2026· International Journal of All Research Education & Scientific Methods· 0 citations
TL;DR
A hybrid deep learning framework combining Convolutional Neural Networks for local spatial feature extraction and Bidirectional Long Short-Term Memory with a temporal attention mechanism for capturing long-range degradation trends in multivariate time-series sensor data is proposed.
Abstract
Unplanned equipment failure remains one of the costliest problems in modern manufacturing, driving research toward Predictive Maintenance (PdM) strategies that anticipate failures before they occur using real-time sensor data. This paper proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) for local spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks with a temporal attention mechanism for capturing long-range degradation trends in multivariate time-series sensor data. The framework is designed to estimate Remaining Useful Life (RUL) and detect early fault signatures in Industry 4.0
environments equipped with IoT-based condition-monitoring sensors. We formulate the problem as a windowed sequence-to-value regression task, describe a sliding-window feature pipeline, and benchmark the framework's data
pipeline against classical machine learning regressors (Random Forest, Gradient Boosting, Support Vector
Regression, and a Multi-Layer Perceptron) on a run-to-failure sensor dataset. Experimental results show that
ensemble tree-based models achieve strong baseline performance (RMSE as low as 9.96 cycles, R2 = 0.946), establishing a validated pipeline and baseline for the full CNN-BiLSTM-Attention model. The proposed architecture, evaluation protocol, and ablation plan are presented in full so the framework can be reproduced and extended on industrial-scale datasets such as NASA C-MAPSS or live plant telemetry.
Accurate prediction of Remaining Useful Life (RUL) is critical for predictive maintenance and minimizing downtime in industrial systems. This paper presents a cross-domain deep learning framework based on a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture. Unlike domain-specific models that require handcrafted features, the proposed framework extracts local degradation features through CNN layers and captures long-term dependencies via BiLSTM networks. The model is evaluated on three heterogeneous datasets: construction machinery, continuous casting machines, and lithium-ion batteries. Experimental results show that CNN–BiLSTM consistently outperforms baselines, achieving up to 22% lower RMSE compared to GRU and 30–50% lower RMSE compared to traditional models. On the construction dataset, it achieves an MAE of 48.2 hours and RMSE of 67.1 hours (R2 = 0.88), outperforming GRU by 20%. For the casting dataset, the model attains an MAE of 87.6 tons and RMSE of 113.9 tons (R2 = 0.87), surpassing Random Forest by over 35%. On the battery dataset, CNN–BiLSTM reduces the MAE to 49.6 cycles and RMSE to 72.8 cycles (R2 = 0.89), while also achieving the lowest Timeliness Score (27.5) and PHM08 Score (192.4). Cross-domain experiments are evaluated under two settings: zero-shot transfer, where the model is trained on one source domain and directly tested on a different target domain without using labeled target-domain samples, and fine-tuned transfer, where 20% of labeled target-domain samples are used to update only the fully connected layers while keeping the CNN and BiLSTM layers frozen. The zero-shot results reflect the effect of domain shift, while the fine-tuned results show that lightweight transfer adaptation reduces RMSE by 25–40% across domains. These findings indicate cross-domain adaptability under limited target-domain supervision rather than fully unsupervised cross-domain generalization. These results highlight the feasibility of a unified CNN–BiLSTM framework for scalable, cross-domain RUL estimation and its suitability for real-world prognostic applications.
Sudip Saha, Muhammad Arslan Pervaiz, Muhammad Safwat Rahman et al.· PLoS ONE· 0 citations
High-precision state-of-charge (SOC) prediction is critical for electric vehicle (EV) safety and performance. To address the high computational complexity of existing data-driven methods, which rely on long historical sequences, this paper proposes a purely data-driven end-to-end SOC prediction framework based on sliding-window technology. The framework adopts an SCSSA-optimized convolutional neural networks (CNN)-long short-term memory (LSTM)-attention hybrid model that integrates a CNN for local feature extraction, an LSTM for modeling temporal dependencies, and an attention mechanism for adaptive feature weighting, with an improved sparrow search algorithm for global hyper-parameter optimization. Experiments are conducted using 29 months of operational data from 20 EVs. Results show that the proposed method achieves 14.8%, 8.9%, and 17.6% improvements in mean absolute error, root mean square error, and mean absolute percentage error, respectively, compared with the best benchmark model, with R2 consistently above 0.96. The method demonstrates excellent robustness across seasonal variations and diverse charging patterns, laying a solid technical foundation for SOC prediction in battery management systems.
