Remaining Useful Life Estimation of Lithium-Ion Batteries Using Alpha Evolutionary Algorithm-Optimized Deep Learning

The study of intelligent operation and maintenance methods for turbofan engines is of great importance for improving the reliability of turbofan engines. Given the harsh operating conditions and complex structure of the turbofan engine, it is extremely difficult to establish an accurate physical model for remaining useful life (RUL) prediction. The traditional operation and maintenance method based on the physical model has several limitations in the application of turbofan engines, while the data-driven method offers a new solution. Compared with traditional machine learning models, deep learning models possess more powerful nonlinear expression capabilities and feature extraction capabilities. Therefore, this study focuses on studying the RUL prediction algorithm for turbofan engines based on the fused deep learning models. In this article, a multimodal deep learning approach based on a 1DCNN (1D convolutional neural network) + attention enhanced Bi-LSTM (bidirectional long short-term memory) network is proposed to predict the RUL by mining the temporal information of data. Furthermore, a DDResNet (dilated deep residual network) is also introduced to the 1DCNN submodel to leverage its hidden pattern mining capability due to its advance in preventing performance degradation across layers. Subsequently, the output of these two submodels is weighted fused to obtain the final RUL prediction. The merits of the proposed method are demonstrated by comparing it with existing methods for RUL prediction using the C-MAPSS (commercial modular aero-propulsion system simulation) dataset.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and reliable operation of battery systems. However, due to the highly nonlinear degradation behavior of lithium-ion batteries under complex working conditions, traditional prediction methods often fail to achieve satisfactory accuracy. This study proposes a hybrid prediction model that integrates an attention (ATT) mechanism, a convolutional neural network (CNN), and a bidirectional long short-term memory (BiLSTM) network, termed ATT-CNN-BiLSTM. First, a one-dimensional CNN is employed to extract local degradation patterns and inter-channel correlated features from multi-dimensional sensor time-series data. Then, the BiLSTM network captures long-term dependencies within the degradation process. Furthermore, an attention mechanism is introduced to dynamically adjust the contribution of key time-step features and suppress noise interference. Experimental results based on the NASA battery aging datasets demonstrate that the proposed ATT-CNN-BiLSTM model reduces RMSE and MAE by 49.08% and 54.67%, respectively, compared with the LSTM model, and by 10.89% and 17.85%, respectively, compared with the ACNN-LSTM-MMD model. Ablation experiments also confirm that further confirm that the ATT-CNN-BiLSTM model decreases RMSE and MAE by 26.45% and 33.33%, respectively, compared with CNN-BiLSTM, and by 21.85% and 23.36%, respectively, compared with ATT-BiLSTM.

RUL prediction of lithium ion battery based on CEEMDAN-CNN BiLSTM model

Remaining Useful Life (RUL) prediction is a critical technology in prognostics and health management. Rotating machinery plays an indispensable role in industrial processes, and accurate RUL prediction for such equipment is essential to ensure production continuity, optimize maintenance strategies and reduce operational costs. In recent years, deep learning has emerged as a powerful tool for the analysis of multi-source monitoring data derived from rotating machinery, owing to its exceptional proficiency in feature extraction and non-linear modelling. This approach can effectively excavate the deeper-level information within the data, thereby improving prediction accuracy. Consequently, deep learning demonstrates significant application value and broad development prospects in RUL prediction for rotating machinery. This paper provides a comprehensive review of the application of deep learning techniques in RUL prediction for rotating machinery. Firstly, it introduces a unified framework for RUL prediction based on deep learning methodologies. The article then elaborates on the theoretical foundations of several key learning methods, including autoencoders, convolutional neural networks, long short-term memory, gated recurrent units, as well as emerging models such as transformers, temporal convolutional networks and graph neural networks. Subsequently, the paper summarizes the applications of these deep learning methods in RUL prediction for rotating machinery, evaluating the specific problems each method addresses based on their distinct characteristics. Finally, it discusses the significant challenges that deep learning faces within this domain and outlines potential directions for future research.

