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remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimization

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Transportation Safety and Environment 2024年第2期6卷 75-86页

作者： Deqiang He Jingren Yan Zhenzhen Jin Xueyan Zou Sheng Shan Zaiyu Xiang Jian Miao State Key Laboratory of Featured Metal Materials and life-cycle Safety for Composite Structures Guangxi Key Laboratory of Manufacturing System&Advanced Manufacturing Tec hnologySc hool of Mec hanical EngineeringGuangxi Univ ersityNanning 530004GuangxiChina Zhuzhou CRRC Times Electric Co. Ltd.Zhuzhou 412001HunanChina

remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current prediction accuracy makes it difficult to meet the re-quirements of high reliability operation.Aiming at the problem,a prediction model based on an improved long short-term memory(ILSTM)network is proposed.Firstly,the variational mode decomposition is used to process the signal,the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed com-bined with the time domain characteristics.Then,to improve learning ability,a rectified linear unit(ReLU)is applied to activate a fully connected layer lying after the long short-term memory(LSTM)network,and the hidden state outputs of the layer are weighted by attention mechanism.The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM.Finally,the ILSTM is applied to predict bearing RUL.Through experimental cases,the better perfor-mance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated,and its superiority of hyperparameters setting is demonstrated.

关键词： train bearing remaining useful life prediction long short-term memory(LSTM) attention mechanism Harris Hawks op-timization(HHO)

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remaining useful life prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

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Journal of Transportation Technologies 2024年第2期14卷 137-160页

作者： Zhongmei Wang Min Li Jing He Jianhua Liu Lin Jia College of Railway Transportation Hunan University of Technology Zhuzhou Hunan College of Electrical and Information Engineering Hunan University of Technology Zhuzhou Hunan

In order to prevent possible casualties and economic loss, it is critical to accurate prediction of the remaining useful life (RUL) in rail prognostics health management. However, the traditional neural networks is difficult to capture the long-term dependency relationship of the time series in the modeling of the long time series of rail damage, due to the coupling relationship of multi-channel data from multiple sensors. Here, in this paper, a novel RUL prediction model with an enhanced pulse separable convolution is used to solve this issue. Firstly, a coding module based on the improved pulse separable convolutional network is established to effectively model the relationship between the data. To enhance the network, an alternate gradient back propagation method is implemented. And an efficient channel attention (ECA) mechanism is developed for better emphasizing the useful pulse characteristics. Secondly, an optimized Transformer encoder was designed to serve as the backbone of the model. It has the ability to efficiently understand relationship between the data itself and each other at each time step of long time series with a full life cycle. More importantly, the Transformer encoder is improved by integrating pulse maximum pooling to retain more pulse timing characteristics. Finally, based on the characteristics of the front layer, the final predicted RUL value was provided and served as the end-to-end solution. The empirical findings validate the efficacy of the suggested approach in forecasting the rail RUL, surpassing various existing data-driven prognostication techniques. Meanwhile, the proposed method also shows good generalization performance on PHM2012 bearing data set.

关键词： Equipment Health Prognostics remaining useful life prediction Pulse Separable Convolution Attention Mechanism Transformer Encoder

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Dense-Structured Network Based Bearing remaining useful life prediction System

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Computer Modeling in Engineering & Sciences 2022年第10期131卷 133-151页

作者： Ping-Huan Kuo Ting-Chung Tseng Po-Chien Luan Her-Terng Yau Department of Mechanical Engineering National Chung Cheng UniversityChiayi62102Taiwan Advanced Institute of Manufacturing with High-Tech Innovations(AIM-HI) National Chung Cheng UniversityChiayi62102Taiwan

This work is focused on developing an effective method for bearing remaining useful life predictions.The method is useful in accurately predicting the remaining useful life of bearings so that machine damage,production outage,and human accidents caused by unexpected bearing failure can be prevented.This study uses the bearing dataset provided by FEMTO-ST Institute,Besancon,France.This study starts with the exploration of neural networks,based on which the biaxial vibration signals are modeled and analyzed.This paper introduces pre-processing of bearing vibration signals,neural network model training and adjustment of training data.The model is trained by optimizing model parameters and verifying its performance through cross-validation.The proposed model’s superiority is also confirmed through a comparison with other traditionalmodels.In this study,the neural network model is trained with various types of bearing data and can successfully predict the remaining useful life.The algorithm proposed in this study achieves a prediction accuracy of coefficient of determination as high as 0.99.