Xing Zhang, Ju-Qiang Feng, Shunli Wang et al.· Engineering Research Express· 0 citations
Predicting the Remaining Useful Life (RUL) of rolling bearings is a central problem in predictive maintenance, but most existing data-driven solutions still rely on a single vibration channel and a CNN–LSTM architecture. This combination ignores cross-axis degradation information, depends on noise-sensitive Fourier features, carries a heavy parameter count from standard convolutions, and is limited by the sequential nature of LSTM. In this paper, we propose a Dual-Channel Wavelet Scattering Network(DC-WSNet) that addresses these four limitations in a single architecture. The model uses two parallel depthwise separable CNN branches to fuse the horizontal and vertical vibration channels. It then simultaneously extracts features from the wavelet, Fourier, and time-statistical domains, recalibrates the fused channels using an Efficient Channel Attention (ECA) module, and uses a Temporal Convolutional Network (TCN) for temporal modeling. Experimental and interpretability analysis demonstrate that the proposed model effectively captures the cross-axis degradation patterns and leverages multi-axis information for improved RUL prediction.
S. T, Poornesh M, Siddhartha Reddy Salla et al.· 2026 4th International Confe...· 0 citations
Experimental results demonstrate that the hybrid architecture consistently outperforms the standalone TCN and Reservoir components, as well as other benchmark methods, achieving substantially improved PHM scores while retaining competitive RMSE performance.
Mahika Annie Verghese, C. Columbus, E. Elakiya· Scientific Reports· 0 citations
The rapid emergence of Industry 4.0 technologies has significantly transformed manufacturing industries by
integrating Artificial Intelligence (AI), Industrial Internet of Things (IIoT), Cloud Computing, Big Data Analytics, and CyberPhysical Systems. Among these advancements, predictive maintenance has emerged as one of the most promising applications
for improving operational efficiency and equipment reliability. Traditional maintenance strategies, such as corrective and
preventive maintenance, often lead to increased operational costs, unnecessary maintenance activities, and unexpected
equipment failures. Consequently, organizations are increasingly adopting AI-powered predictive maintenance systems that
utilize deep learning techniques to predict machine failures before they occur. Deep learning models, including Convolutional
Neural Networks (CNN), Long Short-Term Memory (LSTM), Autoencoders, Recurrent Neural Networks (RNN), and
Transformer-based architectures, have demonstrated remarkable capabilities in analyzing large volumes of industrial sensor
data and identifying hidden patterns associated with equipment degradation. This study provides a comprehensive review of AIpowered predictive maintenance using deep learning approaches, examining its applications, benefits, challenges, and future
opportunities. The study further proposes a conceptual framework integrating AI, IIoT, and deep learning technologies to
improve maintenance decision-making. The findings indicate that deep learning significantly enhances fault diagnosis,
Remaining Useful Life (RUL) prediction, anomaly detection, and maintenance optimization. However, challenges such as data
quality issues, model interpretability, cybersecurity concerns, and integration complexities continue to influence industrial
adoption. The study concludes that AI-powered predictive maintenance will become a fundamental component of smart
manufacturing and Industry 5.0 initiatives.
M. Varusai Mohamed, N. Malathi, P. Vanithamani et al.· International Journal for Re...· 0 citations
To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical. Traditional maintenance often proves insufficient under dynamic mission profiles. In this study, a deep learning-based predictive maintenance model capable of autonomously extracting features from multivariate sensor data was developed. Using the NASA C-MAPSS FD001 and FD004 datasets, data were converted into sequential blocks via 50- and 30-step sliding windows, respectively. The model's architectural superiority in autonomously extracting temporal degradation features was validated against RF, CNN-LSTM, and BiLSTM baselines. On FD001, it achieved an R-squared (R2) of 0.8901, a 13.28 RMSE, and a 320.34 NASA risk score, demonstrating generalizability on the multi-regime FD004 dataset with a 15.71 RMSE. The proposed maintenance protocol achieved a 0.9973 AUC at the critical 30-cycle threshold, ensuring high reliability. Additionally, a decision-support simulator has been developed to validate this protocol under aggressive combat flight profiles.
Fatih Ürgen, Dogay Altinel· 0 citations
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