Accurate Remaining Useful Life Prediction Research Articles

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries

Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency. The model’s name reflects its precision (“AccuCell”) and predictive strength (“Prodigy”). The proposed methodology involves preparing a dataset of battery operational features, split using an 80–20 ratio for training and testing. Leveraging 22 variations of current (critical parameter) across three Li-ion cells, AccuCell Prodigy significantly reduces prediction errors, achieving a mean square error of 0.1305%, mean absolute error of 2.484%, and root mean square error of 3.613%, with a high R-squared value of 0.9849. These results highlight its robustness and potential for advancing battery health management.

Remaining Useful Life Prediction Method Based on Dual-Path Interaction Network with Multiscale Feature Fusion and Dynamic Weight Adaptation

In fields such as manufacturing and aerospace, remaining useful life (RUL) prediction estimates the failure time of high-value assets like industrial equipment and aircraft engines by analyzing time series data collected from various sensors, enabling more effective predictive maintenance. However, significant temporal diversity and operational complexity during equipment operation make it difficult for traditional single-scale, single-dimensional feature extraction methods to effectively capture complex temporal dependencies and multi-dimensional feature interactions. To address this issue, we propose a Dual-Path Interaction Network, integrating the Multiscale Temporal-Feature Convolution Fusion Module (MTF-CFM) and the Dynamic Weight Adaptation Module (DWAM). This approach adaptively extracts information across different temporal and feature scales, enabling effective interaction of multi-dimensional information. Using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset for comprehensive performance evaluation, our method achieved RMSE values of 0.0969, 0.1316, 0.086, and 0.1148; MAPE values of 9.72%, 14.51%, 8.04%, and 11.27%; and Score results of 59.93, 209.39, 67.56, and 215.35 across four different data categories. Furthermore, the MTF-CFM module demonstrated an average improvement of 7.12%, 10.62%, and 7.21% in RMSE, MAPE, and Score across multiple baseline models. These results validate the effectiveness and potential of the proposed model in improving the accuracy and robustness of RUL prediction.

Remaining useful life prediction of high-capacity lithium-ion batteries based on incremental capacity analysis and Gaussian kernel function optimization

Remaining useful life (RUL) is a key indicator for assessing the health status of lithium (Li)-ion batteries, and realizing accurate and reliable RUL prediction is crucial for the proper operation of battery systems. As high-capacity Li batteries have more complex chemical properties, most of the current RUL prediction methods rely mainly on a priori knowledge to make judgments. As a result, prediction accuracy is not high. In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized its kernel function to achieve accurate RUL prediction for 280 Ah high-capacity Li batteries. Validation against the United States National Aeronautics and Space Administration’s four battery test datasets showed that the use of the HI-C dual GPR model resulted in a mean absolute percentage error and root mean square error of less than 0.02 and 0.04, respectively, for the four battery-rated capacity predictions. Additionally, this model achieved an absolute error of less than five battery failure turns. Compared with a single model, the HI-C dual GPR model not only had high accuracy but also solved the problem that the HI was not measurable in the actual battery operation, which made it more suitable for RUL prediction of Li batteries.

A remaining useful life prediction method based on DATCN-PSOSEN for turbofan engines

Abstract Turbofan engine is a crucial operational component of aircraft. Remaining useful life (RUL) prediction is important for the stable and reliable operation of the turbofan engine. High-dimensional and large-capacity monitoring data of turbofan engines pose a considerable challenge to accurate RUL prediction. A novel approach based on dual-attention temporal convolutional network (DATCN) and particle swarm optimization with selective ensemble (PSOSEN) is proposed in this paper. In the first stage, DATCN is utilized to explore the internal correlations among various input features and different time steps in monitoring data, highlighting the degradation information from two dimensions. In the second stage, PSOSEN is developed to prune base models, excluding the ones with poor performance and assigning varying weights to the others, leading to selectively ensembled prediction results. The experimental results on the C-MAPSS aero-engine degradation dataset validate the effectiveness of the proposed DATCN-PSOSEN method and show improvements of RUL prediction accuracy by 14.2% compared with other methods.