关键词： Bearing neural network remaining useful life prediction machine learning

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Position Encoding Based Convolutional Neural Networks for Machine remaining useful life prediction

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1427-1439页

作者： Ruibing Jin Min Wu Keyu Wu Kaizhou Gao Zhenghua Chen Xiaoli Li Institute for Infocomm Research Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore School of Computer Science Liaocheng UniversityLiaocheng 252000 Macao Institute of System Engineering Macao University of Science and TechnologyTaipaMacao 999078China

Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.

关键词： Convolutional neural network(CNN) deep learning position encoding remaining useful life prediction

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Practical Options for Adopting Recurrent Neural Network and Its Variants on remaining useful life prediction

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 32-51页

作者： Youdao Wang Yifan Zhao Sri Addepalli School of Aerospace Transport and ManufacturingCranfield UniversityCranfield MK430ALUK

The remaining useful life(RUL)of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators.Recently,different deep learning(DL)techniques have been used for RUL prediction and achieved great success.Because the data is often time-sequential,recurrent neural network(RNN)has attracted significant interests due to its efficiency in dealing with such data.This paper systematically reviews RNN and its variants for RUL prediction,with a specific focus on understanding how different components(e.g.,types of optimisers and activation functions)or parameters(e.g.,sequence length,neuron quantities)affect their performance.After that,a case study using the well-studied NASA’s C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance.The result suggests that the variant methods usually perform better than the original RNN,and among which,Bi-directional Long Short-Term Memory generally has the best performance in terms of stability,precision and accuracy.Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately.It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance.

关键词： remaining useful life prediction Deep learning Recurrent neural network Long short-term memory Bi-directional long short-term memory Gated recurrent unit

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Residual Convolution Long Short-Term Memory Network for Machines remaining useful life prediction and Uncertainty Quantification

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Journal of Dynamics, Monitoring and Diagnostics 2022年第1期1卷 2-8页

作者： Wenting Wang Yaguo Lei Tao Yan Naipeng Li Asoke KNandi Key Laboratory of Education Ministry for Modern Design&Rotor-Bearing System Xi’an Jiaotong UniversityXi’anChina Department of Electronic and Electrical Engineering Brunel University LondonUxbridgeUK School of Mechanical Engineering Xi’an Jiaotong UniversityXi’anChina

Recently,deep learning(DL)has been widely used in the field of remaining useful life(RUL)prediction.Among various DL technologies,recurrent neural network(RNN)and its variant,e.g.,long short-term memory(LSTM)network,have gained extensive attention for their ability to capture temporal dependence.Although existing RNN-based methods have demonstrated their RUL prediction effectiveness,they still suffer from the following two limitations:1)it is difficult for the RNN to directly extract degradation features from original monitoring data and 2)most RNN-based prognostics methods are unable to quantify RUL uncertainty.To address the aforementioned limitations,this paper proposes a new prognostics method named residual convolution LSTM(RC-LSTM)network.In the RC-LSTM,a new ResNet-based convolution LSTM(Res-ConvLSTM)layer is stacked with a convolution LSTM(ConvLSTM)layer to extract degradation representations from monitoring data.Then,under the assumption that the RUL follows a normal distribution,an appropriate output layer is constructed to quantify the uncertainty of prediction results.Finally,the effectiveness and superiority of the RC-LSTM are verified using monitoring data from accelerated bearing degradation tests.

关键词： Deep learning residual convolution LSTM network remaining useful life prediction uncertainty quantification

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remaining useful life prediction of lithium-ion batteries based on phase space reconstruction and optimized LSTM network

Remaining useful life prediction of lithium-ion batteries ba...

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第40届中国控制会议

作者： Qizheng Qian Yong Zhang Xiujuan Zheng Jin Xie Weiguang Hao School of Information Science and Engineering Wuhan University of Science and Technology

To solve the problems of insufficient adaptability and low prediction accuracy for lithium-ion battery remaining useful life（RUL） prediction,this paper proposes an optimized neural network prediction method to improve prediction ability.Firstly,the original signal is used to decompose and reduce noise by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise（CEEMDAN）,and extract the residual that best reflects the degradation trend as a feature.Secondly,the phase space reconstruction method can determine the most appropriate sliding window size and reconstruct a new sample space to improve prediction accuracy.Thirdly,the genetic algorithm is applied to perform an adaptive global search for the optimal key parameters of Long Short-Term Memory（LSTM） network,which improves the adaptability of the method on RUL prediction.In order to verify effectiveness,the model was actually applied to NASA batteries,and the experimental results show that the proposed method has higher accuracy and generality than other traditional methods.

