pointed out the pros and cons of them, as well
as the improvement direction of each method.
Conclusion:
For better implementation, the applications of these four approaches on maintenance and
decision-making are systematically introduced. Finally, the possible future trends are concluded
tentatively.
Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.
Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.
Remaining Useful Life Prediction Using Temporal Convolution with Attention