Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine
Abstract
:1. Introduction
2. Related Works
2.1. RUL Prediction Based on Physical Models
2.2. RUL Prediction Based on Data-Driven Models
3. The Proposed Approach
3.1. C-MAPSS Benchmark Dataset
3.2. Feature Selection Using the Prognosability Algorithm
3.3. Data Normalization
3.4. Deep Convolutional Neural Networks
3.4.1. Convolutional Layer
3.4.2. Pooling Layer
3.4.3. Fully Connected Layer
3.5. Proposed Attention Mechanism
3.6. Time Window Technique
4. Experimental Results and Discussion
4.1. Experimental Results
4.2. Case Study of Turbofan Engine System
4.3. Comparison with Literature
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Authors | Year | Method Used | Benchmark Dataset | Results Achieved | Limitations/Gaps |
---|---|---|---|---|---|
Peng et al., [2] | 2021 | FCLCNN-LSTM | C-MAPSS | This model verified only with FD001 (11.17) and FD003 (9.99) subset of data | The key drawback of this model is the need to incrementally update the prognosis results. |
Wen, Dong and Gao [12] | 2019 | ResCNN | C-MAPSS | RMSE for FD001 (12.16)--- RMSE for FD002 (20.85)--- RMSE for FD003 (12.01)--- RMSE for FD004 (24.79) | The limitations of the proposed method are that the imbalance of signal data is ignored, and the tuning parameter process of the ensemble ResCNN is very time-consuming. |
Babu et al., [13] | 2016 | First attempt at a deep CNN | C-MAPSS | RMSE for FD001 (18.44)--- RMSE for FD002 (30.29)--- RMSE for FD003 (19.81)--- RMSE for FD004 (29.15) | The limited accuracy of the RUL estimation, means this method is not practical for real-world applications. |
Li, Ding & Sun, [14] | 2018 | DCNN | C-MAPSS | RMSE for FD001 (18.45)--- RMSE for FD002 (22.36)--- RMSE for FD003 (12.64)--- RMSE for FD004 29.16 | Additional architecture improvements are required, as the current training time exceeds that of the majority of shallow networks in the literature. |
Zhang et al., [36] | 2016 | Multi-objective deep belief network ensemble | C-MAPSS | RMSE for FD001 (15.04)--- RMSE for FD002 (25.05)--- RMSE for FD003 (12.51)--- RMSE for FD004 (28.66) | This model suffers from slow prediction process and limited accuracy of RUL estimation, which made it not cost-effective method in industrial contexts. |
Zheng et al., [37] | 2017 | Deep LSTM | C-MAPSS | RMSE for FD001 (16.14)--- RMSE for FD002 (24.49)--- RMSE for FD003 (16.18)--- RMSE for FD004 (28.17) | The main drawback can be summarised in twofold. First, the limited accuracy of the RUL prediction, which make this method is not practical for industrial contexts. Second, high computational load. |
Zhu, Chen and Peng [38] | 2018 | multi-scale CNN | PRONOSTIA | Tested on bearing dataset | Further architecture improvements are required, as the current model need more optimization. |
Zhang et al., [39] | 2019 | CNN-XGB | C-MAPSS | RMSE for FD001 (12.61)--- RMSE for FD002 (19.61)--- RMSE for FD003 (13.01)--- RMSE for FD004 (19.41) | This main drawback of this method is the computational speed and cost, with a prediction time of around 621.7 s. It not cost-effective model in industrial contexts. |
This study | 2021 | Attention-based DCNN | C-MAPSS | RMSE for FD001 (11.81)--- RMSE for FD002 (18.34)--- RMSE for FD003 (13.08)--- RMSE for FD004 (19.88) | The proposed model training time is 142 s, which shows its superiority in reducing the training time and model complexity compared to several popular methods in the literature |
Dataset | C-MAPSS | |||
---|---|---|---|---|
FD001 | FD002 | FD003 | FD004 | |
Training Units (N) | 100 | 260 | 100 | 249 |
Testing Units | 100 | 259 | 100 | 248 |
Operating Conditions (OC) | 1 | 6 | 1 | 6 |
Fault modes (FM) | 1 | 1 | 2 | 2 |
Training samples (default) | 17,731 | 48,819 | 21,820 | 57,522 |
Testing samples | 100 | 259 | 100 | 248 |
Batch Size | Dropout Size | Epoch Number | Iteration per Epoch | Maximum Alteration | Num Hidden Units | Activation |
---|---|---|---|---|---|---|
512 | 0.5 | 20 | 32 | 640 | 1000 | RELU |
Pooling Layer | RMSE |
---|---|
With | 21.34 |
Without | 14.92 |
Prediction Model | C-MAPSS | ||||
---|---|---|---|---|---|
Measure | FD001 | FD002 | FD003 | FD004 | |
Proposed attention-based DCNN Predictor | RMSE--- Score | 11.81--- 223.0 | 18.34--- 2550 | 13.08--- 280.5 | 19.88--- 2982.31 |
CNN-XGB [39] | RMSE--- Score | 12.61--- 224.73 | 19.61--- 2525.99 | 13.01--- 279.36 | 19.41--- 2930.65 |
MODBNE [36] | RMSE--- Score | 15.04--- 334.23 | 25.05--- 5585.34 | 12.51--- 6557.62 | 28.66--- 6557.62 |
Echo State Network with Kalman Filter [55] | RMSE--- Score | 63.46--- - | - --- - | - --- - | - --- - |
ANN-EN [56] | RMSE--- Score | 14.39--- 337 | 29.09--- - | 15.42--- 533 | 34.74--- - |
MLP [13] | RMSE--- Score | 37.56--- 17972 | 80.03--- 780280 | 37.56--- 17409 | 77.36--- 5616600 |
Deep CNN [13] | RMSE--- Score | 18.45--- 1286.7 | 30.29--- 13570 | 19.81--- 1596.2 | 29.16--- 7886.4 |
DW-RNN [57] | RMSE--- Score | 22.52--- N/A | 25.90--- N/A | 18.75--- N/A | 24.44--- N/A |
MTL-RNN [57] | RMSE--- Score | 21.47--- N/A | 25.78--- N/A | 17.98--- N/A | 22.82--- N/A |
DCNN [14] | RMSE--- Score | 12.61--- 273.7 | 22.36--- 10412.0 | 12.64--- 284.1 | 23.31--- 12466 |
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Muneer, A.; Taib, S.M.; Fati, S.M.; Alhussian, H. Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine. Symmetry 2021, 13, 1861. https://doi.org/10.3390/sym13101861
Muneer A, Taib SM, Fati SM, Alhussian H. Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine. Symmetry. 2021; 13(10):1861. https://doi.org/10.3390/sym13101861
Chicago/Turabian StyleMuneer, Amgad, Shakirah Mohd Taib, Suliman Mohamed Fati, and Hitham Alhussian. 2021. "Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine" Symmetry 13, no. 10: 1861. https://doi.org/10.3390/sym13101861
APA StyleMuneer, A., Taib, S. M., Fati, S. M., & Alhussian, H. (2021). Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine. Symmetry, 13(10), 1861. https://doi.org/10.3390/sym13101861