关键词： remaining useful life prediction Phase space reconstruction Genetic algorithm Long short-term memory

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remaining useful life prediction of Power Batteries based on Multi-observation Fusion

Remaining Useful Life Prediction of Power Batteries based on...

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2021中国汽车工程学会年会暨展览会

作者： Wang Chenxu Xiong Rui Lu Jiahuan Beijing Institute of Technology

As a substitute for fuel,power battery has a short lifespan,which causes users’ anxiety about driving range.Accurate life prediction is also crucial for system maintenance,repair and residual value assessment.Traditional extended Kalman filtering algorithms rely too much on historical data,which results in a poor accuracy on long-term prediction.A multi-observation fusion method is adopted to solve it and to predict the remaining useful life of power batteries.In order to improve its practical ability,the capacity data extracted from working conditions are used with the testing capacity data to jointly modify the aging states’ trajectory of power batteries.Because of the less requirements on the quantity and quality of historical data,the long-term prediction ability and stability are improved.In order to improve the prediction ability of the filtering algorithm,the semi-empirical aging model is introduced to extract prior information from offline experiments to form the reference capacity decline trend.Combined with the semi-empirical model and the extended Kalman filter,the extended Kalman filter based on multi-observation fusion method is constructed.Results show that the RUL prediction results of the proposed method are 60% more accurate than that of the extended Kalman filter method as well as the particle filter method.It converges in the whole lifespan and has a significant advantage in the early RUL prediction.

关键词： electric vehicle lithium ion battery remaining useful life prediction extended Kalman filter

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Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions

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Friction 2024年第6期12卷 1272-1282页

作者： Florian KÖNIG Florian WIRSING Georg JACOBS Rui HE Zhigang TIAN Ming J.ZUO Institute for Machine Elements and Systems Engineering RWTH Aachen UniversityAachen 52062Germany Department of Mechanical Engineering University of AlbertaEdmonton T6G1H9Canada Qingdao International Academician Park Research Institute Qingdao 266000China

This study introduces a method to predict the remaining useful life(RUL)of plain bearings operating under stationary,wear-critical conditions.In this method,the transient wear data of a coupled elastohydrodynamic lubrication(mixed-EHL)and wear simulation approach is used to parametrize a statistical,linear degradation model.The method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient,system-dependent wear progression within the RUL prediction.A case study is used to show the suitability of the proposed method.The results show that the method can be applied to three distinct types of post-wearing-in behavior:wearing-in with subsequent hydrodynamic,stationary wear,and progressive wear operation.While hydrodynamic operation leads to an infinite lifetime,the method is successfully applied to predict RUL in cases with stationary and progressive wear.

关键词： plain bearings wear modeling remaining useful life prediction Bayesian inference

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remaining useful life prediction for Turbofan Engine using SAE-TCN Model

Remaining Useful Life Prediction for Turbofan Engine using S...

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第40届中国控制会议

作者： Yiming Zhang Xiaofeng Liu School of Transportation Science and Engineering Beihang University

Turbofan engines are known as the heart of the aircraft,as important equipment of the aircraft,the health state of the engine determines the aircraft’s operational status.Therefore,the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable operation of the aircraft,and the remaining useful life（RUL） prediction of the engine is an important part of it.The monitoring data of turbofan engines have a high dimension and a long time span,which brings difficulties to predicting the remaining useful life of the engine.This paper proposes a residual life prediction model based on Autoencoder and temporal convolutional network（TCN）.Among them,Autoencoder is used to reduce the dimension of the data and extract features from the engine monitoring data.The obtained low-dimensional data is trained in the TCN network to predict the remaining useful life.The model mentioned in this article is verified on the NASA public dataset（C-MAPSS）and compared with common machine learning methods and other deep neural networks.The experimental results show that the model proposed in this paper performs best in the evaluation methods,and this conclusion has important implications for engine health.

关键词： Deep Learning Turbofan Engine remaining useful life prediction Temporal Convolutional Network Autoencoder Target Generation

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