International Journal of Prognostics and Health Management

A Hybrid Particle Filter-Weibull Approach for Probabilistic End-of-life Prediction of Industrial Machines Using Vibration Data

2026-07-15T18:56:12+00:00

Accurate degradation modeling is essential for ensuring the reliability of industrial machinery operating under uncertain conditions. This paper presents a hybrid prognostic framework that integrates Particle Filters (PF) with a Weibull Failure Rate Function (WFRF) for probabilistic End-of-Life (EOL) prediction using vibration data. The PF is employed to estimate hidden degradation states and update model parameters sequentially, while the WFRF provides a statistical representation of failure behavior. The proposed framework enables joint degradation tracking and uncertainty quantification. The method is validated using vibration datasets from a mining dumper operating under real conditions and from an induction motor operating under controlled laboratory conditions. Results demonstrate that the hybrid PF-WFRF approach achieves accurate EOL prediction with small deviation from actual failure thresholds (within ~120 seconds) and provides meaningful uncertainty bounds through probability density functions. Unlike existing hybrid approaches that rely on machine learning, the proposed framework provides a computationally efficient, interpretable probabilistic solution. The framework is robust to both smooth and noisy degradation signals, making it suitable for real-world prognostics applications.

Bayesian CNN-LSTM for Battery State of Health (SoH) Estimation under Randomized Usage Conditions

2026-06-26T10:19:32+00:00

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.

Regularization of Divergent Series via Classical Summability Methods with Applications to Reliability and Engineering Systems

2026-05-28T16:36:39+00:00

Accurate estimation of remaining useful life (RUL) from noisy and oscillatory degradation data remains a fundamental challenge in prognostics and health management (PHM), especially in safety-critical and resource-constrained settings. While modern data-driven methods achieve high predictive accuracy, they often require extensive training, lack interpretability, and provide limited guarantees on reliability. This study addresses these challenges by proposing a Cesàro-based regularization framework for constructing stable, interpretable, and conservative health indicators from degradation signals. The main objective is to develop a parameter-free and computationally efficient smoothing method that ensures safety-aware degradation tracking. The novelty of this work lies in adapting classical summability theory to PHM and establishing an explicit finite-sample bound that guarantees the smoothed estimate remains conservatively biased relative to the most recent observation, even under mild non-monotonic conditions. The method applies Cesàro averaging to a univariate health index derived from sensor fusion and is evaluated on NASA C-MAPSS FD001 and FD004 datasets using a consistent experimental framework. It is compared with standard smoothing techniques, including SMA, EWMA, Kalman filtering, and isotonic regression. Results show that the proposed approach achieves competitive or improved performance across RMSE, MAE, PHM score, and prognostic horizon, while significantly reducing variance and computational complexity. The Cesàro regularization provides a theoretically grounded, lightweight, and interpretable solution for degradation modeling. Its conservative nature enhances reliability, making it particularly suitable for real-time predictive maintenance applications in aerospace, energy, and industrial systems.

Physics-Informed Multi-Scale Network with Loss-Guided Curriculum Learning for Robust Fault Diagnosis

2026-06-19T22:35:00+00:00

Reliable fault diagnosis of rotating machinery is critical, yet early weak fault impulses are frequently buried in severe compound interference from mechanical harmonics and environmental noise. To address this, a novel Physics-Informed Multi-Scale Network (PI-MSN) is proposed. Variational Mode Decomposition (VMD) is first employed to decouple raw signals into distinct physical frequency bands. Subsequently, a Physics-Informed Channel Attention (PICA) module jointly evaluates the Kurtosis and Root Mean Square of each channel to autonomously highlight fault impulses and suppress harmonic interference. A Multi-Scale Feature Extractor then captures comprehensive fault characteristics. Furthermore, a closed-loop Loss-Guided Smooth Interference Scheduler (LGSIS) dynamically regulates injected interference during training based on real-time loss, fundamentally eradicating catastrophic forgetting. Extensive experiments on the CWRU and HUST datasets demonstrate the framework's exceptional robustness. The highly lightweight PI-MSN achieves state-of-the-art diagnostic accuracy, sustaining over 98\% accuracy even under severe -4 dB compound interference, proving that physical interpretability effectively eliminates the reliance on massive parameter stacking.

