scholarly journals Deep-Learning Based Prognosis Approach for Remaining Useful Life Prediction of Turbofan Engine

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1861
Author(s):  
Amgad Muneer ◽  
Shakirah Mohd Taib ◽  
Suliman Mohamed Fati ◽  
Hitham Alhussian
Keyword(s):  
Time Window ◽  
Scoring Function ◽  
Remaining Useful Life ◽  
Temporal Information ◽  
Turbofan Engine ◽  
Turbofan Engines ◽  
Testing Dataset ◽  
Window Method ◽  
Useful Life ◽  
Prediction Problems

The entire life cycle of a turbofan engine is a type of asymmetrical process in which each engine part has different characteristics. Extracting and modeling the engine symmetry characteristics is significant in improving remaining useful life (RUL) predictions for aircraft components, and it is critical for an effective and reliable maintenance strategy. Such predictions can improve the maximum operating availability and reduce maintenance costs. Due to the high nonlinearity and complexity of mechanical systems, conventional methods are unable to satisfy the needs of medium- and long-term prediction problems and frequently overlook the effect of temporal information on prediction performance. To address this issue, this study presents a new attention-based deep convolutional neural network (DCNN) architecture to predict the RUL of turbofan engines. The prognosability metric was used for feature ranking and selection, whereas a time window method was employed for sample preparation to take advantage of multivariate temporal information for better feature extraction by means of an attention-based DCNN model. The validation of the proposed model was conducted using a well-known benchmark dataset and evaluation measures such as root mean square error (RMSE) and asymmetric scoring function (score) were used to validate the proposed approach. The experimental results show the superiority of the proposed approach to predict the RUL of a turbofan engine. The attention-based DCNN model achieved the best scores on the FD001 independent testing dataset, with an RMSE of 11.81 and a score of 223.

scholarly journals Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2453
Author(s):  
Amgad Muneer ◽  
Shakirah Mohd Taib ◽  
Sheraz Naseer ◽  
Rao Faizan Ali ◽  
Izzatdin Abdul Aziz
Keyword(s):  
Time Window ◽  
Attention Mechanism ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
Prognostic Models ◽  
Prediction Ability ◽  
Turbofan Engine ◽  
Useful Life ◽  
Engine System

Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method’s applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

scholarly journals An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 35 ◽  
Author(s):  
Hung-Cuong Trinh ◽  
Yung-Keun Kwon
Keyword(s):  
Scoring Function ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Filter Method ◽  
Feature Construction ◽  
Training Set ◽  
Robust Solution ◽  
Benchmark Datasets ◽  
Useful Life ◽  
Better Than

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

scholarly journals Remaining Useful Life Estimation Using CNN-XGB With Extended Time Window

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 154386-154397 ◽  
Author(s):  
Xiaoyong Zhang ◽  
Pengcheng Xiao ◽  
Yingze Yang ◽  
Yijun Cheng ◽  
Bin Chen ◽  
...  
Keyword(s):  
Time Window ◽  
Remaining Useful Life ◽  
Extended Time ◽  
Life Estimation ◽  
Useful Life

scholarly journals A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 752
Author(s):  
Jun Peng ◽  
Zhiyong Zheng ◽  
Xiaoyong Zhang ◽  
Kunyuan Deng ◽  
Kai Gao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Lithium Ion Battery ◽  
Time Window ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Adaptive Optimization ◽  
Feature Enhancement ◽  
Useful Life ◽  
Box Cox Transformation

Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. To address this challenge, this paper proposes a new data-driven method using feature enhancement and adaptive optimization. First, the features of battery aging are extracted online. Then, the feature enhancement technologies, including the box-cox transformation and the time window processing, are used to fully exploit the potential of features. The box-cox transformation can improve the correlation between the features and the aging status of the battery, and the time window processing can effectively exploit the time information hidden in the historical features sequence. Based on this, gradient boosting decision trees are used to establish the RUL prediction model, and the particle swarm optimization is used to adaptively optimize the model parameters. This method was applied on actual lithium-ion battery degradation data, and the experimental results show that the proposed model is superior to traditional prediction methods in terms of accuracy.

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