The industrial evolution has emerged the concept of Industry 5.0 paradigm. The concept of Industry 5.0 emphasizes three main aspects of industrial development, i.e. sustainability, resilience, and human centric. The emerging tech-nologies related to digitalisation and artificial intelligence are expected to augment human-system-interaction in various industrial processes. Integrating AI and digital technologies to enable human-centric solutions in industry can manifest in a concept called “Industrial Metaverse”. The Industrial Metaverse is considered a concept that emerged from integrating the Metaverse and Digital Twin, but adapted to specific characteristics of industrial contexts. Industrial Metaverse can enhance Asset Management (AM) within the Industry 5.0 framework. By integrating AI and advanced digital technologies, the Metaverse offers a promising roadmap for optimising human-system interaction in industrial contexts. Metaverse is expected to enhance human centricity of Industry 5.0 and developing metaverse in industrial contexts is challenging and requires a framework. Hence, this paper provides a framework that helps to develop Industrial Metaverse in the context of AM specifically in maintenance. This paper employs literature review in popular databases and discussions with industrial stakeholders to present the current landscape of Metaverse application in Industry 5.0 with a specific focus on maintenance. A case study from the railway sector demonstrates the practical benefits of the Metaverse in improving operational efficiency and maintenance processes. The findings contribute to understanding the Metaverse’s role in shaping the future of industrial asset management and maintenance within Industry 5.0.

Ensemble learning methods, particularly stacking, are often expected to enhance the performance of machine learning models. In this study, an investigation was carried out on whether stacking consistently improves classification accuracy in the context of fault diagnosis. Vibration signals collected from a reciprocating air compressor wherein three distinct features such as statistical, histogram and autoregressive moving average (ARMA) features were extracted. The most significant features were selected using the J48 algorithm and a variety of machine learning classifiers were trained on these features. The performances of individual classifiers were recorded and compared against stacking ensembles built from the same models. The results show that while several individual models achieved high classification performance, stacking did not provide consistent improvements. These findings highlight that stacking was ineffective on the considered air compressor dataset and is not always advantageous in fault diagnosis.

This study presents a machine learning-based framework for detecting critical events in railway infrastructure by analyzing vibration signals from trackside accelerometers. Traditional maintenance is often reactive and labor-intensive, but this approach uses continuous sensing and data analytics to enable proactive, real-time monitoring. The research leverages a comprehensive pipeline that includes data preprocessing, segmentation of time-series data into one-second intervals labeled as "event" or "no-event", and the extraction of statistical, temporal, and spectral features like crest factor and kurtosis. Key contribution of this work is the systematic evaluation of 72 algorithm-feature selection configurations. Twelve diverse classification algorithms were compared, including tree-based, linear, and neural network models. Extensive hyperparameter optimization was performed to benchmark performance using metrics such as accuracy, precision, recall, and F1-score. The Multi-Layer Perceptron (MLPClassifier) achieved a peak cross-validation accuracy of 98.89% with the full feature set. The study also found that comparable accuracy (98.67%) could be achieved with a 47% dimensionality reduction using Recursive Feature Elimination (RFE) with only eight features, demonstrating a balance between efficiency and performance. The findings provide actionable insights for developing scalable, high-performance event detection systems.

Photovoltaic (PV) systems experience various faults due to environmental conditions, human errors, and equipment failure during their service life. To necessitate maximum power generation and ensure ideal operating conditions, the development of intelligent fault diagnosis models is essential. In the present study, an attention-based deep learning network, namely, vision transformer (ViT), is adopted to automatically detect the visual faults, such as glass breakage, discoloration, burn marks, snail trail, good panel, and delamination on PV modules. An image dataset has been developed with the true color images of various faulty PV modules. The ViT model was fine-tuned and trained over the custom dataset created. The trained ViT model demonstrates a superior classification accuracy of 99.84% for fault detection and classification in PV modules. The obtained classification results of the model are compared with several other classification results reported in the literature. The ViT model could potentially be integrated into existing inspection systems for autonomous, real-time, efficient, and robust condition monitoring of PV modules.