Sparse Graph Structure Fusion Convolutional Network for Machinery Remaining Useful Life Prediction

Effective prediction of machinery remaining useful life (RUL) is prominent to achieve intelligent preventive maintenance in manufacturing systems. In this paper, a sparse graph structure fusion convolutional network (SGSFCN) is proposed for more accurate end-to-end RUL prediction of machine. A novel node-level graph structure called time series shapelet distance graph (TSSDG) is designed to convert the time series to node feature. The SGSFCN model is proposed to learn degradation information from the graph structure. In SGSFCN, a sparse graph structure (SGS) layer and a fusion graph structure (FGS) layer preceding the graph convolutional network (GCN) are designed to learn the SGS from node representation and fuse the original graph structure, enabling the graph structure and node update iteratively in subsequent layers. Concurrently, a bidirectional long short-term memory network (BiLSTM) layer is integrated to capture the global temporal dependencies. The method is validated by two test rig data, and results demonstrate that the proposed method offers significantly higher prediction accuracy of RUL compared to several state-of-art methods.

Remaining Useful Life Prediction of Rolling Bearings Based on Adaptive Continuous Deep Belief Networks and Improved Kernel Extreme Learning Machine

ABSTRACTRolling bearings are crucial components in a wide variety of machinery. Monitoring their conditions and predicting their remaining useful life (RUL) is vital to prevent unexpected breakdowns, optimize maintenance schedules, and reduce operational costs. This article proposes an approach based on adaptive continuous deep belief networks (ACDBN) and improved kernel extreme learning machine (KELM) to predict the RUL of rolling bearings. In the proposed approach, the ACDBN model is used for extracting hidden fault features and the distance between the initial health state and the real‐time degradation state is used to construct a health indicator (HI). Then, a hybrid kernel extreme learning machine prediction model optimized by the sparrow search algorithm (SSA‐KELM) is proposed to estimate the RUL using the extracted HIs. The SSA is used to find the optimal parameters of the KELM model. The proposed method has been assessed using existing bearing datasets. The obtained results indicate that the proposed method successfully improves RUL prediction accuracy compared to existing approaches in the literature.

Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention

Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.

Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: A comprehensive review

As artificial intelligence (AI) technology evolves, data-driven approaches are gaining attention in predicting lithium-ion battery's remaining useful life (RUL). Indeed, accurate RUL prediction is challenging, primarily because of the complex nature of the work and dynamic shifts in model parameters. To address these challenges, this article comprehensively explores five significant publicly accessible lithium-ion battery datasets, encompassing diverse usage conditions and battery types, offering researchers a rich repository of experimental data. In particular, we not only provide detailed information and access addresses for each dataset, but also present, for the first time, four innovative methods for battery aging health factor extraction. These methods, based on advanced AI techniques, are able to effectively identify and quantify key indicators of battery performance degradation, thereby enhancing the precision and dependability of RUL prediction. Additionally, the article identifies major challenges faced by current predictive techniques, including data quality, model generalization capabilities, and computational cost, highlighting the need for research focused on dataset diversity, multiple algorithm fusion, and hybrid physical-data-driven models to enhance prediction accuracy. We believe that this review will help researchers gain a comprehensive understanding of RUL estimation methods and promote the development of AI in battery.

An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction

The battery management system (BMS) plays a critical role in electric vehicles (EVs), with the prediction of remaining useful life (RUL) being of utmost importance. This functionality enables real-time monitoring and enhances driver assistance through intelligent driving mechanisms. To improve the precision of stack voltage degradation and RUL prognostication for proton exchange membrane fuel cell (PEMFC) aging, especially with limited real-time training data, we propose a novel approach using a layer-enhanced quantum long short-term memory model with transfer learning. The proposed model is trained offline using historical stack voltage degradation data from a source stack, which is then fine-tuned and transferred to a target stack. Comparative evaluations demonstrate the model's robustness, with consistently low root mean square errors in stack voltage predictions, even with increased training data. The proposed model significantly improves RUL prediction accuracy, achieving a maximum relative accuracy enhancement of 1.33 % when initiating predictions at 712 h. These results affirm the effectiveness of our approach in enhancing the accuracy of PEMFC aging prognosis, thereby offering valuable insights for practical integration within the BMS framework for EVs.

