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.

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life

Accurate estimation of the remaining useful life(RUL)of lithium-ion batteries is critical for their large-scale deployment as energy storage devices in electric vehicles and stationary storage.A fundamental understanding of the factors affecting RUL is crucial for accelerating battery technology development.However,it is very challenging to predict RUL accurately because of complex degradation mechanisms occurring within the batteries,as well as dynamic operating conditions in practical applications.Moreover,due to insignificant capacity degradation in early stages,early prediction of battery life with early cycle data can be more difficult.In this paper,we propose a hybrid deep learning model for early prediction of battery RUL.The proposed method can effectively combine handcrafted features with domain knowledge and latent features learned by deep networks to boost the performance of RUL early prediction.We also design a non-linear correlation-based method to select effective domain knowledge-based features.Moreover,a novel snapshot ensemble learning strategy is proposed to further enhance model generalization ability without increasing any additional training cost.Our experimental results show that the proposed method not only outperforms other approaches in the primary test set having a similar distribution as the training set,but also generalizes well to the secondary test set having a clearly different distribution with the training set.The PyTorch implementation of our proposed approach is available at https://github.com/batteryrul/battery_rul_early_prediction.

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.

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.

Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks

Accurate estimation of the remaining useful life(RUL)and health state for rollers is of great significance to hot rolling production.It can provide decision support for roller management so as to improve the productivity of the hot rolling process.In addition,the RUL prediction for rollers is helpful in transitioning from the current regular maintenance strategy to conditional-based maintenance.Therefore,a new method that can extract coarse-grained and fine-grained features from batch data to predict the RUL of the rollers is proposed in this paper.Firstly,a new deep learning network architecture based on recurrent neural networks that can make full use of the extracted coarsegrained fine-grained features to estimate the heath indicator(HI)is developed,where the HI is able to indicate the health state of the roller.Following that,a state-space model is constructed to describe the HI,and the probabilistic distribution of RUL can be estimated by extrapolating the HI degradation model to a predefined failure threshold.Finally,application to a hot strip mill is given to verify the effectiveness of the proposed methods using data collected from an industrial site,and the relatively low RMSE and MAE values demonstrate its advantages compared with some other popular deep learning methods.

Remaining useful life prediction for a nonlinear multi-degradation system with public noise

To predict the remaining useful life(RUL) for a class of nonlinear multi-degradation systems, a method is presented. In the real industrial processes, systems are usually composed by several parts or components, and these parts or components are working in the same environment, thus the degradations of these parts or components will be influenced by common factors. To describe such a phenomenon in degradations, a multi-degradation model with public noise is proposed. To identify the degradation states and the unknown parameters, an iterative estimation method is proposed by using the Kalman filter and the expectation maximization(EM) algorithm. Next, with known thresholds,the RUL of each degradation can be predicted by using the first hitting time(FHT). In addition, the RUL of the whole system can be obtained by a Copula function. Finally, a practical case is used to demonstrate the method proposed.

A Novel Product Remaining Useful Life Prediction Approach Considering Fault Effects

In this paper,a novel remaining useful life prediction approach considering fault effects is proposed.The Wiener process is used to construct the degradation process of single performance characteristic with the fault effects.The first passage time based remaining useful life distribution is calculated by assuming fault occurrence moment is a random variable and follows a certain distribution.Expectation maximization algorithm is employed to estimate model parameters,where the fault occurrence moment is considered as a missing data.Finally,a Copula function is used to describe the dependence between the multiple performance characteristics and derive joint remaining useful life(RUL)distribution of product with the fault effects.The effectiveness of the proposed approach is verified by the experiments of turbofan engines.

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.

Remaining Useful Life Prediction of Rolling Element Bearings Based on Different Degradation Stages and Particle Filter

A method is proposed to improve the accuracy of remaining useful life prediction for rolling element bearings,based on a state space model(SSM)with different degradation stages and a particle filter.The model is improved by a method based on the Paris formula and the Foreman formula allowing the establishment of different degradation stages.The remaining useful life of rolling element bearings can be predicted by the adjusted model with inputs of physical data and operating status information.The late operating trend is predicted by the use of the particle filter algorithm.The rolling bearing full life experimental data validate the proposed method.Further,the prediction result is compared with the single SSM and the Gamma model,and the results indicate that the predicted accuracy of the proposed method is higher with better practicability.

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.

Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Health Indicator Extraction and Trajectory Enhanced Particle Filter

Aiming at the difficulty of mining fault prognosis starting points and constructing prognostic models for remaining useful life(RUL)prediction of rolling bearings,a RUL prediction method is proposed based on health indicator(HI)extraction and trajectory-enhanced particle filter(TE-PF).By extracting a HI that can accurately track the trending of bearing degradation and combining it with the early fault enhancement technology,early abnormal sample nodes can be mined to provide more samples with fault information for the construction and training of subsequent prediction models.Aiming at the problem that traditional degradation rate models based on PF are vulnerable to HI mutations,a TE-PF prediction method is proposed based on comprehensive utilization of historical degradation information to timely modify prediction model parameters.Results from a rolling bearing prognostic study show that prediction starting points can be accurately detected and a reasonable prediction model can be conveniently constructed by the RUL prediction method based on HI amplitude abnormal detection and TE-PF.Furthermore,aiming at the RUL prediction problem under the condition of HI mutation,RUL prediction with probability and statistics characteristics under a confidence interval can be obtained based on the method proposed.

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.

Effective Latent Representation for Prediction of Remaining Useful Life

AI approaches have been introduced to predict the remaining useful life(RUL)of a machine in modern industrial areas.To apply them well,challenges regarding the high dimension of the data space and noisy data should be met to improve model efficiency and accuracy.In this study,we propose an end-toend model,termed ACB,for RUL predictions;it combines an autoencoder,convolutional neural network(CNN),and bidirectional long short-term memory.A new penalized root mean square error loss function is included to avoid an overestimation of the RUL.With the CNN-based autoencoder,a high-dimensional data space can be mapped into a lower-dimensional latent space,and the noisy data can be greatly reduced.We compared ACB with five state-of-the-art models on the Commercial Modular Aero-Propulsion System Simulation dataset.Our model achieved the lowest score value on all four sub-datasets.The robustness of our model to noise is also supported by the experiments.

Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold

The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life(RUL) of equipment. Most of the existing studies on the RUL prediction assume that the failure threshold is a fixed value,as they have difficulty in reflecting the random variation of the failure threshold. In connection with the inadequacies of the existing research, an in-depth analysis is carried out to study the effect of the random failure threshold(RFT) on the prediction results for the RUL. First, a nonlinear degradation model with unit-to-unit variability and measurement error is established based on the nonlinear Wiener process. Second, the expectation-maximization(EM) algorithm is used to solve the estimated values of the parameters of the prior degradation model, and the Bayesian method is used to iteratively update the posterior distribution of the random coefficients. Then, the effects of three types of RFT constraint conditions on the prediction results for the RUL are analyzed, and the probability density function(PDF) of the RUL is derived. Finally,the degradation data of aero-turbofan engines are used to verify the correctness and advantages of the method.

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

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.