Search Results (633)

Accurate RUL prediction of bearings is essential, as bearing failures compromise operational safety. However, distribution discrepancies caused by varying working conditions often degrade prediction performance. DA has been widely used to mitigate this issue, but most DA methods align feature distributions on a global scale, overlooking fine-grained discrepancies within the same domain. SDA offers a promising alternative by aligning feature distributions at a subdomain level. Despite its potential, existing SDA methods often use fixed subdomain boundaries, overlook the unequal importance of subdomains, and lack clustering mechanisms for similar features. These limitations hinder further improvements in RUL prediction accuracy. To address these issues, this paper proposes a novel model, SC-SAN , which dynamically adjusts subdomain boundaries, assigns higher weights to key features, and clusters similar features during model training. The effectiveness of SC-SAN is validated through ablation, comparison and generalization experiments on the XJTU-SY and PRONOSTIA datasets. Experimental results show that SC-SAN achieves an average MAE of 0.1009 and RMSE of 0.1231 across two datasets, representing reductions of 19.86% and 23.41%, respectively, compared to existing state-of-the-art methods. Full article

Accurate remaining useful life (RUL) prediction of tantalum capacitors is essential for enhancing the reliability and maintainability of power electronic systems. However, online RUL prediction remains a challenging task due to the difficulty of accessing internal degradation states and the non-stationarity of operating conditions. This paper presents a novel CNN-LSTM-Attention-based deep learning framework for accurate online RUL prediction of tantalum capacitors, leveraging infrared surface temperature measurements and ambient thermal compensation. The proposed framework initiates with the collection of degradation temperature data under controlled accelerated aging experiments, where true degradation indicators are extracted by eliminating ambient temperature interference through dual-sensor compensation. The resulting preprocessed data are used to train a hybrid deep neural network model that integrates convolutional layers for local feature extraction, long short-term memory (LSTM) units for sequential dependency modeling, and a soft attention mechanism to selectively focus on the critical degradation patterns. A channel attention module is further embedded to adaptively optimize the importance of different feature channels. Experimental validation using three groups of aging data demonstrates the effectiveness and superiority of the proposed method over conventional LSTM and CNN-LSTM baselines. The CNN-LSTM-Attention model achieves a substantial improvement in prediction accuracy, with mean absolute percentage error (MAPE) reductions of up to 60.97%, root mean squared error (RMSE) reductions of up to 65.63%, and coefficient of determination (R²) increases of up to 68.67%. The results confirm the ability to deliver precise and robust online RUL predictions for tantalum capacitors under complex operational conditions. Full article

Predicting the remaining useful life (RUL) of aircraft engines is crucial for ensuring flight safety, optimizing maintenance, and reducing operational costs. This paper introduces a novel hybrid deep learning model, Transformer–KAN–BiLSTM, for aero-engine RUL prediction. The model is designed to leverage the complementary strengths of its components: the Transformer architecture effectively captures long-range temporal dependencies in sensor data, the emerging Kolmogorov–Arnold Network (KAN) provides superior approximation flexibility and a unique degree of interpretability through its spline-based activation functions, and the Bidirectional LSTM (BiLSTM) extracts nuanced local temporal patterns. Evaluated on the benchmark NASA C-MAPSS dataset, the proposed fusion framework demonstrates exceptional performance, achieving remarkably low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values that significantly surpass existing benchmarks. These results validate the model’s robustness and its high potential for practical deployment in prognostics and health management systems. Full article

In the context of Industry 4.0 and smart manufacturing, predicting cutting tool remaining useful life (RUL) is crucial for enabling and enhancing the reliability and efficiency of CNC machining. This paper presents an innovative predictive model based on the data fusion architecture of Graph Neural Networks (GNNs) and Transformers to address the complexity of shallow multimodal data fusion, insufficient relational modeling, and single-task limitations simultaneously. The model harnesses time-series data, geometric information, operational parameters, and phase contexts through dedicated encoders, employs graph attention networks (GATs) to infer complex structural dependencies, and utilizes a cross-modal Transformer decoder to generate fused features. A dual-head output enables collaborative RUL regression and health state classification of cutting tools. Experiments are conducted on a multimodal dataset of 824 entries derived from multi-sensor data, constructing a systematic framework centered on tool flank wear width (VB), which includes correlation analysis, trend modeling, and risk assessment. Results demonstrate that the proposed model outperforms baseline models, with MSE reduced by 26–41%, MAE by 33–43%, R² improved by 6–12%, accuracy by 6–12%, and F1-Score by 7–14%. Full article