Data-Driven Fault Diagnosis of Rolling Bearings Using Neural Networks Optimized via Bayesian, Particle Swarm, and Genetic Algorithm Methods with Time–Frequency Features

2026-06-27T11:32:41+00:00

This study presents an applied comparative evaluation of automated rolling bearing fault identification using Artificial Neural Networks (ANNs) optimized through Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Vibration signals were collected from bearings operating under five health conditions (healthy, outer ring fault, inner ring fault, ball fault, and combined faults), at three rotational speeds, and along three measurement directions. The acquired signals were preprocessed using filtering, normalization, and segmentation. Time-domain and Fast Fourier Transform (FFT)-based frequency-domain features were extracted and used to train ANN models. The ANN architectures, including hidden layers, neurons, and activation functions, were optimized using BO, PSO, and GA, resulting in six configurations. Since the problem is a multi-class classification task, performance was assessed using F1-score, Accuracy, precision, and recall. The optimized ANN models were also benchmarked against Support Vector Machine (SVM), K-Nearest Neighbors (kNN), and Random Forest (RF) classifiers using the same feature sets. Results show that FFT -based features consistently outperformed time-domain features, and ANN-PSO with FFT-based features achieved the best performance, with F1-score = 0.982, Accuracy = 0.998, precision = 0.982, and recall = 0.982. This work contributes a systematic applied comparison of ANN optimization strategies rather than a fundamentally new machine-learning architecture, highlighting optimized ANNs as competitive and computationally efficient solutions for bearing fault diagnosis.

Remaining Useful Life Prediction across Datasets via Semi-Supervised Domain Adaptation

2026-06-29T12:08:14+00:00

Remaining useful life (RUL) prediction has become a critical task, as accurate prediction enables effective maintenance planning and minimizes unplanned downtime. Complex systems frequently operate under diverse operating conditions, with distributional differences and limited RUL labels. To address these issues, domain adaptation (DA) methods have attracted increasing attention. Mainstream RUL prediction DA methods mainly adopt an unsupervised setting (UDA), relying solely on unlabeled target-domain data. However, prediction performances of UDA methods are far inferior to Target-Only (TO) models trained on full labeled target data, and it fails to effectively transfer degradation-related knowledge for RUL prediction when source-target distribution differences are large. Additionally, existing research mainly focuses on in-dataset adaptation across operating conditions, lacking systematic exploration of cross-dataset RUL prediction. Therefore, this work proposes a semi-supervised domain adaptation (SSDA) workflow for RUL prediction between different datasets. We design an alignment pipeline to address feature discrepancies between datasets and sampling frequency differences. During SSDA training, the original RUL labels of the source domain are treated as noisy labels with respect to the target domain and are progressively modified throughout the adaptation process. We conduct two case studies, one on turbofan engines and the other on rolling bearings. The experimental results demonstrate that the proposed approach can effectively realize cross-dataset RUL prediction.

Estimating Sufficient Amount of Training Data for Data-Driven Fault Diagnosis

2026-09-19T04:41:28+00:00

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.

Multiscale Ordinal Complexity Analysis for Wind Turbine Bearing Fault Diagnosis

2026-06-15T23:38:26+00:00

Permutation-entropy methods for bearing fault diagnosis in wind turbines are typically validated on laboratory test rigs without enforcing specimen-level separation in cross-validation or quantifying axis-specific diagnostic contributions. This study addresses these limitations using the Fraunhofer LBF operational wind turbine bearing dataset, applying multiscale permutation entropy (MPE) to triaxial front-bearing accelerometer signals across 44 bearing specimens (18 healthy, 10 inner race, 4 outer race, 12 roller element), with two corrupted files excluded following data quality screening. GroupKFold cross-validation with unique specimen-level group identifiers prevents the data leakage that arises when temporally correlated analysis windows are split without regard to bearing identity - a limitation present in all ten studies identified in a systematic literature search. MPE achieves 97.67% +/- 2.10% window-level and 97.73% specimen-level accuracy using 12 features across three accelerometer axes, outperforming weighted permutation entropy (WPE, 91.83% window-level, 95.45% specimen-level) and matching a physically-augmented hybrid (MPE+Physical, 21 features) that contributes no additional specimen-level accuracy despite 33.0% feature importance within the combined set. The X-axis accelerometer (brng.f.x) accounts for 40.7% of classification importance, consistent with the primary radial load direction. Permutation entropy reveals a monotonic complexity hierarchy across fault types - Healthy (mu=0.702) < Roller Element (mu=0.889) < Inner Race (mu=0.981) < Outer Race (mu=0.992) - with Cohen's d > 0.9 for all pairwise comparisons. Roller element faults exhibit bimodal permutation entropy distributions, explained by load-zone-dependent impulsive generation. These results demonstrate that multiscale ordinal pattern analysis captures physically meaningful fault signatures in operational wind turbine data when evaluated under methodologically sound cross-validation protocols.