TransRUL: A Transformer-Based Multihead Attention Model for Enhanced Prediction of Battery Remaining Useful Life

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.

An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction

ABSTRACT Remaining Useful Life (RUL) prediction of rolling bearings is one of the intricate and important issues for equipment intelligent maintenance and health management. Various machine learning models and methods have been applied to rolling bearing RUL prediction. However, a single model cannot effectively extract state information and obtain accurate prediction results, and its generalisation is not stable under the condition of small sample data. Therefore this paper proposes an intelligent hybrid deep learning model for achieving accurate RUL prediction of rolling bearings. Firstly, the one-dimensional vibration signal is transformed into the corresponding two-dimensional time-frequency diagram via Continuous Wavelet Transform (CWT). Secondly, the diagram is input into a Multilayer Perceptron (MLP) consisting of a basic three-layer feed-forward network to obtain a one-dimensional feature vector. And lastly, the obtained feature vector is input into an integrated model based on Deep Autoregressive and Transformer to produce the probability distribution and obtain the prediction results of rolling bearing RUL. Extensive experiments on two rolling bearing datasets show that the proposed model outperforms six other comparative models in extracting bearing fault features and predicting bearing RUL, which demonstrates that the proposed model can effectively extract bearing fault features and accurately predict bearing remaining useful life.

Remaining useful life prediction across operating conditions based on deep subdomain adaptation network considering the weighted multi-source domain

As one of the core contents of transfer learning, domain adaptation (DA) has been widely used to predict of the remaining useful life (RUL) of rolling bearings across operating conditions. However, existing RUL prediction methods often adopt single-source DA and do not consider the sample's drift phenomenon and migration degree in the source domain. At the same time, the distribution of bearing samples in different subdomains (degradation stages) differs, forcing global adaptation to inevitably lead to negative migration. Therefore, a deep subdomain adaptation network considering weighted multi-source domain (DSAN-WM) is proposed to improve the prediction accuracy of RUL across operating conditions. Firstly, a migration metric is developed to measure the migration degree of samples in the source domain, and a high-quality multi-source domain sample set is constructed. Secondly, a fine-grained feature distribution alignment strategy for subdomains based on adaptive degradation stage identification is proposed to reduce the difference in sample distribution in the same subdomain. Thirdly, for each decision boundary obtained by the multi-source domain model, consistent alignment and degradation constraints based on physical knowledge are established to improve the consistency and prediction accuracy of each regressor output. Lastly, the effectiveness of the proposed method is verified using eight migration scenarios of two bearing data sets, and the superiority of the method is proven through a comparison with advanced deep learning and transfer learning approaches.

A knowledge-data integration framework for rolling element bearing RUL prediction across its life cycle

Prediction of Remaining Useful Life (RUL) for Rolling Element Bearings (REB) has attracted widespread attention from academia and industry. However, there are still several bottlenecks, including the effective utilization of multi-sensor data, the interpretability of prediction models, and the prediction across the entire life cycle, which limit prediction accuracy. In view of that, we propose a knowledge-based explainable life-cycle RUL prediction framework. First, considering the feature fusion of fast-changing signals, the Pearson correlation coefficient matrix and feature transformation objective function are incorporated to an Improved Graph Convolutional Autoencoder. Furthermore, to integrate the multi-source signals, a Cascaded Multi-head Self-attention Autoencoder with Characteristic Guidance is proposed to construct health indicators. Then, the whole life cycle of REB is divided into different stages based on the Continuous Gradient Recognition with Outlier Detection. With the development of Measurement-based Correction Life Formula and Bidirectional Recursive Gated Dual Attention Unit, accurate life-cycle RUL prediction is achieved. Data from self-designed test rig and PHM 2012 Prognostic challenge datasets are analyzed with the proposed framework and five existing prediction models. Compared with the strongest prediction model among the five, the proposed framework demonstrates significant improvements. For the data from self-designed test rig, there is a 1.66 % enhancement in Corrected Cumulative Relative Accuracy (CCRA) and a 49.00 % improvement in Coefficient of Determination (R2). For the PHM 2012 datasets, there is a 4.04 % increase in CCRA and a 120.72 % boost in R2.

