Advancements in Industry 4.0 technologies, which combine big data analytics, robotics, and intelligent decision systems to enable new ways to increase automation in the industrial sector, have undergone significant transformations. In this research, a Hybrid Attention-Gated Recurrent Unit (At-GRU) model, combined with Sand Cat Optimization (SCO), is proposed to enhance fault identification and predictive maintenance capabilities. The model utilized multivariate sensor data from cyber-physical and IoT-enabled robotic platforms to learn operational patterns and predict failures with enhanced reliability. The At-GRU provides deeper temporal feature extraction, thereby improving classification performance. The robustness of the proposed model is validated through analysis of a benchmark dataset for industrial robots, and the results demonstrate that the proposed model exhibits impressive predictive capacity, surpassing other prediction methods and predictive maintenance approaches. Additionally, the performance evaluation indicates a lower computational cost due to the lightweight gating architecture of GRU, combined with attention. The robotic motion is further optimized by the SCO algorithm, which reduces energy usage, execution delay, and trajectory deviations while ensuring smooth operation. Overall, the proposed work offers an intelligent and scalable solution for next-generation industrial automation systems. Furthermore, the proposed model demonstrates the real-world applicability and significant benefits of incorporating hybrid artificial intelligence models into real-time robot control applications for smart manufacturing environments.

Predictive maintenance based on vibration monitoring can significantly improve gearbox reliability in heavy-industry environments. Although it is well established in vibration engineering that operating regimes influence vibration levels, the contribution of this work lies in providing an integrated, data-driven diagnostic linkage between continuously acquired IoT vibration indicators and key process/operational variables to identify and quantify the dominant drivers of vibration escalation. This study deployed wireless IoT sensors for continuous acquisition of RMS vibration and lubrication temperature in gearboxes operating in cement and mining plants and applied multivariate machine learning models to detect anomalies and identify root causes. We compared boosted multilayer feedforward neural networks, boosted trees, and k-nearest neighbors using RMS vibration and process variables including mill feed, lubrication pressures, and temperature. The boosted neural network delivered superior validation performance and isolated low or near-zero mill feed during operation as the primary driver of elevated RMS vibration, with lubrication instability acting as a secondary interacting factor. This shifts the diagnosis from a generic “high vibration during transients” statement to actionable operational mitigations—minimum feed set-points, controlled ramping logic, and lubrication pressure governance—supported by multivariate evidence. Our results motivate further validation with k-fold and out-of-time tests.

Purpose: Substation equipment and power transformers constitute vital parts of the modern power systems, and its proper functioning is the key to the system stability, efficiency, and safety. Sensors predictive maintenance uses predictive maintenance based on sensor data which is a time-series of data to propose proactive methods to monitor asset health, anomaly detection and predictive maintenance, thus minimizing unplanned outages and maximizing maintenance schedules. Materials and Methods: This review gives an overall overview of sensor technologies, data features and modeling techniques used in predictive maintenance of transformers and substation equipment. The classical models of statistics, machine learning methods, and deep learning systems are addressed in terms of condition monitoring, anomaly detection, and remaining useful life estimation. Problems such as data quality, model interpretability and deployment concerns are discussed and future research directions such as digital twins, physics-informed models, Edge-AI and secure cloud-edge are identified to inform the further development of the field. Findings: Additionally, the review highlights predictive models for estimating the remaining useful life (RUL) of assets to optimize maintenance planning. Unique Contribution to Theory, Practice, and Policy: This review provides a comprehensive understanding of predictive maintenance techniques for transformers and substation equipment. It contributes to theory by summarizing and evaluating various models and methods used in the field. In practice, it offers insight into current and future technologies for asset management and maintenance. The identification of future research areas like digital twins, Edge-AI, and secure cloud-edge will help to drive future developments and influence policy in the power systems sector.

