Human-Centric Smart Manufacturing under Industry 5.0:IIoT-Enabled Machine Learning for Real-Time Fault Identification and Adaptive Workforce Scheduling

Machine Learning for Predictive Maintenance in Manufacturing

The findings reveal that ML algorithms significantly improve fault detection, Remaining Useful Life (RUL) estimation, and maintenance decision-making, but challenges related to data quality, model interpretability, cybersecurity, and integration with legacy systems continue to affect implementation effectiveness.

Predictive Modeling of Failure States in Manufacturing Systems Using Artificial Intelligence in the Context of Sustainability

This study investigates predictive modeling of failure states in manufacturing systems using artificial intelligence in the context of sustainable maintenance and Industry 4.0 to point to the potential of integrating AI-supported maintenance as a tool for increasing the reliability and sustainability of manufacturing systems.

Artificial Intelligence as Effectiveness Enabler of Dynamic Reconfiguration of Systems Architecture in Industry 5.0

Industrial operations increasingly face high-stakes decisions that involve people, data streams, simulations, and control systems. Urgent sessions often require external expertise, retrieval of documents and live telemetry, running what-if simulations, and verifying safety constraints. These scenarios highlight the need for secure interoperability, explainable decision support, and human-in-the-loop control. This paper presents a proposal of a technology-agnostic reference architecture that builds on Industry 4.0 frameworks by incorporating the human-centric, resilient, and sustainable principles of Industry 5.0. Its intelligent layer enables the new approach to human involvement in the process, facilitating meaningful human–machine collaboration. The proposed research provides a practical and conceptual framework for systems engineers, industrial software architects, and operations managers seeking to transition legacy operational plants into human-aligned ecosystems. Its feasibility is evaluated through a simulation-based underground mining testbed, where heterogeneous data sources and communication protocols are integrated into a common operational environment. The proof of concept shows how telemetry, data storage, machine learning models, and operator feedback can be combined to support auditable, explainable, and human-contestable industrial decisions, demonstrating the classification accuracy, remaining useful life forecasting capabilities, and enhanced recommendation precision enabled by iterative operator feedback loops.

Design of a Fault Tolerance System for Industrial Controllers Utilizing Fractional Ensemble Learning

Smart manufacturing systems integrate technological platforms like cloud computing and computational paradigms to facilitate maintenance, monitoring, and automation. Operations and automation control based on devices or controllers depend on their connectivity and the quantity of machines they manage. This leads to further issues of task failures caused by controller interruptions. This article introduces a Fault Tolerance Computable (FTC) Fractional Learning (FL) to address this issue. The suggested computational approach utilizes ensemble learning through two fractional ensemble techniques: stacking and boosting. During the stacking process, the ratio of the preceding maximum fault tolerance rate is equated with the current failure rate. This equalization is optimized from median tolerance to a high rate via recurrent training. The training commences in the stacking state utilizing the previously provided controller logs. The boosting state offers an alternative for the existing job allocations and machine schedules derived from the equalization factor acquired in the preceding state. The controller's output for machine control facilitates swapping at different time intervals. The process is initiated between two consecutive operational states of the industrial controller to optimize task completion rates. Findings: FTC-FL improves task allocation, completion, and tolerance rate by 10.79%, 11.88%, and 9.86% respectively with 11.9% fewer failures.

AI-Powered Predictive Maintenance Framework Using Digital Twin Technology Enhancing Industrial Efficiency and Reducing Downtime

Unplanned downtime and suboptimal maintenance practices remain a significant challenge across most industrial sectors, leading to costly operational interruptions and reduced productivity. The Institute of Mechanical Engineers estimates that industrial downtime costs the global economy approximately $500 billion annually, with the majority attributed to unplanned maintenance and equipment failures. Predictive Maintenance (PdM) systems leveraging digital twin technology offer promising solutions for real-time monitoring, fault detection, and failure prediction. However, many industries find it challenging to apply these technologies effectively due to data accuracy issues, system complexity, and scaling difficulties. This paper proposes an integrated digital twin-based PdM framework that enhances fault detection and diagnosis capabilities for complex industrial machinery, particularly wind turbines. The system simulates equipment behavior, predicts potential failures, and proactively schedules maintenance to reduce unplanned downtime through the exploitation of real-time data from Internet of Things (IoT) sensors. Moreover, integrating blockchain technology ensures secure, transparent data sharing among stakeholders. The proposed framework is projected, under the stated assumptions to reduce downtime and maintenance costs by margins consistent with prior digital-twin predictive-maintenance literature, pending empirical validation. Thus, this paper contributes a novel predictive maintenance approach that integrates leading-edge technologies, providing practical insights for future deployments across diverse industrial sectors. This indicates the high potential of digital twin technology as a foundation for the applications of Industry 4.0 and will lay the ground for more robust and efficient industrial operations. This paper presents a conceptual and theoretical framework; it has not been empirically validated on physical equipment or real operational data, and the figures reported herein are illustrative projections rather than measured outcomes.

Redefining performability in Industry 5.0: integrating smart technologies for circular remanufacturing and resilient supply chains