The development of Digital Twin (DT) technology in Battery Management Systems (BMSs) presents a transformative approach for maintenance, monitoring, and predictive diagnostics, especially in the demanding field of space applications. DTs, through their three-layer structure, provide an accurate and dynamic virtual representation of the physical entity, continuously updated via bidirectional data exchange provided by the communication link. Given the promising capabilities of the DT approach in real-time applications, its integration into BMSs is straightforward, as it can enhance monitoring and prediction of nonlinear electrochemical systems, such as space-grade lithium-ion batteries, supporting the mitigation of ageing effects under the unique constraints of the space environment. Despite notable progress in BMS technologies, the choice of estimation techniques consistent with the DT paradigm remains insufficiently defined. This survey examines the state of the art with the aim of bridging the conceptual framework of DTs and existing battery management algorithms, identifying the methodologies most suitable in accordance with DT architectures and principles. The scope of this paper is to provide researchers and engineers with a comprehensive overview of the advancements, key enabling technologies, and implementation strategies for Digital Twins in space BMSs, ultimately contributing to more reliable and efficient space missions. Full article

The aviation industry operates as a complex, dynamic system generating vast volumes of data from aircraft sensors, flight schedules, and external sources. Managing this data is critical for mitigating disruptive and costly events such as mechanical failures and flight delays. This paper presents a comprehensive application of predictive analytics and machine learning to enhance aviation safety and operational efficiency. We address two core challenges: predictive maintenance of aircraft engines and forecasting flight delays. For maintenance, we utilise NASA’s C-MAPSS simulation dataset to develop and compare models, including one-dimensional convolutional neural networks (1D CNNs) and long short-term memory networks (LSTMs), for classifying engine health status and predicting the Remaining Useful Life (RUL), achieving classification accuracy up to 97%. For operational efficiency, we analyse historical flight data to build regression models for predicting departure delays, identifying key contributing factors such as airline, origin airport, and scheduled time. Our methodology highlights the critical role of Exploratory Data Analysis (EDA), feature selection, and data preprocessing in managing high-volume, heterogeneous data sources. The results demonstrate the significant potential of integrating these predictive models into aviation Business Intelligence (BI) systems to transition from reactive to proactive decision-making. The study concludes by discussing the integration challenges within existing data architectures and the future potential of these approaches for optimising complex, networked transportation systems. Full article

Accurately assessing the remaining useful life (RUL) is a significant challenge to the reliability of Silicon Carbide (SiC) MOSFETs and is crucial for their safe operation. Consequently, this paper proposes a novel data-driven prediction method that integrates Successive Variational Mode Decomposition (SVMD), the Sparrow Search Algorithm (SSA), and the Transformer model. The threshold voltage V_th is selected as the degradation parameter for prediction. Firstly, SVMD is utilized to decompose the original V_th data into a degradation trend component and several fluctuation components with different central frequencies, thereby providing a more precise feature for prediction models. Subsequently, based on the Transformer model, trend predictions are conducted on each intrinsic mode function (IMF) derived from SVMD, and these results are aggregated as the final predicted value of V_th. The hyperparameters of the Transformer are optimized using SSA to enhance prediction accuracy. Ultimately, a power cycling platform is constructed to acquire the dataset of the device, where the device is subjected to rated current and 80 °C junction temperature fluctuation stress during testing. Building upon this, the difference between the number of cycles when V_th reaches its upper limit and the current number of cycles is determined as the predicted RUL value. Results demonstrate that compared to both a single Transformer model and the SVMD-Transformer model, the proposed method achieves a higher coefficient of determination (R²) and a lower root mean square error (RMSE), indicating superior prediction performance. Full article

This paper presents a mathematics-focused AI iterative prediction framework that combines Extreme Gradient Boosting (XGBoost) for nonlinear function approximation with nonlinear autoregressive model with exogenous inputs (NARXs) for time-series modeling, applied to analyzing the Remaining Useful Life (RUL) and availability of Unmanned Aerial Vehicle (UAV) Brushless DC (BLDC) motors. The framework integrates nonlinear regression, temporal recursion, and survival analysis into a unified system. The dataset includes five UAV motor types, each recorded for 10 min at 20 Hz, totaling approximately 12,000 records per motor for validation across these five motor types. Using grouped K-fold cross-validation by motor ID, the framework achieved mean absolute error (MAE) of 4.01 h and root mean square error (RMSE) of 4.51 h in RUL prediction. Feature importance and SHapley Additive exPlanation (SHAP) analysis identified temperature, vibration, and HI as key predictors, aligning with degradation mechanisms. For availability assessment, survival metrics showed strong performance, with a C-index of 1.00 indicating perfect risk ranking and a Brier score at 300 s of 0.159 reflecting good calibration. Additionally, Conformalized Quantile Regression (CQR) enhanced interval coverage under diverse operating conditions, providing mathematically guaranteed uncertainty bounds. The results demonstrate that this framework improves both accuracy and interpretability, offering a reliable and adaptable solution for UAV motor prognostics and maintenance planning. Full article