Model-data hybrid driven approach for remaining useful life prediction of cutting tool based on improved inverse Gaussian process

In the cutting process, reasonable evaluation of tool remaining useful life (RUL) is essential to ensure machining stability and safety. However, traditional data-driven methods rely on datasets with complete degradation data and prior knowledge, which are usually difficult to obtain in real scenarios. For these problems, this paper proposed a model-data hybrid driven approach for RUL prediction of cutting tool based on improved inverse Gaussian process. The tool wear degradation is considered as a random process, the cutting physical model and the data-driven stochastic process model are fused, so that the prior knowledge from the physical model and the statistical law of measured data are fully utilized to improve the accuracy of tool RUL prediction. The cutting physical model is established, and the prior knowledge of tool wear degradation is obtained based on physical simulation method. A data-driven random effects inverse Gaussian process model is constructed for tool RUL modeling and prediction. According to Bayesian method, the unknown parameters are estimated and updated integrating physical simulated and measured wear data, so as to obtain the probability density function for specific tool RUL, and quantify the prediction uncertainty. Finally, comparative experiments are conducted to validate the performance of the proposed method. The results suggest that the hybrid approach has the best performance in different tool RUL prediction.

A novel interactive prognosis framework with nonlinear Wiener process and multi-sensor fusion for remaining useful life prediction

Accurate remaining useful life (RUL) prediction plays a vital role in increasing the system operation safety and reducing maintenance costs. In industrial applications, there is usually a large amount of multi-sensor data generated. Therefore, how to construct an appropriate health index (HI) based on multi-sensor signals is very important for the RUL prediction. However, existing works treat sensor selection, HI construction, and degradation modeling independently as unrelated parts, which may result in the combination of sensors selected not constituting an optimal HI or the constructed HI not matching the degradation model. In addition, most existing works treat prior units as a whole to obtain a unique set of sensor combinations and fusion coefficients, which cannot reflect unit-to-unit heterogeneity, thus affecting the accuracy of RUL prediction. Therefore, a novel interactive feedback framework is established to construct HI, where the sensor selection, fusion coefficient calculation, and nonlinear Wiener process degradation modeling are incorporated into the feedback. Furthermore, an adaptive weight selection method based on particle swarm optimization and leave-one-out cross-validation (PSO-LV) is proposed to adjust the fusion coefficients in real-time. Then, the RUL is estimated by updating model parameters online, detecting degradation trends, and deriving the probability density function (PDF) of the RUL. Finally, two examples of engine datasets are provided to verify the effectiveness of the proposed method.

An attention-based multi-scale temporal convolutional network for remaining useful life prediction

Remaining Useful Life (RUL) prediction is one of the key technologies in Prognostics and Health Management (PHM). Accurate RUL prediction is crucial for equipment reliability, and equipment sensor data contains information at different scales that can be used for RUL prediction. Current mainstream methods usually extract features from only a single scale, neglecting the information from other scales. Additionally, the attention mechanisms used in these methods do not incorporate multi-dimensional information, which can lead to a loss in prediction performance. To address this issue, this paper proposes an end-to-end framework which utilizes Multi-Scale Temporal Convolutional Network (MSTCN) to extract multi-scale features and introduces Self-Attention (SA) and Global Fusion Attention (GFA) mechanisms to assist MSTCN's work. SA is located at the head of MSTCN, emphasizing the features of those steps that are particularly important, thereby improving the extraction efficiency of MSTCN. GFA is an original design of attention mechanism located at the end of MSTCN that intelligently integrates multi-dimensional attention information, suppressing redundant information in the multi-scale features. To validate the effectiveness of the proposed method, we conduct experiments on the C-MAPSS dataset released by NASA. The results show that our method outperforms other state-of-the-art prediction methods in terms of RUL prediction performance.

