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.

Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

2020 ◽  
pp. 1-1
Author(s):  
Naipeng Li ◽  
Yaguo Lei ◽  
Nagi Gebraeel ◽  
Zhijian Wang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
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.

scholarly journals A New Hybrid Prognostic Methodology

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Omer F. Eker ◽  
Fatih Camci ◽  
Ian K. Jennions
Keyword(s):  
Hybrid Methods ◽  
Industrial Applications ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Prognostic Models ◽  
Advantages And Disadvantages ◽  
Minimal Data ◽  
Useful Life ◽  
Filter Clogging ◽  
Short Term Prediction

Methodologies for prognostics usually centre on physics-based or data-driven approaches. Both have advantages and disadvantages, but accurate prediction relies on extensive data being available. For industrial applications, this is very rarely the case, and hence the chosen method’s performance can deteriorate quite markedly from optimal. For this reason, a hybrid methodology, merging physics-based and data-driven approaches, has been developed and is reported here. Most, if not all, hybrid methods apply physics-based and data-driven approaches in different steps of the prognostics process (i.e. state estimation and state forecasting). The presented technique combines both methods in forecasting, and integrates the short-term prediction of a physics-based model with the longer-term projection of a similarity-based data-driven model, to obtain remaining useful life estimation. The proposed hybrid prognostic methodology has been tested on two engineering datasets, one for crack growth and the other for filter clogging. The performance of the presented methodology has been evaluated by comparing remaining useful life estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability, especially in the case of minimal data being available.

scholarly journals A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets

2017 ◽  
Vol 231 (4) ◽  
pp. 350-363 ◽  
Author(s):  
Sameer Al-Dahidi ◽  
Francesco Di Maio ◽  
Piero Baraldi ◽  
Enrico Zio
Keyword(s):  
Markov Model ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Data Driven ◽  
Prognostic Models ◽  
Ensemble Approach ◽  
Fuzzy Similarity ◽  
Finite State ◽  
Useful Life ◽  
Life Predictions

In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity–based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity–based models.

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 A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
David Solís-Martín ◽  
Juan Galán-Páez ◽  
Joaquín Borrego-Díaz
Keyword(s):  
Feature Vector ◽  
Remaining Useful Life ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Aircraft Engines ◽  
Deep Convolutional Neural Networks ◽  
Turbofan Engine ◽  
The Third ◽  
Useful Life ◽  
Low Dimensional

This paper presents the data-driven techniques and methodologies used to predict the remaining useful life (RUL) of a fleet of aircraft engines that can suffer failures of diverse nature. The solution presented is based on two Deep Convolutional Neural Networks (DCNN) stacked in two levels. The first DCNN is used to extract a low-dimensional feature vector using the normalized raw data as input. The second DCNN ingests a list of vectors taken from the former DCNN and estimates the RUL. Model selection was carried out by means of Bayesian optimization using a repeated random subsampling validation approach. The proposed methodology was ranked in the third place of the 2021 PHM Conference Data Challenge.

scholarly journals Remaining Useful Life Estimation of Aircraft Engines Based on Deep Convolution Neural Network and LightGBM Combination Model

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Lijun Liu ◽  
Lan Wang ◽  
Zhen Yu
Keyword(s):  
Time Window ◽  
Health Management ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Gradient Boosting ◽  
Data Set ◽  
Light Gradient ◽  
Model Combining ◽  
Aero Engine ◽  
Useful Life

AbstractAccurately predicting the remaining useful life (RUL) of aero-engines is of great significance for improving the reliability and safety of aero-engine systems. Because of the high dimension and complex features of sensor data in RUL prediction, this paper proposes a model combining deep convolution neural networks (DCNN) and the light gradient boosting machine (LightGBM) algorithm to estimate the RUL. Compared with traditional prognostics and health management (PHM) techniques, signal processing of raw sensor data and prior expertise are not required. The procedure is shown as follows. First, the time window of raw data of the aero-engine is used as the input of DCNN after normalization. The role of DCNN is to extract information from the input data. Second, considering the limitations of the fully connected layer of DCNN, we replace it with a strong classifier-LightGBM to improve the accuracy of prediction. Finally, to prove the effectiveness of the proposed method, we conducted some experiments on the C-MAPSS data set provided by NASA, and obtained good accuracy. By comparing the prediction effect with other commonly used algorithms on the same data set, the proposed algorithm has obvious advantages.

scholarly journals Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics

2019 ◽  
Vol 9 (19) ◽  
pp. 4156 ◽  
Author(s):  
Zhengmin Kong ◽  
Yande Cui ◽  
Zhou Xia ◽  
He Lv
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
Complex Data ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory

Reliable prediction of remaining useful life (RUL) plays an indispensable role in prognostics and health management (PHM) by reason of the increasing safety requirements of industrial equipment. Meanwhile, data-driven methods in RUL prognostics have attracted widespread interest. Deep learning as a promising data-driven method has been developed to predict RUL due to its ability to deal with abundant complex data. In this paper, a novel scheme based on a health indicator (HI) and a hybrid deep neural network (DNN) model is proposed to predict RUL by analyzing equipment degradation. Explicitly, HI obtained by polynomial regression is combined with a convolutional neural network (CNN) and long short-term memory (LSTM) neural network to extract spatial and temporal features for efficacious prognostics. More specifically, valid data selected from the raw sensor data are transformed into a one-dimensional HI at first. Next, both the preselected data and HI are sequentially fed into the CNN layer and LSTM layer in order to extract high-level spatial features and long-term temporal dependency features. Furthermore, a fully connected neural network is employed to achieve a regression model of RUL prognostics. Lastly, validated with the aid of numerical and graphic results by an equipment RUL dataset from the Commercial Modular Aero-Propulsion System Simulation(C-MAPSS), the proposed scheme turns out to be superior to four existing models regarding accuracy and effectiveness.

scholarly journals Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2135
Author(s):  
Marcin Witczak ◽  
Marcin Mrugalski ◽  
Bogdan Lipiec
Keyword(s):  
Historical Data ◽  
Field Effect Transistors ◽  
Failure Process ◽  
Practical Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Data Repository ◽  
Uncertainty Interval ◽  
Useful Life ◽  
Takagi Sugeno

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

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%.

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