Background Accurate prediction of the remaining useful life (RUL) of assets is fundamental to the development of effective maintenance strategies and overall asset management. Despite significant advancements, there remains a notable gap in integrating fault detection and diagnostics (FDD) with RUL prediction models to create more comprehensive and accurate maintenance systems. One of the key challenges in this field is the limited ability of current models to generalize effectively across different types of equipment and varying operating conditions. This gap emphasizes the need for further research and innovation in developing robust and adaptable RUL prediction methodologies that can be applied broadly across diverse industrial scenarios. Methodology This review systematically evaluates the machine learning (ML) and deep learning (DL) techniques used for anomaly detection and RUL prediction, focusing on their efficacy and practical application. By adhering to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, the review identifies and addresses the deficiencies in existing models. It explores a range of machine learning and deep learning methods, including probabilistic approaches, hybrid models that combine multiple machine learning techniques, and neural networks designed to handle large-scale time-series data. The review also examines the potential for synergy between machine learning models and FDD, aiming to enhance the precision of equipment monitoring and the early detection of defects. The challenges of data variability, the irregularity in equipment deterioration, and the interpretability of complex models are highlighted. Results The analysis reveals that while current machine learning and deep learning models have made considerable strides in predicting the RUL of assets, significant challenges remain, particularly in their ability to generalize across various equipment types and operational contexts. Hybrid models and neural networks have shown promise in improving the accuracy of RUL predictions, especially when managing large, complex datasets. However, the irregular nature of equipment wears and tear, coupled with data variability, continues to pose significant challenges. The review highlights the need for more robust and adaptable models that can not only predict RUL more accurately but also integrate seamlessly with FDD systems to provide a more holistic approach to maintenance. Conclusion This comprehensive review focusses on the need for continued research in developing more integrated, generalizable, and efficient predictive maintenance systems. By exploring the application of AI in virtual assistants, the review suggests promising avenues for extending asset longevity and optimizing maintenance schedules. While current models offer valuable insights, they must evolve to address the identified gaps in generalizability and model interpretability.

To satisfy the need to accurately monitor emotional stress, this paper explores the effectiveness of the attention mechanism based on the deep learning model CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) As different attention mechanisms can cause the framework to focus on different positions of the feature map, this discussion adds attention mechanisms to the CNN layer and the BiLSTM layer separately, and to both the CNN layer and BiLSTM layer simultaneously to generate different CNN–BiLSTM networks with attention mechanisms. ECG (electrocardiogram) data from 34 subjects were collected on the server platform created by the Institute of Psychology of the Chinese Academy of Science and the researches. It verifies that the average accuracy of CNN–BiLSTM is up to 0.865 without any attention mechanism, while the highest average accuracy of 0.868 is achieved using the CNN–attention–based BiLSTM.

Remaining useful life (RUL) prediction is a cornerstone of Prognostic and Health Management (PHM), playing a crucial role in ensuring the reliability and safety of equipment. In recent years, deep learning techniques have shown great promise in RUL prediction, providing more reliable and accurate outcomes. However, many current deep learning models still fall short in comprehensive feature extraction. To tackle this limitation, we propose a multi-feature fusion model leveraging a dual-attention mechanism. Initially, convolutional neural networks (CNN) and channel attention mechanisms are employed to preliminarily extract spatial features. Subsequently, a layer combining Long Short-Term Memory (LSTM) and self-attention mechanisms is used to further extract and integrate temporal features. Concurrently, domain knowledge-based handcrafted features are selected for multi-feature fusion. Finally, RUL values are predicted via regression. The effectiveness of the proposed method is validated on widely-used academic datasets, and its superior performance in RUL prediction is demonstrated through comparative analysis with other methods.

