The accurate assessment of State of Health (SoH) of battery plays pivotal role in enabling the safe and dependable operation of lithium-ion battery systems. In this work, we propose a hybrid deep learning architecture integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks within a Bayesian learning paradigm to improve SoH prediction accuracy considering complex charge-discharge patterns. The CNN module automatically extracts temporal and local degradation features from sequential charge-discharge profiles, while the LSTM component captures long-term dependencies in the cycling behavior. Bayesian inference is incorporated to quantify predictive uncertainty; posterior sampling yields 90% prediction intervals (S = 100; μ ± 1.64σ), which support uncertainty-informed assessment under irregular and noisy inputs. The model is trained and evaluated on the NASA randomized battery usage dataset; labels are computed from capacities at consecutive reference discharge cycles as the incremental change in SoH (dSoH), with absolute SoH reconstructed by accumulation at inference. Experimental results demonstrate that the proposed Bayesian CNN-LSTM architecture achieves high prediction accuracy, with an R² score of 0.932 and an RMSE of 0.022, while providing uncertainty estimates with reasonable empirical coverage under diverse operational conditions. These findings indicate potential applicability in battery management workflows.
Fault detection and diagnosis (FDD), Fault classification in the Tennessee Eastman Process (TEP) involves strongly coupled, nonlinear multivariate trajectories. The present research evaluates a residual Temporal Convolutional Network (TCN) with Squeeze-and-Excitation (SE) channel attention, denoted CA-TCN. We compare CA-TCN with plain TCN, Long Short-Term Memory (LSTM), and Convolutional Neural Network-LSTM (CNN-LSTM) baselines. CA-TCN and TCN+SE refer to the same implemented architecture. In the primary five-seed comparison, plain TCN achieved a mean accuracy of 99.8756 ± 0.0807%, compared with 99.8511 ± 0.1104% for CA-TCN, 99.1711 ± 0.9467% for LSTM, and 99.8444 ± 0.0820% for CNN-LSTM. A separate matched five-seed TCN/CA-TCN ablation yielded mean accuracies of 99.8000 ± 0.1199% and 99.7756 ± 0.1823%, respectively. The mean paired CA-TCN-minus-TCN difference was −0.0244 percentage points. The exploratory paired analysis did not identify a statistically detectable difference in accuracy. Adding the SE module introduced 4,192 additional trainable parameters and increased the measured training time while providing explicit reweighting of 64 learned latent feature channels. These coefficients are interpreted as internal latent-channel recalibration rather than sensor-importance scores. The results therefore characterize the performance–complexity trade-off associated with adding SE attention to the residual TCN backbone. The conclusions are limited to offline whole-trajectory classification on the evaluated TEP benchmark; cross-domain generalization and online fault-detection performance were not assessed.
GE-Hitachi’s BWRX-300 Small Modular Reactor design adopts innovations from the Advanced Boiling Water Reactor (ABWR), including the use of servomotor-driven fine motion control rod drives (FMCRDs) instead of the hydraulically-driven locking piston control rod drives (LPCRDs) commonly used in current boiling water reactors (BWRs). The FMCRDs provides more precise power control during normal operation and functional backup to the hydraulic SCRAM system during emergency scenarios. To address the challenge of monitoring the novel servomotor-driven FMCRDs in the BWRX-300, for which no operational data exist, this paper develops and validates a simulation-based, two-stage diagnostic framework. A model of the FMRCDs was created and verified in MATLAB/Simulink to generate comprehensive datasets for healthy operation and key fault modes, including stator short circuits and mechanical load failures. From these simulations, diagnostic features were extracted from motor currents and control response signals. The proposed framework first uses Auto-Associative Kernel Regression (AAKR) and a Sequential Probability Ratio Test (SPRT) for robust anomaly detection, achieving an 85.9% true positive rate with a 2.15% false positive rate. Upon detection, a second-stage multi-channel SPRT classifier diagnoses the specific fault, successfully identifying 100% of load faults and 75% of short faults. This study validates that a simulation-driven approach can establish an effective and interpretable predictive health monitoring system for critical reactor components before they are physically deployed, enabling condition-based maintenance from the outset.
Maritime operations require high levels of availability and safety under changing cargo, operational, and environmental conditions. Therefore, anticipating degradation and failures in critical ship systems is an important operational requirement.Although predictive maintenance has advanced rapidly through data-driven approaches and artificial intelligence (AI), the available literature remains fragmented and does not consistently establish the relationships among the ship systems analyzed, the methodological approaches employed, and the conditions required for operational implementation.This article presents a systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to examine the development of AI-based predictive maintenance for ship systems from a Prognostics and Health Management (PHM) perspective. Scopus and Web of Science were searched, and predefined screening, eligibility, inclusion, and exclusion criteria were applied.The findings show that research is strongly concentrated on propulsion systems, classified under Ship Work Breakdown Structure (SWBS) Group 200, and electric-plant systems, classified under SWBS Group 300, with most studies focusing on detection and diagnosis. Prognostic and prescriptive applications remain comparatively less developed. Methodologically, the literature is dominated by machine-learning and deep-learning approaches using operational and condition-monitoring variables associated with different operating regimes.Overall, maritime PHM exhibits uneven levels of functional and implementation maturity. Important challenges remain in model comparability, independent validation, and sustained onboard deployment, while opportunities persist for extending PHM to underrepresented ship systems and validating these approaches in real-world fleet operations
Data-driven approaches for diagnosing and predicting the health of engineering systems require sufficient training data to achieve reliable performance. However, despite its high practical relevance, the question of how much data are actually required has not yet been systematically investigated.This paper presents and evaluates two complementary approaches to answering this question. Approach I fits parametric functions to the initial segments of learning curves and extrapolates them to estimate the amount of training data required to achieve a target classification accuracy. Approach II introduces a training-free, entropy-based stopping criterion that uses the gradient of normalized entropy (GNE). The information gain from the data collected over time is monitored to determine whether further data acquisition is necessary. Both approaches are evaluated using a dataset for fault classification in rolling bearings, employing a multi-layer perceptron classifier and randomly drawn, in-distribution test sets. The results show that learning curve extrapolation enables predictions of the required amount of data and that GNE can serve as an effective online stopping rule during data acquisition due to the strong correlation between GNE and classification accuracy. The two approaches complement each other: entropy analysis can monitor the progress of data acquisition in real time without model training, while learning curve extrapolation provides quantitative estimates of the remaining data requirements for specific accuracy targets.