Abstract Hydropower sector is undergoing a transformation of replacing old analog control systems and leveraging the effect of digitalization to increase operational flexibility, production and safety using digital elements like sensors, wireless platforms, real time monitoring, predictive maintenance and decision support systems etc.. Implementation of digitalization to hydropower plants has a potential of 42 TWh increase in annual production worldwide, hence creates a potential for USD 5 billion annual operational cost savings and reduction in greenhouse gas emissions. One of the critical and common applications of digitalization in hydropower sector is unmanned/remote operated hydropower plants, of which there are several examples of in the Water-Energy nexus projects. In Water Distribution Networks (WDN), Pressure Reduction Valves (PRV) are the most common tools to manage excessive pressure due to the topography, resulting in energy waste. To harvest this waste energy, micro hydropower plants can be used for pressure reduction and the harvested energy can be used in remote areas lacking grid connectivity or directly supplied to the local grid. Digitalization has potential advantages on both micro, mini and small sized hydropower plants installed in water networks such as optimal control of water pressure using digital twins of the WDNs, autonomous operation of the plants and proactive or predictive maintenance to ensure trouble-free operation. In this paper, we present the insights from a technical point of view from a practical Water-Energy nexus project and from a short term scientific mission study conducted with a COST action PEN@Hydropower member institute. This research aims to reveal main challenges to be encountered during implementation of digital control and monitoring solutions in a small hydropower plant, including hands on observations during erection, commissioning and operation phases. Review on data collection and storage issues from critical equipment, cleaning out the collected data for analysis and machine learning applications, cyber security issues brought by digital transformation along with the convenience in installation and operation it brings is presented as a guide for future research.

ABSTRACT Dust accumulation significantly degrades the energy output of photovoltaic (PV) panels, particularly in arid and semi‐arid regions. While existing studies have separately explored image‐based dust detection, environmental modeling, and machine learning (ML) for performance prediction, few have integrated these approaches to capture the coupled optical‐thermal effects of soiling. This study proposes a novel integrated framework that combines fuzzy clustering for panel segmentation, a hybrid SVM–fuzzy logic classifier for dust detection using intensity‐texture features, and a semi‐empirical plus ML–based thermal model. The framework uniquely fuses image‐derived dust maps with real‐time meteorological data, including humidity, ambient temperature, solar zenith angle (SZA), and global horizontal irradiance (GHI)–form a 5‐year dataset for model spatially non‐uniform solar absorption and thermal behavior. Experimental validation using reference dust loads of 3, 5.001, and 8 g·m⁻ 2 across multiple PV panels yielded mean absolute errors of approximately 0.07, 0.12, and 0.18 g·m⁻ 2 , respectively. The thermal coefficient α was estimated through environmentally driven regression, providing a physically consistent, dust‐informed temperature assessment suitable for real‐time monitoring and predictive maintenance. This work advances the state‐of‐the‐art by offering a lightweight, interpretable, and integrated solution that outperforms fragmented approaches in accuracy and practical deployability.

Acoustic emission (AE) sensors offer significant potential for early fault detection in rotating machinery through the monitoring of high-frequency transients. However, extracting effective features from complex AE signals remains challenging for automated fault severity classification across multiple damage mechanisms. This study investigates multi-scale entropy methods for extracting a computationally efficient set of 16 non-linear information entropy features from AE signals to diagnose gearbox fault severity. Three approaches were systematically compared: Composite Multi-Scale Entropy (CMSE), Hierarchical Multi-Scale Entropy (HMSE), and Composite Hierarchical Multi-Scale Entropy (CHMSE). Experimental data were collected from a spur gearbox test rig operating under controlled conditions, with artificially induced faults representing four damage mechanisms (pitting, broken teeth, root cracks, and scuffing) at nine severity levels each, providing the most granular assessment reported in the entropy-based fault diagnosis literature. Features extracted using each multi-scale method were classified using several classical machine learning models. The CHMSE combined with Random Forests (RF) models achieved the highest classification accuracy (97.37-99.50%), representing a 1-4% improvement over conventional single-scale methods and demonstrating superior performance compared to statistical features and alternative machine learning models. SHAP-based interpretability analysis revealed that generalized entropy measures, specifically Rényi entropy and Tsallis entropy, emerge as primary discriminators across CMSE, HMSE, and CHMSE approaches, with threshold entropy and log energy entropy demonstrating substantial discriminative power when combined with hierarchical decomposition methods (HMSE and CHMSE). Statistical analysis confirmed significant performance improvements (p <0.05) for the hierarchical approaches. These findings demonstrate that CHMSE-based feature extraction enables reliable AE-based condition-monitoring systems for predictive maintenance in industrial gearboxes.