Accurate prediction of the degradation trajectory and estimation of the remaining useful life (RUL) of lithium-ion batteries are crucial for ensuring the reliability and safety of modern energy storage systems. However, many existing approaches rely on deep or highly complex models to achieve high accuracy, often at the cost of computational efficiency and practical applicability. To tackle this challenge, we propose a novel hybrid deep-learning framework, IVCLNet, which predicts the battery’s state-of-health (SOH) evolution and estimates RUL by identifying the end-of-life threshold (SOH = 80%). The framework integrates Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Variational Mode Decomposition (VMD), and an attention-enhanced Long Short-Term Memory (LSTM) network. IVCLNet leverages a cascade decomposition strategy to capture multi-scale degradation patterns and employs multiple indirect health indicators (HIs) to enrich feature representation. A lightweight Convolutional Block Attention Module (CBAM) is embedded to strengthen the model’s perception of critical features, guiding the one-dimensional convolutional layers to focus on informative components. Combined with LSTM-based temporal modeling, the framework ensures both accuracy and interpretability. Extensive experiments conducted on two publicly available lithium-ion battery datasets demonstrated that IVCLNet significantly outperforms existing methods in terms of prediction accuracy, robustness, and computational efficiency. The findings indicate that the proposed framework is promising for practical applications in battery health management systems. Full article

Accurately predicting the Remaining Useful Life (RUL) of milling tools based on monitoring signals is crucial for maximizing tool utilization and reducing machining costs. However, tool degradation and working conditions complicate the extraction of features from monitoring data, making the nonlinear mapping from these features to RUL challenging. To address this, a novel Kolmogorov–Arnold Attention Allocation Network (KA-AAN) is proposed, which consists of an Attentional Feature Extraction Network (AFEN) and a KAN regression block. First, the S-transform is applied to multi-sensor signals to create a time-frequency feature dataset. Then, the AFEN allocates attention to extracting attentional features concerning both tool degradation and working conditions by using multiple Kolmogorov–Arnold Attention Blocks and the unique activation function of KAN, which enhances the importance of features at different levels. Furthermore, the KAN regression block maps from attentional features to RUL, learning the most appropriate way to activate and combine features based on the specific circumstances of the tool and machining process. The nonlinear fusion of the attentional features improves the model’s adaptability to different working conditions. Finally, an experiment was designed and conducted to verify the robustness and stability of the proposed method. The experimental results show that the proposed method achieves a mean squared error of 0.97 and a mean absolute percentage error of 8.27% under the same working condition and 2.91% and 18.63% under different conditions (average). Compared to other advanced methods, the proposed method exhibits higher accuracy and adaptability. Full article

In response to the insufficient adaptability of power transformer remaining useful life (RUL) prediction under complex working conditions and the difficulty of multi-scale feature fusion, this study proposes an industrial time series prediction model based on the parallel Transformer–BiGRU–GlobalAttention model. The parallel Transformer encoder captures long-range temporal dependencies, the BiGRU network enhances local sequence associations through bidirectional modeling, the global attention mechanism dynamically weights key temporal features, and cross-attention achieves spatiotemporal feature interaction and fusion. Experiments were conducted based on the public ETT transformer temperature dataset, employing sliding window and piecewise linear label processing techniques, with MAE, MSE, and RMSE as evaluation metrics. The results show that the model achieved excellent predictive performance on the test set, with an MSE of 0.078, MAE of 0.233, and RMSE of 11.13. Compared with traditional LSTM, CNN-BiGRU-Attention, and other methods, the model achieved improvements of 17.2%, 6.0%, and 8.9%, respectively. Ablation experiments verified that the global attention mechanism rationalizes the feature contribution distribution, with the core temporal feature OT having a contribution rate of 0.41. Multiple experiments demonstrated that this method has higher precision compared with other methods. Full article

