Integrating Artificial Intelligence into Mechatronics: A Comprehensive Study of Its Influence on System Performance, Autonomy, and Manufacturing Efficiency

Predictive maintenance (PdM) is one of the enabling technologies of smart manufacturing in which artificial intelligence (AI) is used to predict equipment troubles, or even better anticipate the failure of equipment, based on sensor data, machine learning algorithms, and digital twins. The traditional techniques of maintenance such as the reactive maintenance technique and the preventive maintenance technique will most probably introduce undue downtimes, wastages or redundancy. In comparison, AI-oriented predictive maintenance is a combination of real-time data analytics, industrial internet (IIoT), and existing deep learning technology will allow equipment to operate 24/7, minimize the risk of operations, and make decisions. It is a review article on the use of AI-based predictive maintenance in smart manufacturing. It talks about the establishment of PdM, how AI can develop real-time faults and use machine learning, deep learning and deep reinforcement learning procedures. Providing the advantages of PdM systems, the list below states with comment of automatic saving in case of downtime, of low cost and sustainability of aerospace case, case of automotive and case of energy. The issue of the heterogeneity of the data, non-standardization, threat to cybersecurity, and explainable AI is solved. Predictive maintenance and its combination with Industry 4.0, such as digital twins, edge computing, and blockchain, is another item on the list of the ways to make manufacturing systems more autonomous and resilient. The paper ends with a definition of future directions that include hybrid AI model, federated learning of collaborative PdM, and explainable AI model of trust and adoption. Smart manufacturing ecosystems can enable all this by adding intelligence to maintenance to move to more sustainable, reliable, and adaptable operations as defined in Industry 5.0.

Artificial Intelligence (AI) and Big Data Analytics (BDA) are increasingly recognized as transformative forces that are reshaping industries, governance, and human society in profound ways. AI, with its foundations in machine learning, deep learning, natural language processing, and predictive modelling, requires vast amounts of data to train algorithms and generate reliable insights. Complementing this, BDA provides the scale, diversity, and velocity of data processing required to uncover hidden patterns, correlations, and trends within complex datasets. Together, AI and BDA form a synergistic paradigm in which Big Data serves as the fuel and AI operates as the engine, driving intelligent, data-driven innovation across multiple domains. Globally, AI-BDA applications are enabling predictive diagnostics, telemedicine platforms, genomic-driven personalized treatments, and advanced medical imaging, thereby enhancing both the accessibility and quality of care. In India, where the healthcare system must serve more than 1.4 billion citizens with limited resources, these technologies can play a pivotal role in addressing systemic challenges such as inadequate rural access, shortages of medical professionals, real-time disease surveillance, and hospital resource optimization. International best practices, including the NHS AI Lab in the United Kingdom, Mayo Clinic initiatives in the United States, and Ping a Good Doctor in China, provide models that India can adapt and scale to its unique demographic and infrastructural context. Despite their vast potential, the adoption of AI and BDA faces several barriers. Data privacy and cybersecurity remain critical concerns, particularly with the emergence of new regulations and the sensitive nature of health-related information. Additionally, algorithmic bias and the uneven availability of digital infrastructure could exacerbate existing inequities if not adequately addressed. Looking forward, advancements in Edge AI, Explainable AI (XAI), and quantum-enhanced analytics are expected to extend the scope and reliability of AI-BDA systems. This paper argues that the integration of AI and BDA is not merely a technological advancement but a paradigm shifts in how societies innovate, make decisions, and deliver essential services. Specifically, it highlights the need for a human-centric, ethical, and affordable approach to healthcare innovation in India in order to unlock the full transformative potential of these technologies. Keywords: Artificial Intelligence, Big Data Analytics, Healthcare Innovation, Predictive Diagnostics, Ethical AI, Edge AI.

The Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) intersected in the realm of industry, transforming the industrial system to allow making real-time decisions at the edge. Latency issues, bandwidth consumption, and data security also pose a challenge to the traditional cloud-based system, and are of utmost priority in the industry where time is limited. Edge AI combines AI features with edge computing, moving the intelligence to the devices in the IIoT, allowing decisions to be made faster and more responsive to the context without depending on centralized infrastructures that might overwhelm the cloud. The paper summarizes the present-day developments in edge AI as a decision-making tool in IIoT and identifies the future directions. It discusses architectures, algorithms and applications that enable intelligent decision making at the network edge with particular focus on manufacturing, predictive maintenance, supply chain optimization and energy management. It is reviewed that facilitating technologies, such as lightweight deep learning models, federated learning, and hardware accelerators, and problems, such as scalability, interoperability, and cybersecurity are discussed. As depicted in the literature section, edge AI has been found to enhance efficiency of distributed industrial systems by reducing latencies, reliability, and independent decision-making. However, barriers such as the shortage of resources, failure to interface with current systems and standardized structures still persist. It is anticipated that future studies will be based on adaptive AI models and edge-cloud collaboration with application of 6G-enabled IIoT ecosystems. This paper summarizes the synthesis of state-of-the-art methods to inform the next generation of industrial automation and digital transformation powered by edge AI. It indicates the need of powerful, hardy, and scalable systems to open up the complete scope of opportunities that edge AI can introduce to decisions made by IIoT.

Abstract: This book presents a comprehensive exploration of the convergence between Artificial Intelligence (AI) and Edge Computing as a transformative framework for optimizing Internet of Things (IoT) systems. It addresses the core challenge of deploying intelligent decision-making capabilities in latency-sensitive, bandwidth-constrained, and privacy-critical environments where traditional cloud-centric architectures prove insufficient. Through the integration of lightweight AI models, distributed inference, and federated learning, the book develops a conceptual and technical foundation for enabling scalable, real-time analytics at the edge. The methodology combines theoretical modeling with empirical case studies and performance benchmarking across diverse IoT applications, including smart cities, industrial automation, autonomous systems, and healthcare monitoring. Key AI techniques—such as reinforcement learning, spatiotemporal modeling, and knowledge distillation—are evaluated in the context of resource-constrained edge hardware. The book further explores secure and ethical deployment through privacy-preserving learning, encrypted model updates, and explainable AI frameworks aligned with international regulatory standards. Findings highlight significant improvements in system responsiveness, energy efficiency, and data sovereignty when AI is embedded at the edge. The book concludes with an assessment of emerging innovations—neuromorphic computing, quantum edge AI, and 6G-enabled edge-satellite intelligence—and proposes a cross-sectoral roadmap for future research, policy, and system design. This volume offers foundational insights for scholars, engineers, and policymakers navigating the evolution of intelligent, decentralized IoT infrastructures. Keywords Artificial Intelligence, Edge Computing, Internet of Things, IoT Optimization, Federated Learning, Real-Time Analytics, Edge AI, Reinforcement Learning, Spatiotemporal Modeling, Lightweight Neural Networks, Distributed Inference, Privacy-Preserving AI, Explainable AI, Smart Systems, Low-Latency Processing, Secure Edge Intelligence, 6G Networks, Neuromorphic Computing, Quantum AI, Autonomous IoT Systems

The growing complexity and critical importance of energy infrastructure necessitate the adoption of advanced maintenance strategies to ensure reliability, efficiency, and sustainability. Traditional maintenance approaches, such as reactive and preventive maintenance, have proven inadequate in addressing the challenges posed by modern energy systems, particularly with the integration of renewable energy sources. This research explores the potential of artificial intelligence (AI)-driven predictive maintenance (PdM) as a transformative solution for the energy sector. By leveraging historical maintenance records and real-time sensor data, AI models, including machine learning and deep learning techniques, were developed to predict equipment failures with high accuracy. The study employed a mixed-methods approach, combining quantitative analysis of data and qualitative insights from case studies conducted in wind farms, solar power plants, and thermal power plants. The results demonstrated that AI-driven PdM significantly reduces unplanned downtime, lowers maintenance costs, and extends the lifespan of critical energy assets. Deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), outperformed traditional models in terms of predictive accuracy, with F1-scores exceeding 90%. Despite the promising results, the research also identified challenges related to data quality, system integration, and organizational adoption. These challenges highlight the need for further research in areas such as explainable AI, the integration of IoT and digital twins, and the exploration of PdM applications across different sectors. The findings underscore the potential of AI-driven PdM to revolutionize maintenance practices in the energy sector, offering a pathway to more reliable, efficient, and sustainable energy systems.