Abstract Imagine grappling with a sprawling codebase where hidden complexities threaten to derail your next release—that's the reality for many developers. This paper introduces "AI Architecture Pro," an intuitive tool designed to transform code analysis from a tedious chore into a proactive ally. By harnessing static analysis, graph theory, machine learning (ML), and large language models (LLMs), it effortlessly dissects polyglot codebases in Python, JavaScript, and Java, unveiling architectural insights that traditional tools overlook. At its core, the tool—built as a user-friendly Streamlit web app—employs libraries like NetworkX for dependency graphs, Radon for precise metrics, and Groq for intelligent AI chat. Developers can explore interactive 3D visualizations of "code cities," where skyscrapers represent complexity, and receive ML-driven predictions on refactoring risks. It also generates clustered architecture maps and radar charts for technical debt assessment, all while supporting GitHub cloning, local paths, or pasted code for flexibility. Evaluations on real-world repositories show it pinpointing high-risk functions with 85% accuracy and boosting maintainability scores by up to 20%. Ultimately, "Code Architect AI" bridges the divide between raw metrics and actionable wisdom, empowering teams to refactor smarter and build more resilient software. Keywords:- code analysis, Software Architecture,Machine Learning, Static Analysis, Visualizations, AI -Assisted Development, Dependency Graph, Predictive Maintenance.

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening.

The food processing industry is undergoing a profound transformation with the integration of Artificial Intelligence (AI), evolving from traditional automation to intelligent, adaptive systems aligned with Industry 5.0 principles. This review examines AI’s role across the food value chain, including supply chain management, quality control, process optimization in key unit operations, and emerging areas. Recent advancements in machine learning (ML), computer vision, and predictive analytics have significantly improved detection in food processing, achieving accuracy exceeding 98%. These technologies have also contributed to energy savings of 15–20% and reduced waste through real-time process optimization and predictive maintenance. The integration of blockchain and Internet of Things (IoT) technologies further strengthens traceability and sustainability across the supply chain, while generative AI accelerates the development of novel food products. Despite these benefits, several challenges persist, including substantial implementation costs, heterogeneous data sources, ethical considerations related to workforce displacement, and the opaque, “black box” nature of many AI models. Moreover, the effectiveness of AI solutions remains context-dependent; some studies report only marginal improvements in dynamic or data-poor environments. Looking ahead, the sector is expected to embrace autonomous manufacturing, edge computing, and bio-computing, with projections indicating that the AI market in food processing could approach $90 billion by 2030.

A new, cost-efficient modular sensor platform for IoT and predictive maintenance in industrial applications

Predictive group maintenance using probabilistic prognostics and deep reinforcement learning

Predictive Maintenance of Metro Rail Station Facilities Using Tree-Based Machine-Learning Algorithms

A data-driven predictive maintenance framework for smart buildings: Integrating digital twin and machine learning in HVAC systems

Adaptive mission risk control under incomplete health information and resource limitation: A constrained multi-state predictive maintenance model

Modeling switch rail wear using laser profilometry for predictive maintenance

Univariate anomaly detection in pressure data of a pilot aerobic membrane bioreactor unit using long short-term memory autoencoders.

Toll roads have a strategic role in supporting the mobility and distribution of goods in Indonesia, so the fulfillment of the Standar Pelayanan Minimal (SPM) is important to ensure safety, comfort, and service efficiency. This study evaluates the implementation of SPM in six national toll road corridors based on the Regulation of the Minister of Pekerjaan Umum dan Perumahan Rakyat (PUPR) Number 16/PRT/M/2014. The results of the study show that in general toll road services have met standards, especially in the indicators of average travel speed, transaction speed, and vehicle queue length at toll gates. However, unevenness and roughness of the pavement are still the main obstacles, which is reflected in the Surabaya-Gresik and Ciawi-Sukabumi sections due to unevenness of the pavement, as well as on the Cikampek-Palimanan section and the Ujung Pandang Toll Road Sections 1-3 due to the unavailability of up-to-date periodic monitoring data. The novelty of this study lies in the mapping of cross-corridor SPM fulfillment which shows that the decline in service performance is more influenced by operational factors and vehicle load over dimension over load (ODOL) on the logistics line than the quality of the initial construction. These findings confirm the importance of integrating information technology in predictive maintenance systems and adaptive traffic management to detect pavement degradation early. The limitation of this study lies in the use of secondary data, so that administrative discipline in updating operational data is an important factor for Badan Usaha Jalan Tol (BUJT) in supporting accurate and transparent decision-making.