Industrial machine tools are both performance assets and environmental hotspots over their long service lives. Maintenance is traditionally optimized to safeguard availability, quality and cost. However, maintenance choices also determine the energy consumption, footprints, component duration and end-of-life pathways. In this study, we present a decision framework to compare performance-only maintenance (POM) with sustainability-aware maintenance (SAM) for machine tools. The framework integrates degradation and Remaining Useful Life (RUL) estimation, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Outcomes are summarized with a Sustainable Maintenance Balance (SMB) index. We test the proposed approach on a horizontal machining center for aluminum, validated by running a Monte Carlo simulation over a 1000 h functional unit. Across empirical data and simulation, SAM—compared to POM—demonstrated an ability to improve availability, reduces downtime and scrap, and lower total LCC while cutting carbon emissions. The proposed method is proposed as readily deployable in real plants, supporting robust sustainable-production decisions. Full article

In battery energy storage systems, accurately estimating battery capacity and state of health is crucial to ensure satisfactory operation and system efficiency and reliability. However, these tasks present particular challenges under irregular charge–discharge conditions, such as those encountered in renewable energy integration and electric vehicles, where heterogeneous cycling accelerates degradation. This study introduces a hybrid deep learning framework to address these challenges. It combines convolutional layers for localized feature extraction, bidirectional recurrent units for sequential learning and a temporal attention mechanism. The proposed hybrid deep learning model, termed CBATE-Net, uses ensemble averaging to improve stability and emphasizes degradation-critical intervals. The framework was evaluated using voltage, current and temperature signals from four benchmark lithium-ion cells across complete life cycles, as part of the NASA dataset. The results demonstrate that the proposed method can accurately track both smooth and abrupt capacity fade while maintaining stability near the end of the life cycle, an area in which conventional models often struggle. Integrating feature learning, temporal modelling and robustness enhancements in a unified design provides the framework with the ability to make accurate and interpretable predictions, making it suitable for deployment in real-world battery energy storage applications. Full article

The precise prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance for improving energy management efficiency and extending battery lifespan, and it is widely applied in the fields of new energy and electric vehicles. However, accurate RUL prediction still faces significant challenges. Although various methods based on deep learning have been proposed, the performance of their neural networks is strongly correlated with the hyperparameters. To overcome this limitation, this study proposes an innovative approach that combines the Alpha evolutionary (AE) algorithm with a deep learning model. Specifically, this hybrid deep learning architecture consists of convolutional neural network (CNN), time convolutional network (TCN), bidirectional long short-term memory (BiLSTM) and multi-scale attention mechanism, which extracts the spatial features, long-term temporal dependencies, and key degradation information of battery data, respectively. To optimize the model performance, the AE algorithm is introduced to automatically optimize the hyperparameters of the hybrid model, including the number and size of convolutional kernels in CNN, the dilation rate in TCN, the number of units in BiLSTM, and the parameters of the fusion layer in the attention mechanism. Experimental results demonstrate that our method significantly enhances prediction accuracy and model robustness compared to conventional deep learning techniques. This approach not only improves the accuracy and robustness of battery RUL prediction but also provides new ideas for solving the parameter tuning problem of neural networks. Full article

Predicting the Remaining Useful Life (RUL) of light aircraft landing gear is complicated by flight-to-flight variability in operational loads, particularly in sensor-free fleets that rely only on mass-and-balance records. This study develops a hybrid physics–data framework to quantify operational-load-driven uncertainty in the main landing gear strut of a Cessna 172. High-fidelity finite-element strain–life simulations were combined with a quadratic Ridge surrogate and a two-layer bootstrap to generate full probabilistic RUL distributions. The surrogate mapped five mass-and-balance inputs (fuel, front seats, rear seats, forward and aft baggage) to per-flight fatigue damage with high accuracy (R² = 0.991 ± 0.013). At the same time, ±3% epistemic confidence bands were attached via resampling. Borgonovo’s moment-independent Δ indices were applied to incremental damage (ΔD) in this context, revealing front-seat mass as the dominant driver of fatigue variability (Δ = 0.502), followed by fuel (0.212), rear seats (0.199), forward baggage (0.141), and aft baggage (0.100). The resulting RUL distribution spanned 9 × 10⁴ to >2 × 10⁶ cycles, with a fleet average of 0.41 million cycles (95% CI: 0.300–0.530 million). These results demonstrate that operational levers—crew assignment, fuel loading, and baggage placement—can significantly extend strut life. Although demonstrated on a specific training fleet dataset, the methodological framework is, in principle, transferable to other aircraft or mission types. However, this would require developing a new, component-specific finite element model and retraining the surrogate using a representative set of mass and balance records from the target fleet. Full article