Digital Twin (DT) technology, when combined with Artificial Intelligence (AI), has emerged as a transformative approach for predictive maintenance in modern industries. This paper synthesizes secondary evidence from academic literature, industry reports, and international standards (2018–2025) to examine how AI-enhanced digital twins are enabling predictive maintenance across manufacturing, energy, and transportation. Digital twins replicate physical assets virtually, while AI models analyze real-time sensor data to detect anomalies, forecast failures, and optimize maintenance schedules. Using structured analysis of ISO standards, Gartner, McKinsey, and IEEE literature, we map how digital twins integrate with IoT data streams, machine learning models, and maintenance workflows. The study identifies key enabling technologies (IoT, cloud computing, ML/DL), common architectural layers, and documented benefits, such as reduction in unplanned downtime (30–50%) and improved asset life cycles. A conceptual flowchart illustrates the AI–DT predictive maintenance loop, and a table compares sectoral adoption patterns. Findings highlight the convergence between AI analytics and DT simulations, enabling data-driven, condition-based maintenance strategies. Challenges remain in data interoperability, model updating, and cybersecurity. Future work should focus on standardized frameworks and cost-benefit models for broader adoption.

Artificial intelligence (AI) is increasingly vital in modern mineral processing, where it addresses critical challenges such as falling ore grades, rising energy costs, and the demand for sustainable operations. Despite notable progress, most existing studies focus on individual applications like ore sorting or predictive maintenance and lack a holistic view of AI-enabled mineral processing systems. This review aims to bridge that gap by examining how AI tools can be integrated into a unified workflow that spans ore characterization, sorting, and real-time process optimization. Using a structured review of research articles, industrial case studies, and technical reports from 2015 to 2025, the study evaluates key AI techniques including machine learning, computer vision, digital twins, and predictive modelling. Findings indicate that AI has improved ore recovery by up to 30% in smart sorting systems and reduced equipment downtime by as much as 50% through predictive maintenance. These results demonstrate AI’s ability to enhance both productivity and resource efficiency, though challenges related to data quality, system compatibility, and model interpretability persist. The review highlights the need for explainable AI, scalable digital twin architectures, and targeted workforce development to support wider adoption. Overall, the paper emphasizes the potential of AI to accelerate the transition toward intelligent, sustainable mining under the Mining 4.0 paradigm.

The increasing complexity of electrical power systems necessitates advanced fault detection and predictive maintenance strategies to enhance operational efficiency and grid reliability. Traditional maintenance approaches, such as reactive and preventive maintenance, have proven insufficient in mitigating unplanned outages and optimizing asset utilization. Recent advancements in artificial intelligence (AI) have introduced data-driven solutions that significantly improve fault classification, failure prediction, and automated recovery processes. This study conducts a systematic review of 180 high-quality peer-reviewed articles, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous and transparent research methodology. The findings reveal that AI-driven predictive maintenance methods, including machine learning, deep learning, digital twin technology, IoT-enabled sensor networks, and self-healing grids, have outperformed traditional fault detection techniques in terms of accuracy, adaptability, and cost-effectiveness. AI-based fault detection models achieve an average accuracy of 85% to 95%, reducing false alarms by 50% and minimizing power restoration times by up to 60%. The integration of IoT sensors with real-time analytics has improved anomaly detection rates by 28%, while digital twin technology has enhanced predictive maintenance efficiency, reducing unplanned outages by 35%. Additionally, self-healing grid mechanisms, powered by reinforcement learning algorithms, have demonstrated the ability to autonomously isolate faults and reconfigure energy distribution, preventing nearly 45% of potential service disruptions. Despite these advancements, challenges such as the black-box nature of deep learning models, cybersecurity vulnerabilities, and interoperability with legacy systems continue to pose barriers to large-scale AI adoption. The study highlights the need for explainable AI frameworks, standardized data governance policies, and enhanced cybersecurity measures to ensure the sustainable deployment of AI in power grid management. The findings provide valuable insights for researchers, utility companies, and policymakers seeking to enhance the resilience and efficiency of modern electrical power systems through AI-driven fault detection and predictive maintenance strategies.

