Remaining Useful Life Prediction of Rail Based on Improved Pulse Separable Convolution Enhanced Transformer Encoder

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.

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

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.

Practical Options for Adopting Recurrent Neural Network and Its Variants on Remaining Useful Life Prediction

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.

Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

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.

Dense-Structured Network Based Bearing Remaining Useful Life Prediction System

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.

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

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.

Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling

Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics.These features have an uncertain effect on the remaining useful life(RUL)prediction of the equipment.The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function.This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model.Based on the historical measured data of similar equipment,the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient.Using the on-site measured data of the target equipment,the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm.The analytical form of the RUL distribution function is derived based on the first hitting time distribution.Combined with the two case studies,the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.

Research on mechanical wear life feature fusion prediction method based on temporal pattern attention mechanism

In order to solve the problem of low prediction accuracy when only vibration or oil signal is used to predict the remaining life of gear wear,a gear wear life feature fusion prediction method based on temporal pattern attention mechanism is proposed.Firstly,deep residual shrinkage network(DRSN)is used to extract the features of the original vibration time series signals with low signal-tonoise ratio,and the vibration features associated with gear wear evolution are obtained.Secondly,the extracted vibration features and the oil monitoring data that can intuitively reflect the wear process information are jointly input into the bi-directional long short-term memory neural network based on temporal pattern attention mechanism(TPA-BiLSTM),the complex nonlinear relationship between vibration features,oil features and gear wear process evolution is further explored to improve the prediction accuracy.The gear life cycle dynamic response and wear process signals are obtained based on the gear numerical simulation model,and the feasibility of the proposed method is verified.Finally,the proposed method is applied to the residual life prediction of gear on a test bench,and the comparison between different methods proved the validity of the proposed method.

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure.In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.

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

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.

Application of Digital Twin in Smart Battery Management Systems

Lithium-ion batteries have always been a focus of research on new energy vehicles,however,their internal reactions are complex,and problems such as battery aging and safety have not been fully understood.In view of the research and preliminary application of the digital twin in complex systems such as aerospace,we will have the opportunity to use the digital twin to solve the bottleneck of current battery research.Firstly,this paper arranges the development history,basic concepts and key technologies of the digital twin,and summarizes current research methods and challenges in battery modeling,state estimation,remaining useful life prediction,battery safety and control.Furthermore,based on digital twin we describe the solutions for battery digital modeling,real-time state estimation,dynamic charging control,dynamic thermal management,and dynamic equalization control in the intelligent battery management system.We also give development opportunities for digital twin in the battery field.Finally we summarize the development trends and challenges of smart battery management.

In-service aircraft engines turbine blades life prediction based on multi-modal operation and maintenance data

The in-service life of turbine blades directly affects the on-wing lifetime and operating cost of aircraft engines.It would be essential to accurately evaluate the remaining useful life of turbine blades for safe engine operation and reasonable maintenance decision-making.In this paper,a machine learning-based mechanism with multiple information fusion is proposed to predict the remaining useful life of high-pressure turbine blades.The developed method takes account of the in-service operating factors such as the high-pressure rotor speed and exhaust gas temperature,as well as the engine operating environments and performance degradation.The effectiveness of this method is demonstrated on simulated test cases generated by an integrated blade creep-life assessment model,which comprises engine performance,blade stress,thermal,and creep life estimation models.The results show that the proposed method provides a prospective result for in-service life evaluation of turbine blades and is of significance to evaluating the engine on-wing lifetime and making a reasonable maintenance plan.

Opportunistic Replacement Optimization for Multi-Component System Based on Programming Theory

It is widely accepted that too excessive or too insufficient maintenance actions on a system are consumptive or potentially risky. This paper focuses on the optimization of opportunistic replacement for a multicomponent system in which no failure or suspension histories can be used for prediction of all the critical components in the system. Firstly, the remaining useful life(RUL) is predicted using the real-time sensor data, which is based on an "individual-based lifetime inference" method. Then a failure risk estimation method is introduced,which is based on the degradation extent and service time of components. Subsequently, the possible replacement combinations of components are compared, which is based on a proposed current-term cost rate. Finally, the best replacement scheduling is selected. The proposed framework is validated by the simulation dataset and PHM-2012 competition bearing dataset. Group replacement and individual replacement are conducted for comparison, and sensitivity analysis is discussed.

A Multitime-scale Deep Learning Model for Lithium-ion Battery Health Assessment Using Soft Parameter-sharing Mechanism

Efficient assessment of battery degradation is important to effectively utilize and maintain battery management systems.This study introduces an innovative residual convolutional network(RCN)-gated recurrent unit(GRU)model to accurately assess health of lithium-ion batteries on multiple time scales.The model employs a soft parameter-sharing mechanism to identify both short-d dT and long-term degradation patterns.The continuously looped(V),T(V),dQ/dV and dT/dV are extracted to form a four-channel image,dV dV from which the RCN can automatically extract the features and the GRU can capture the temporal features.By designing a soft parameter-sharing mechanism,the model can seamlessly predict the capacity and remaining useful life(RUL)on a dual time scale.The proposed method is validated on a large MIT-Stanford dataset comprising 124 cells,showing a high accuracy in terms of mean absolute errors of 0.00477 for capacity and 83 for RUL.Furthermore,studying the partial voltage fragment reveals the promising performance of the proposed method across various voltage ranges.Specifically,in the partial voltage segment of 2.8-3.2 V,root mean square errors of 0.0107 for capacity and 140 for RUL are achieved.