Emotion recognition (ER) is crucial for understanding human behaviours, social interactions, and psychological well-being. Electroencephalography (EEG) has emerged as a promising tool for capturing the neural correlates of emotions. This work is a systematic review of articles in ER using EEG signals. A total of 120 articles from 1041 articles were selected based on PRISMA guidelines using defined inclusion and exclusion criteria, published between 2018 and 2024. This article aims to provide an in-depth understanding of the current landscape of ER from EEG signals utilizing deep learning (DL). This review offers valuable guidance for researchers and practitioners seeking more refined and reliable emotion classification systems. To explore the effectiveness of DL models in EEG-based ER, several potential DL models, such as convolutional neural network, long short-term memory (LSTM), gated recurrent unit (GRU), hybrid bidirectional LSTM (BiLSTM), bidirectional GRU, and advanced DL models such as convolutional recurrent neural network and EEG-Conformer models are applied to two popular datasets, SEED and GAMEEMO, respectively, to depict the full process of ER. Additionally, the performance of DL models is also compared with the performance of basic machine learning (ML) models such as SVM, k-nearest neighbors, logistic regression, and boosting algorithms such as AdaBoost, XGBoost and LightGBM. Through extensive experiments and performance evaluations, the performance of different models when applied to the datasets mentioned above is compared. The accuracy, precision, recall, and F1-scores are analysed to determine the most effective model for EEG-based ER. The findings of this study demonstrate that the performance of hybrid DL models is more efficacious than that of ML models. The best-performing model (BiLSTM) classified the emotions, with an accuracy of 90.54% when applied to the GAMEEMO dataset. This research contributes to the growing body of literature on ER and provides insights into the feasibility of using EEG signals to understand emotional states, and presents a structured roadmap for future exploration. The findings can aid in the development of more accurate and reliable ER systems, which can have wide-ranging applications in psychology, social sciences, and human-computer interactions.

Sentiment Analysis of Self Driving Car Dataset: A comparative study of Deep Learning approaches

Remaining useful life (RUL) prediction of bearings has extraordinary significance for prognostics and health management (PHM) of rotating machinery. RUL prediction approaches based on deep learning have been dedicated to finding a nonlinear mapping relationship between non-stationary monitoring data and RUL. However, most existing approaches pay little attention to the degradation trend of diverse health stages of bearing and lack the discriminative power of crucial degradation features, resulting in the loss of some important information associated with RUL. To address this challenge, this article proposes a novel RUL prediction framework based on degradation-trend-aware deep neural network with attention mechanism (DTADAN). Firstly, the multi-direction features with evident degradation trend are extracted via the analysis of bearing vibration signal from both time domain and time-frequency domain. Next, the deep neural network architecture with attention mechanism is utilized to adaptively learn the critical degradation features beneficial for RUL prediction. Distinct from the existing approaches, the proposed framework is able to dynamically extract key degradation features of the bearing including degradation trend information and effectively fuse multi-direction information to improve RUL prediction accuracy. The performance of the proposed approach is evaluated via case studies on XJTU-SY bearing dataset and PRONOSTIA bearing dataset. Compared with other state-of-the-art approaches, the proposed framework has better predictive accuracy and robustness. Additionally, interpretable analysis is provided to reveal the process of model learning and data characteristics, and the analysis results are helpful in guiding model learning.

Remaining useful life prediction for multi-sensor systems using a novel end-to-end deep-learning method

The prediction of the remaining useful life (RUL) of bearings is of great significance for reducing cost and increasing efficiency of mechanical equipment and ensuring healthy operation. Most of the traditional RUL prediction methods based on deep learning only analyze single sensor data, which makes the prediction accuracy fluctuate greatly and the reliability is low, and the contributions of data collected by different sensors to RUL prediction are inconsistent, which makes the low utilization of data lead to poor prediction results. To solve the above problems, a new RUL prediction framework with convolutional attention mechanism and temporal convolutional network (CAMTCN) is proposed. Firstly, the vibration signals collected by multi-sensor are normalized and processed directly as the input of the network. Secondly, an Efficient Adaptive Shrinkage (EAS) model is designed to adaptively eliminate noise interference and improve the accuracy of RUL prediction. Finally, we use the attention mechanism to design the convolutional attention subnet and combine it with the TCN to obtain the contribution degree of bearing degradation features collected by different sensors. Adaptive weight allocation captures more important degradation features to achieve end-to-end RUL prediction. Experiments were conducted on IEEE PHM 2012 dataset and XJTU-SY dataset respectively, and compared with some advanced in-depth learning methods. The experimental results show that this method can predict RUL more accurately on different bearing data sets, and has good generalization.