The integration of Artificial Intelligence (AI) into cloud computing represents a pivotal shift in the technological landscape, offering unprecedented opportunities for innovation across industries. Deploying AI applications at scale on the cloud poses unique challenges, necessitating the development of scalable cloud architectures tailored to meet the computational demands and data processing needs inherent to AI. This article explores the foundational aspects of cloud computing architectures, emphasizing the principles of scalability essential for AI applications. It delves into the core challenges faced when deploying AI on the cloud, such as computational requirements, data management, network constraints, and security concerns. Further, it presents various architectural models that facilitate scalability, including containerization, serverless computing, and cloud-native AI services, drawing from realworld case studies to illustrate effective strategies and best practices. Additionally, the article examines performance optimization techniques, security considerations, and the future directions of cloud-based AI deployments, highlighting the role of emerging technologies such as quantum computing and edge AI. By providing a comprehensive overview of scalable cloud architectures for AI applications, this article aims to guide researchers, practitioners, and organizations in leveraging cloud computing to its full potential, thereby enabling more efficient, secure, and scalable AI solutions.

Artificial intelligence (AI) is revolutionizing the world we live in. It holds significant promise for improving performance across various sectors. Researchers worldwide have been working on developing solutions that leverage this technology for application by diverse user groups. A key category of AI is healthcare AI; smart solutions designed to address the needs of various stakeholders in the sector, from medical practitioners to patients. Crucial for any AI deployment are good adoption rates from the intended users and support from key stakeholders. Therefore, understanding the factors that influence its adoption is essential. This paper aims to provide insights through a systematic literature review of research related to healthcare AI adoption. Specifically, it reports on research works from ASEAN countries published over the past decade, between 2014 and 2024, identified through a systematic search of the Lens.org database. The search string focused on terms such as “Artificial Intelligence,” “Medical,” “Healthcare,” “Explainable,” “Transparency,” “Trust,” and “Adoption.” The motivation for this search stems from developments in Explainable Artificial Intelligence (XAI). XAI is vital for fostering trust and facilitating the widespread adoption of AI-driven clinical decision support systems (CDSS) in healthcare. While AI has the potential to augment clinical decision-making, the lack of transparency in traditional “black box” models can undermine trust and hinder adoption. XAI techniques aim to make AI systems more interpretable by providing explanations and visualizations of the decision-making process. This transparency is essential for overcoming skepticism and encouraging the adoption of CDSS in clinical practice. The findings of this review will offer insights into the current research landscape in the region and inform future research on the adoption and use of healthcare AI. These issues are particularly relevant in the ASEAN context, where varying levels of digital literacy and healthcare infrastructure necessitate explainable and transparent AI to build user trust and support effective integration.

The integration of artificial intelligence (AI) in predictive maintenance systems represents a transformative approach to addressing supply chain inefficiencies in utility spare-parts logistics. This research investigates how AI-driven predictive grid maintenance can substantially reduce supply chain delays through intelligent forecasting of equipment failures and optimized spare-parts inventory management. Drawing from recent advances in explainable AI and smart maintenance conceptualization, this study develops a comprehensive framework integrating machine learning algorithms, IoT sensor data, and decision-tree based prediction models. The proposed system leverages Long Short-Term Memory (LSTM) neural networks combined with Bayesian inference to predict equipment failures with 92% accuracy, enabling proactive spare-parts procurement. Through empirical analysis of utility maintenance operations, the research demonstrates that AI-driven predictive maintenance reduces supply chain lead times by 43%, decreases emergency spare-parts orders by 67%, and improves overall equipment effectiveness (OEE) by 31%. The framework incorporates explainable AI techniques to enhance stakeholder trust and decision transparency. Validation through case studies in semiconductor manufacturing and industrial IoT environments confirms the scalability and effectiveness of the approach. This research contributes to the growing body of knowledge on Industry 5.0 technologies by demonstrating how AI-driven maintenance systems can transform utility operations, reduce operational costs, and enhance grid reliability. The findings have significant implications for utility operators seeking to modernize maintenance practices and supply chain managers aiming to optimize spare-parts logistics in critical infrastructure contexts.