Rail transport is central to achieving sustainable and energy-efficient mobility, and its digitalization is accelerating the adoption of condition-based maintenance (CBM) strategies. However, existing maintenance practices remain largely reactive or rely on limited rule-based diagnostics, which constrain safety, interoperability, and lifecycle optimization. This survey provides a comprehensive and structured review of Artificial Intelligence techniques applied to the preventive, predictive, and prescriptive maintenance of railway infrastructure. We analyze and compare machine learning and deep learning approaches—including neural networks, support vector machines, random forests, genetic algorithms, and end-to-end deep models—applied to parameters such as track geometry, vibration-based monitoring, and imaging-based inspection. The survey highlights the dominant data sources and feature engineering techniques, evaluates the model performance across subsystems, and identifies research gaps related to data quality, cross-network generalization, model robustness, and integration with real-time asset management platforms. We further discuss emerging research directions, including Digital Twins, edge AI, and Cyber–Physical predictive systems, which position AI as an enabler of autonomous infrastructure management. This survey defines the key challenges and opportunities to guide future research and standardization in intelligent railway maintenance ecosystems.

Artificial intelligence (AI) is increasingly adopted in manufacturing for tasks such as automated inspection, predictive maintenance, and condition monitoring. However, the opaque, black-box nature of many AI models remains a major barrier to industrial trust, acceptance, and regulatory compliance. This study investigates how explainable artificial intelligence (XAI) techniques can be used to systematically open and interpret the internal reasoning of AI systems commonly deployed in manufacturing, rather than to optimise or compare model performance. A unified explainability-centred framework is proposed and applied across three representative manufacturing use cases encompassing heterogeneous data modalities and learning paradigms: vision-based classification of casting defects, vision-based localisation of metal surface defects, and unsupervised acoustic anomaly detection for machine condition monitoring. Diverse models are intentionally employed as representative black-box decision-makers to evaluate whether XAI methods can provide consistent, physically meaningful explanations independent of model architecture, task formulation, or supervision strategy. A range of established XAI techniques, including Grad-CAM, Integrated Gradients, Saliency Maps, Occlusion Sensitivity, and SHAP, are applied to expose model attention, feature relevance, and decision drivers across visual and acoustic domains. The results demonstrate that XAI enables alignment between model behaviour and physically interpretable defect and fault mechanisms, supporting transparent, auditable, and human-interpretable decision-making. By positioning explainability as a core operational requirement rather than a post hoc visual aid, this work contributes a cross-modal framework for trustworthy AI in manufacturing, aligned with Industry 5.0 principles, human-in-the-loop oversight, and emerging expectations for transparent and accountable industrial AI systems.

Addressing climate change necessitates innovative and data-driven approaches for monitoring, managing, and reducing greenhouse gas (GHG) emissions across various sectors. Digital Twin Technology (DTT)—which creates dynamic, real-time digital replicas of physical systems— emerges as a transformative tool in this context. This work explores the potential of DTT to accelerate emission reduction efforts in key domains including energy, manufacturing, transportation, and urban infrastructure. By enabling real-time monitoring, predictive maintenance, and system-level optimization, DTT enhances operational efficiency while minimizing environmental impact. The study presents a comprehensive review of current literature, proposes a machine learning–based methodology for forecasting emissions, and introduces a robust evaluation framework. Case studies from recent implementations are analyzed to demonstrate the tangible benefits and scalability of DTT in achieving decarbonization goals. Findings confirm that Digital Twin Technology plays a critical role in advancing toward net-zero emissions and fostering long-term sustainability.