Small and Medium-sized Enterprises (SMEs) in Sri Lanka are increasingly turning to social media to gain insights into consumer preferences; however, they frequently lack sophisticated analytical tools to derive actionable insights. This research introduces a hybrid deep learning framework aimed at sentiment and emotion analysis specifically for the Sri Lankan cake sector. Customer reviews and social media comments were gathered and processed through various methods including text cleaning, emoji translation, stopword elimination, tokenization, and sequence padding. Several deep learning models— Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Units (GRU) were assessed, along with hybrid models that combined CNN and BiLSTM with GPT-2 embeddings. The CNN+GPT-2 model achieved the highest accuracy in sentiment classification at 96.14%, while BiLSTM performed best in emotion classification with an accuracy of 91.57%. In terms of trend forecasting, Long Short-Term Memory (LSTM) outshone both SARIMA and Prophet, yielding the lowest error metrics and the most reliable sentiment predictions. These results underscore the potential of hybrid deep learning and transformer-based models to effectively capture complex consumer sentiments and provide SMEs with practical, AI-driven insights. This study is among the first to implement such hybrid NLP models within a localized Sri Lankan context, presenting a scalable approach for business intelligence and informed decision-making.

Predicting remaining useful life (RUL) is crucial for tool condition monitoring (TCM) systems. Inaccurate predictions can lead to premature tool replacements or excessive usage, resulting in resource wastage and potential equipment failures. This study introduces a novel tool RUL prediction method that integrates the enhanced northern goshawk optimization (MSANGO) algorithm with a bidirectional long short-term memory (BiLSTM) network. Initially, key statistical features are extracted from collected signal data using multivariate variational mode decomposition. This is followed by effective feature reduction, facilitated by the uniform information coefficient and Mann–Kendall trend tests. The RUL predictions are subsequently refined through a BiLSTM network, with the MSANGO algorithm optimizing the network parameters. Comparative evaluations with BiLSTM, BiGRU, and NGO-BiLSTM models, as well as tests on real-world datasets, demonstrate this method’s superior accuracy and generalizability in RUL prediction, enhancing the efficacy of tool management systems.

The online estimation of battery SOH (state of health) and prediction of RUL (remaining useful life) are a prerequisite of ensuring its safe and reliable operation, optimal balance and battery SOC estimation. Considering the actual situation, a relatively stable charging environment is more suitable for accurate estimation and prediction of SOH and RUL compared to uncertain discharge conditions. A health indicator based on charging characteristics is selected to predict battery degradation trends through Pearson and Spearman correlation analysis. The grid search optimisation (GS) algorithm and support vector regression (SVR) are integrated to extract the intrinsic relationship between the health indicator and capacity, combining GS algorithm to optimise SVR kernel parameters. With the Lithium-ion data set provided by NASA, it’s proved that the GS-SVR fusion model is more effective in estimating battery life decay than the SVR model, with a reduction of 52.64% in mean absolute error and 68.51% mean square error. Besides, the R 2 of the GS-SVR model is 97.3%, and the robustness of the model is stronger. The RUL prediction results demonstrate the effectiveness and advantage. Highlights The methods of obtaining indirect health indicators were discussed in detail. Data-driven algorithm based on small samples to realise online model estimation. The estimation results based on GS-SVR model and SVR model were compared. Application to data taken from the Lithium-ion battery cycle aging test of the NASA’s Prognostics Center of Excellence.