Artificial intelligence (AI) has emerged as a transformative technology in project management, offering advanced capabilities for data-driven decision-making, predictive analytics, and process automation. This study presents a systematic literature review of AI applications in project management, focusing on peer-reviewed journal articles published between 2015 and 2021. A total of 25 relevant studies were selected following PRISMA guidelines and analyzed using both thematic and bibliometric approaches. The findings reveal that AI is predominantly applied in areas such as project scheduling, cost estimation, risk management, safety monitoring, and project portfolio selection, with a strong concentration in construction project management. Machine learning and deep learning techniques are widely used, enabling enhanced prediction accuracy and real-time monitoring. The thematic analysis highlights key benefits, including improved efficiency, better decision-making, and enhanced safety performance. However, significant challenges remain, particularly related to data quality, model interpretability, organizational readiness, and ethical concerns. The bibliometric analysis further demonstrates that research in this field is structured around four main dimensions: AI techniques, application domains, performance outcomes, and emerging challenges. A temporal shift from traditional optimization methods to advanced data-driven approaches is also observed. This study contributes to the existing literature by providing a comprehensive synthesis of AI applications in project management and identifying critical research gaps. The findings suggest that future research should focus on explainable AI, integration with emerging technologies such as digital twins and IoT, and the development of human–AI collaborative systems to enhance project management practices.

The forthcoming of automated driving has led to vehicular heterogeneity, where vehicles with different automation levels, including connected and automated vehicles (CAVs), connected vehicles (CVs) and human-driven vehicles (HVs), coexist in the road traffic. However, the design of current traffic control systems fail to account for the vehicular traffic heterogeneity. In addition, the emerging artificial intelligence (AI) based traffic control strategies require large quantities of computation resources and generate high delay, which cannot meet the faultintolerance requirement of the traffic control system. Therefore, there is an urgent need for constructing a new traffic control framework to jointly satisfy the fault-intolerance requirement and realize ubiquitous control over the heterogeneous vehicles. Inspired by this, a digital twin (DT) empowered edge AI framework is proposed in this article. The DT's virtualization capability is utilized to enable virtual risk assessment and performance analysis under the fault-intolerant traffic control system. In addition, the DT's offline learning capability helps the edge AI understand the road traffic data more intelligently to perform enforced optimizations and decisions. Then, a three-layer vehicle control paradigm is discussed under the proposed framework. In the first layer, deep reinforcement learning (DRL) is utilized to intelligently control the CAV acceleration and lane-changing. In the second layer, indirect CV/HV control can be performed by adjusting the target speed and penetration rate of the DRLcontrolled CAVs which are mixed in the traffic flow. In the third layer, vehicle-to-everything (V2X) communications and variable message signs are utilized to realize the direct control of CVs and HVs. Experimental

The operational efficiency of many industrial processes is greatly affected by condition monitoring, which has become more and more important in the detection and forecast of electrical machine failures. Early identification of possible problems and prompt and precise diagnosis reduce unscheduled downtime, lower maintenance costs, and prevent catastrophic failures. Traditional human-dependent diagnostic techniques are changing as a result of advances in artificial intelligence (AI), opening the door to automated and predictive maintenance plans. This paper provides a detailed examination of artificial intelligence (AI) applications in the prediction of electrical device failures, with a focus on techniques such as fuzzy systems, expert systems, artificial neural networks (ANNs), and complex machine-learning algorithms. These methods use both historical and present data to identify and predict problems and allow timely actions. The study looks at implementation challenges for AI-based diagnostic systems, including data dependencies, processing demands, and model interpretability, in addition to highlighting recent advances such as digital twins, explainable AI, and IoT integration. This review highlights the revolutionary potential of artificial intelligence (AI) in improving the sustainability, efficiency, and dependability of electrical machine systems, especially in the context of rotating machines, by addressing existing constraints and suggesting future research routes.

The integration of edge computing and artificial intelligence (AI) in smart prosthetics and implants represents a significant advancement in healthcare technology, enabling ultra-fast processing, real-time decision-making, and enhanced user control. This chapter explores the critical role of edge AI in addressing latency and computational constraints in prosthetic systems, focusing on hardware acceleration techniques, real-time data acquisition, and neural signal processing. By leveraging edge computing, prosthetic devices can achieve low-latency performance, providing users with seamless interaction and improved functionality. Additionally, the chapter delves into the impact of AI algorithms in optimizing control, offering insights into adaptive learning and neural interface technologies that contribute to more intuitive and responsive prosthetics. The challenges of balancing bandwidth, latency, and computational power in edge-based prosthetic systems are also examined, with a focus on strategies for minimizing delays and enhancing system efficiency. The convergence of AI, edge computing, and prosthetic technologies promises to revolutionize the field, offering users enhanced comfort, mobility, and performance.