This study presents an emission prediction framework for a dual-fuel compression-ignition engine operated on a 20 % winter green oil–diesel blend enriched with hydrogen and equipped with a zeolite-based after-treatment system. Extra Trees, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and AdaBoost are the tree-based ensemble regression models used to predict the emission parameters under limited data conditions. The performance of the models was assessed through 5-fold cross-validation and a 20 % hold-out test method using R-Squared (R²), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) as the evaluation metrics. Among the five tree-based regression models Extra Trees Regressor performed better with highest R² values in the range of 0.99966–0.99974 and the lowest error metrics for all the emission parameters and demonstrates the outstanding robustness and generalization ability of the model. The stronger consistency of extra trees across different test samples was demonstrated by absolute error heatmaps, while the model's accuracy was further validated by comparing actual and predicted values. The study's overall findings demonstrate the potential of tree-based ensemble learning, and extra trees in particular, as a lightweight, accurate and reliable tool for real-time emission prediction in low-carbon dual-fuel systems.

Roller bearings are critical components in many types of machinery, and their failure may cause significant downtime and maintenance costs. Fault diagnosis of roller bearings is thus crucial for detecting potential problems before they cause catastrophic failure and for planning maintenance and repair operations ahead of time. Early detection of roller bearing failures can help to minimize costly machine downtime and save maintenance costs. This study uses the help of deep learning models for roller bearing fault diagnosis, which can help to minimize machinery downtime and maintenance costs. The study utilizes 12 deep learning modules, and they were evaluated using various image generation methods such as vibration plot, radar plot, polar plot, Hilbert–Huang transforms, spectrogram, and scalogram. From the experimental findings, the ResNet18 model has achieved a 100.00% accuracy when the spectrogram image generation method was employed. The findings highlight the importance of selecting and optimizing deep learning models for a specific maintenance application and contribute valuable insights for researchers and practitioners in reliability engineering.

Background: This study delves into the significance of face milling tools in machining, emphasizing the need for timely fault diagnosis to enhance the efficiency of manufacturing processes. By examining defect scenarios such as flank wear, breakage and chipping, along with a reference for good tool condition, the research aims to improve diagnostic accuracy and optimize manufacturing performance.

Methodology: Vibration signals generated during milling operations are analyzed to identify tool faults. A feature extraction process incorporating statistical, histogram, and ARMA features is employed to gain a nuanced understanding of tool behavior. Feature selection is performed using the J48 decision tree algorithm which helps identify the most relevant features. Subsequently, 13 tree-based classifiers are applied to classify tool faults effectively.

Results: A comparative analysis of classification outcomes provides practical insights into the most effective features for fault diagnosis in milling tools. The study’s findings show that the combination of ARMA features with Extra trees achieved an impressive accuracy of 96.88% for milling tool fault diagnosis. The outcomes from the study contribute to real-world applications by enhancing diagnostic methodologies, ultimately advancing fault detection and classification in machining processes.

Tire pressure monitoring systems (TPMS) are essential for vehicle safety and performance as they help detect low tire pressure that impacts fuel efficiency, ride comfort, and overall safety. This study introduces a novel stacking ensemble model to improve the monitoring of nitrogen-filled pneumatic tires. Vibration signals, captured under four conditions such as idle, highspeed, normal, and puncture, using low-cost MEMS accelerometers, are processed to derive autoregressive moving average (ARMA), histogram, and statistical features. The J48 decision tree is employed for feature selection, enhancing classifier accuracy. Experiments with various machine learning classifiers show that the stacking ensemble approach significantly improves classification performance for ARMA (93.75%) and histogram (85.42%) features, thereby achieving higher accuracy than individual classifiers. These findings demonstrate that stacking ensembles can enhance TPMS capabilities, offering a cost-effective and accurate solution for real-time tire pressure monitoring. This advancement contributes to automotive safety and maintenance by enabling more reliable and precise TPMS.

The study investigates the significance of clutch condition monitoring in automotive transmissions to preempt mechanical failures, enhance efficiency, and mitigate risks to human safety and maintenance costs. It explores the integration of Vision Transformer (ViT) with imaging techniques, such as scalograms, spectrograms, polar plots, radar plots, and Hilbert-Huang transforms, to diagnose faults in dry friction clutches. By transforming vibration signals into image representations and utilizing ViT for fault classification, the study aims to identify the most effective imaging technique and optimal hyperparameters for accurate fault diagnosis. Experimental studies on a test rig with varying fault conditions demonstrate the effectiveness of ViT in diagnosing clutch faults when coupled with different image conversion techniques. The results highlight the potential of integrating spectrogram image processing with ViT, achieving a 100% accuracy in fault diagnosis for clutch systems, thus advancing the analysis of faults in clutch systems.