A Light Gradient Boosting Machine for Remainning Useful Life Estimation of Aircraft Engines

Li, Fēi; Zhang, Li; Chen, Bin; Gao, Dianzhu; Cheng, Yijun; Zhang, Xiaoyong; Yang, Yingze; Gao, Kai; Huang, Zhiwu; Peng, Jun

doi:10.1109/itsc.2018.8569801

Cited by 44 publications

(23 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7(b). According to [41] based on the CMAPSS dataset, the largest RUL value in piece-wise linear function was set to 125.…”

Section: Rul Target Fuctionmentioning

confidence: 99%

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

2019

IEEE Access

200

View full text Add to dashboard Cite

Accurate and timely prediction of remaining useful life (RUL) of a machine enables the machine to have an appropriate operation and maintenance decision. Data-driven RUL prediction methods are more attractive to researchers because they can be deployed quicker and cheaper compared to other approaches. The existing deep neural network (DNN) models proposed for the applications of RUL prediction are mostly single-path and top-down propagation. In order to improve the prognostic accuracy of the network, this paper proposes a directed acyclic graph (DAG) network that combines long short term memory (LSTM) and a convolutional neural network (CNN) to predict the RUL. Different from the existing prediction models combined with CNN and LSTM, the method proposed in this paper combines CNN and LSTM organically instead of just using CNN for feature extraction. Moreover, when a single timestamp is used as an input, padding the signals in the same training batch would affect the prediction ability of the developed model. To overcome this drawback, the proposed method generates a short-term sequence by sliding the time window (TW) with one step size. In addition, based on the degradation mechanism, the piece-wise RUL function is used instead of the traditional linear function. In the experimental test, the turbofan engine degradation simulation dataset provided by NASA is used to validate the proposed RUL prediction model. By comparing with the existing methods using the same dataset, it can be concluded that the prediction method proposed in this paper has better prediction capability. INDEX TERMS Remaining useful life prediction, long-short-term memory network, convolutional neural networks, turbofan engine.

show abstract

“…7(b). According to [41] based on the CMAPSS dataset, the largest RUL value in piece-wise linear function was set to 125.…”

Section: Rul Target Fuctionmentioning

confidence: 99%

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

2019

IEEE Access

200

View full text Add to dashboard Cite

show abstract

“…Gradient boosting decision tree (GBDT) is an integrated algorithm based on residual iterative tree [26], which has been widely applied to click-through rate prediction. Extreme gradient boosting (XGBoost) [22] and Light gradient boosting machine (LightGBM) [16] are variants of GBDT. they have achieved good results on other prediction problems.…”

Section: Methodsmentioning

confidence: 99%

“…Gradient boosting and random forest algorithms were used to predict the RUL of a turbofan engine and yielded good prediction accuracy [15]. A variant of the gradient boosting method, the light gradient boosting machine was used to estimate RUL, which is characterized by the rapidity of the method and the redundancy of noise [16]. But the tree structure can be insensitive to the slight degradation in the time series data.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Stacking Ensemble for Remaining Useful Life Estimation of Systems Under Multi-Operating Conditions

Zhang

Chen

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

Remaining useful life (RUL) estimation is expected to provide appropriate maintenance for components or systems in industry to improve the reliability of the systems. Most data-based methods are limited to a single model, which is susceptible to various factors like environmental variability and diversity of operating conditions. In this paper, we propose an optimal stacking ensemble method combining different learning algorithms as meta-learners to mitigate the impact of multi-operating conditions. The selection of meta-learners follows a multi-objective evolutionary algorithm named non-dominated sorting genetic algorithms-II to balance the two conflicting objectives in terms of accuracy and diversity. Then the eventually evolved meta-learners are integrated by the meta-classifier for RUL estimation. In addition, a long-short-term feature extraction strategy is proposed to capture more degradation information from lifecycle data dynamically. Extensive experiments are performed on aero-engine dataset and battery dataset provided by NASA, which achieves the higher prognostic accuracy compared with the single models and existing methods. INDEX TERMS Remaining useful life (RUL), reliability, optimal stacking ensemble, meta-learners.

show abstract

“…In the paper, the LightGBM method is used to select the optimal features. The literature has confirmed that LightGBM is on the top in machine learning in terms of computational accuracy and running speed, which is especially suitable for the processing of big data [ 31 , 32 ]. LightGBM proposed by Ke et al [ 33 ] is a highly efficient gradient boosting decision tree (GBDT), including two algorithms: gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB).…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations

Yao

Zhang

Chen

et al. 2020

Sensors

View full text Add to dashboard Cite

Inefficient remaining useful life (RUL) estimation may cause unpredictable failures and unscheduled maintenance of machining tools. Multi-sensor data fusion will improve the RUL prediction reliability by fusing more sensor information related to the machining process of tools. In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency domain by such methods as statistical analysis, power spectrum density analysis and Hilbert-Huang transform, feature selection by a Light Gradient Boosting Machine method, feature fusion by a tool wear prediction model based on back propagation neural network optimized by improved artificial bee colony (IABC-BPNN) algorithm, and the online RUL prediction model by a polynomial curve fitting method. An example is used to verify whether if the prediction performance of the proposed system is stable and reliable, and the results show that it is superior to its rivals.

show abstract

A Light Gradient Boosting Machine for Remainning Useful Life Estimation of Aircraft Engines

Cited by 44 publications

References 17 publications

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

An Optimal Stacking Ensemble for Remaining Useful Life Estimation of Systems Under Multi-Operating Conditions

Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations

Contact Info

Product

Resources

About