Dual-Channel Acoustic Temporal–Spectral Representation and Noise-Perturbation Learning for Mining Conveyor Fault Diagnosis

2026-06-24T12:41:35+00:00

This study develops a robust acoustic fault diagnosis framework for mining conveyor idlers, addressing the challenge of detecting early-stage mechanical degradation in noisy and imbalanced industrial environments. A dual-channel temporal--spectral representation is constructed by combining multi-scale log-Mel spectrograms and raw waveforms to capture complementary spectral patterns and fine-grained temporal dynamics. A Dual-Stream Cross-Attention Convolutional Recurrent Neural Network (DSCA-CRNN) is proposed to model cross-stream dependencies and enhance feature fusion. Noise-perturbation augmentation and a triplet-based contrastive objective are employed to enrich minority fault samples and improve embedding discriminability. Experiments on a self-collected conveyor auscultation dataset with 2,495 segments across three health states show that DSCA-CRNN achieves an overall accuracy of 0.95 and a macro-F1 score of 0.90, outperforming representative machine learning and deep learning baselines. Severe-fault recognition reaches an F1-score of 0.80. Ablation studies and PCA visualization confirm the effectiveness of recurrent temporal modeling, cross-attention fusion, noise-perturbation learning, and contrastive representation shaping. The proposed auscultation-based framework provides a practical and deployable solution for safety-oriented conveyor condition monitoring.

Channel-Attentive Temporal Convolutional Networks (CA-TCN) for Interpretable Fault Diagnosis in the Tennessee Eastman Process

2026-10-12T16:19:33+00:00

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.

Monotonic Latent Manifold Bayesian Inference for Robust Industrial Predictive Maintenance

2026-07-21T11:06:55+00:00

Industrial predictive maintenance systems require not only accurate fault prediction but also reliable uncertainty estimation and robustness under noisy operational conditions. However, conventional deep learning approaches often produce temporally inconsistent failure trajectories and exhibit poor probabilistic calibration when exposed to stochastic sensor disturbances. This study proposes a Monotonic Latent Manifold Bayesian Neural Network (MM-BNN) framework for robust industrial predictive maintenance. The MM-BNN models degradation progression as a constrained non-decreasing latent probabilistic trajectory, thereby ensuring physically consistent temporal evolution while preserving Bayesian uncertainty quantification. A monotonic latent manifold update mechanism is introduced to enforce structured degradation dynamics across sequential observations. In addition, Bayesian posterior inference is performed using the No-U-Turn Sampler (NUTS) to capture uncertainty in latent degradation states. The proposed MM-BNN framework is evaluated against multiple baseline models, including multilayer perceptron networks, gated recurrent units, random forests, support vector machines, XGBoost, and standard Bayesian neural networks. Experimental results demonstrate that the MM-BNN achieves superior discriminative performance with an average Area Under the Curve (AUC) of 0.772, outperforming competing approaches, most of which remain close to random classification performance. Reliability analysis further demonstrates improved probabilistic calibration, with a low Expected Calibration Error (ECE approx 0.034) and stable reliability behavior under increasing levels of stochastic noise. Ablation studies confirm that the monotonic degradation constraint is the primary factor driving predictive stability and robustness improvements. Furthermore, robustness experiments show that the MM-BNN maintains stable predictive performance even under severe noise perturbations. Overall, the findings indicate that the proposed MM-BNN framework provides a reliable, interpretable, and uncertainty-aware approach for predictive maintenance in safety-critical industrial environments.

Simulation-Based Fault Detection and Diagnosis for Electric Servomotor Control Rod Drives in BWRX-300

2026-09-09T22:02:46+00:00

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.

Development of an Integrated Digital Twin Framework for Online Monitoring and Anomaly Detection

2026-08-31T18:22:20+00:00

Digital Twins (DTs) are emerging as an important enabling technology in the nuclear field, as their development leverages the ongoing digitalization of nuclear systems and offers many advantages for optimizing nuclear operations. Nuclear DTs can be used for predictive maintenance, enhanced operations, improved scheduling, cradle-to-grave lifecycle management, decision support, and more. While the advantages are clear, continued DT development is needed, as many gaps remain, including technological and regulatory standardization, validation and verification, and digital-to-physical communication. To help close these gaps, a forced-water-flow experimental system at the University of Tennessee, Knoxville (UTK) was used to develop a complete, integrated DT, offering an end-to-end use case in the nuclear sector and related disciplines. For this work, a Long Short-Term Memory (LSTM) network was used for forecasting, and an interquartile range (IQR) LSTM hybrid method was used for anomaly detection. This was all interfaced with LabVIEW, enabling real-time data analysis and forecasting. A GUI was then created to provide the operator with a clear indication when a system anomaly is detected, enabling better insights during operation. The resulting platform demonstrates an end-to-end proof of concept for integrating data acquisition, machine learning-based forecasting and anomaly detection, and operator interaction within an experimental DT for various nuclear and related engineering systems.

A Systematic Review of AI-Based PHM Frameworks for Ship Systems and Equipment

2026-10-05T05:45:09+00:00

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