Noisy parallel hybrid model of NBGRU and NCNN architectures for remaining useful life estimation

The parallel hybrid models of different deep neural networks architectures are the most promising approaches for remaining useful life (RUL) estimation. In light of that, this paper introduces for the first time in the literature a new parallel hybrid deep neural network (DNN) solution for RUL estimation, named as the Noisy Multipath Parallel Hybrid Model for Remaining Useful Life Estimation (NMPM). The proposed framework comprises of three parallel paths, the first one utilizes a noisy Bidirectional Long-short term memory (BLSTM) that used for extracting temporal features and learning the dependencies of sequence data in two directions, forward and backward, which can benefit completely from the input data. While the second parallel path employs noisy multilayer perceptron (MLP) that consists of three layers to extract different class of features. The third parallel path utilizes noisy convolutional neural networks (CNN) to extract another class of features. The concatenated output of the previous parallel paths is then fed into a noisy fusion center (NFC) to predict the RLU. The NMPM has been trained based on a noisy training to enhance the generalization behavior, as well as strengthen the model accuracy and robustness. The NMPM framework is tested and evaluated by using CMAPSS dataset provided by NASA.

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Multipath Parallel Hybrid Deep Neural Networks Framework for Remaining Useful Life Estimation

2020 IEEE International Conference on Prognostics and Health Management (ICPHM) ◽

10.1109/icphm49022.2020.9187040 ◽

2020 ◽

Author(s):

Ali Al-Dulaimi ◽

Amir Asif ◽

Arash Mohammadi

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Remaining Useful Life ◽

Life Estimation ◽

Parallel Hybrid ◽

Useful Life

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Remaining useful life estimation: a review on stochastic process-based approaches

Recent Patents on Engineering ◽

10.2174/1872212114999200423115526 ◽

2020 ◽

Vol 14 ◽

Author(s):

Dangbo Du ◽

Jianxun Zhang ◽

Xiaosheng Si ◽

Changhua Hu

Keyword(s):

Stochastic Process ◽

Health Management ◽

Remaining Useful Life ◽

Future Trends ◽

Prognostics And Health Management ◽

Inverse Gaussian ◽

Life Estimation ◽

Inverse Gaussian Process ◽

Pros And Cons ◽

Useful Life

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.

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