Remaining useful life prediction of machinery based on improved Sample Convolution and Interaction Network

Remaining useful life (RUL) prediction is significant in ensuring the safe and reliable operation of machinery and reducing maintenance costs. There are currently numerous deep learning-based methods for machinery RUL prediction. However, some studies overlook the differences in the contributions of data from different sensors or different time points of the same sensor, and most research only extracts information from feature or sequence dimensions, which inevitably affects the efficiency and accuracy of RUL prediction. Therefore, we proposed a method based on a multi-dimensional attention mechanism and feature-sequence dimensional sample convolution and interaction network (MFSSCINet) to predict the machinery RUL effectively, which includes a Feature-Sequence Dimension Attention Module to capture information interactions in feature dimension and learn the impact weights of various time steps in sequence dimension. Then, a Multi-Source Information Fusion Module was constructed to extract helpful information from features of different dimensions and time resolutions and fuse them. Finally, a RUL Prediction Module was built to estimate the machine RUL effectively. The method’s effectiveness is validated in C-MAPSS and XJTU-SY datasets. Experimental results show that the MFSSCINet model has higher accuracy in machine RUL prediction tasks than other advanced computational methods.

A Data-driven Deep Learning Approach for Remaining Useful Life in the ion mill etching Process

Prognostics and Health Management (PHM) is regarded as an essential element in the scope of intelligent manufacturing. Precise forecasting of the remaining useful life (RUL) of an ion mill is crucial in order to enhance the overall efficiency of the ion mill etching (IME) procedure. This paper proposed a Data-driven Deep Learning (DL) framework that integrates a Temporal Convolution Network (TCN), Long Short-Term Memory (LSTM), and self-attention mechanism to improve the accuracy of RUL prediction in the ion mill etching Process. Initially, sensor input data is divided into two parallel paths - one with TCN blocks for capturing long-range dependencies, and the other with LSTM layers for extracting temporal patterns. The outputs from both paths are then merged and input into an LSTM layer for enhanced learning, followed by a self-attention mechanism to highlight important features then fully connected layer for predicting RUL. The efficacy of this suggested model was assessed through the utilization of the 2018 PHM Data Challenge Dataset and juxtaposed against various Deep Learning models to demonstrate its efficacy. The results from the experiments indicate that ATCN-LSTM serves as a robust option for estimating the RUL in the ion mill etching Process as it outperformed all other models that were compared. The source code is publicly accessible at https://github.com/ion-mill-etching-Process.

Design of regression neural network model for estimating the remaining useful life of lithium-ion battery

Lithium-ion batteries are widely utilized in a variety of transportation sectors, including highways, airplanes, and defensive military applications because of their positive features, which include a low self-discharge rate, raised operating voltage, prolonged cycle life, and high energy density. Nevertheless, during operation, the battery undergoes adverse reactions that may eventually cause material aging and capacity deterioration. For this reason, the prediction of remaining useful life (RUL) for LiBs is important and necessary for ensuring reliable system operation. Moreover, accurate RUL prediction can effectively offer maintenance strategies to certify the system’s dependability and safety. The impedance of the battery plays a vital role in the degradation process and hence its measurement accounts for the changes in the battery’s internal parameters as aging occurs. The objective of this paper is to create a forecasting model for the lifespan of batteries through the application of data analysis. Moreover, it aims to use a Regression Neural Network (RNN) based mathematical model to assess the degradation of the battery across diverse operational scenarios. The co-efficient of the RNN model is found by solving the RNN equations. In this research, MATLAB is employed for data analysis, utilizing open-source battery data sourced from the NASA dataset. The conclusive prediction outcomes indicate the effectiveness of the proposed methodology in accurately forecasting the battery RUL.

A dual-model adaptive Kalman filtering for remaining useful life prediction method based on feature fusion and online TSP recognition

Remaining Useful Life (RUL) prediction is of significant importance for the safe operation of machines. However, the Health Indicators (HIs) constructed by a single feature of machines cannot precisely describe the non-stationary and accelerating degradation operation. Moreover, poor identification of Time to Start Prediction (TSP), and the limited adaptability of models restrict the accuracy of RUL prediction. A Dual-Model Adaptive Kalman Filter (DMAKF) RUL prediction method based on feature fusion and online TSP recognition is proposed. Firstly, a feature-fusion strategy is proposed, integrating various information to construct distinguishing HIs. Secondly, a statistic-based change-point detection approach is investigated to online recognize TSP, balancing the sensitivity and interference immunity. Finally, the DMAKF is conducted for adaptive adjustment of RUL prediction in real-time. The proposed method is validated on two sets of bearing datasets from different sources. Comparison with four other methods demonstrates the effectiveness of the proposed method.