Predictive Maintenance Research Articles

The Role of Industrial Internship Activities to Improve Digital Competency of Engineering Students: Perception of Engineering Managers in Industry

Collaboration between the campus and the industrial world can bridge the gap that occurs between the campus and the industrial world, so that both can be integrated and synergized. This study qualitatively explored the contribution of maximizing work-integrated learning through industrial internships. The research findings from the perspective of three industry managers reveal that industrial internships can improve engineering students’ digital engineering competencies such as digital engineering observation and identification, basic and technical understanding of digital technology, work safety, predictive maintenance and programming skills to be ready to enter the workforce. The implications of the research reveal that through industrial internships students can learn from mistakes and challenges faced during industrial projects, for the development of their professional competencies. In addition, the experience and development of digital engineering competencies from the industrial world can be used as a reference for their final assignments, as well as enriching the quality of their research and academic reports, as it can bridge the gap between formal education and the demands in the ever-evolving world of work.

IoT Enabled Motor Drive Vehicle for the Early Fault Detection in New EnergyConservation

An IoT-enabled motor drive vehicle integrates Internet of Things (IoT) technology with traditional vehicle systems to enhance control, monitoring, and automation. Sensors and connected devices within the vehicle collect real-time data on parameters such as speed, battery status, motor performance, and environmental conditions. This data is transmitted to cloud-based platforms for analysis, enabling remote diagnostics, predictive maintenance, and optimization of vehicle performance. IoT integration also facilitates features like vehicle tracking, smart navigation, and user-specific adjustments, improving overall efficiency, safety, and user experience. This paper investigates the utilization of IoT enabled Programmable Logic Controller (PLC) technology to enhance fault detection in new energy vehicle (NEV) motor drive systems. With the increasing adoption of electric vehicles, ensuring the safety and reliability of motor drive systems becomes paramount. The study begins with a comprehensive review of existing literature, examining various fault detection methodologies and technologies. Subsequently, simulation analyses are conducted to evaluate the performance of IOT enabled PLC-based fault detection algorithms under different operating conditions. This paper presents the numerical results of fault detection in new energy vehicle (NEV) motor drive systems using Programmable Logic Controller (IOT enabled PLC) technology. Through comprehensive simulations and experimental validations, the IOT enabled PLC-based fault detection algorithms achieved an average detection accuracy of 93% across various fault scenarios. The response time of the fault detection system.

Digital Twins in Manufacturing: A Survey of Current Practices and Future Trends

Digital Twin (DT) is the advanced tool of smart manufacturing which provides a proper way to model, analyse, and control the physical systems. A DT is the lifelike rendition of a physical object or phenomenon that uses real-time actual phenomena data to depict its actions, efficiency, and responses within the surrounding context. This technology allows electronic manufacturers to obtain a real-time stream of their systems, in which predictive maintenance, increased operation effectiveness, and improved product quality can be attained. By developing an ever-evolving replica of the physical structures, systems or even operation lines, Digital Twins enable the functionality of exploring various demanding circumstances and schemata, all in order to make effective decisions. When the manufacturing environments are rapidly changing, and there are more interconnections between them, the use of Digital Twins is especially useful. They continue improvement by highlighting problems, forecasting failure, and making choices based on data. The purpose of this article is to examine smart manufacturing via the lens of IoT DT technology. It gives general information about its uses, such as in the processes, time-saving and safety measures. As well, the paper considers the difficulties related to the practical application of DT, including costs, data management, and requirements for the formation of standards. Thus, based on analysing future developments and the role of Digital Twins as an innovation enabler in different industries, this study can benefit scholars, practitioners, and politicians who look forward to unlocking the potential of this innovative solution.

Digital light processing of yttria-stabilized zirconia: Modeling photoinitiator decay

A digital process was developed to facilitate additive manufacturing for ceramic materials using digital light processing (DLP). A numerical model that predicts DLP sample properties can be generated from manufacturing inputs to forecast the effect of resin age on mechanical strength of the printed part based on data collected from experiments. Key parameters for printing the green bodies included determining the depth of cure, layer thickness, material composition, and solids loading. Thermogravimetric analyses were used to develop debinding and sintering curves. Debinding is used to remove the volatile organics comprising the photopolymer resin. Sintering is performed after debinding to increase density and mechanical strength of the printed parts. The sintered parts were then subjected to characterization and mechanical testing. The ensemble of data for various DLP-printed ceramic materials were added to a database. A design of experiments can be generated from the manufacturing process defined in the database with selected changeable parameters randomized over a range. Because the database is defined with an architecture to capture manufacturing processes, it can persist as a more generic platform for manufacturing digital twins. This can ease the development of future digital twins and can grow as a common repository for the insights gained from manufacturing research. Creating a digital twin of a DLP system for 3D printing parts enables manufacturers to simulate and assess the impact of resin age on printing parameters and part quality, facilitating optimization, predictive maintenance, and cost reduction.

Power Distribution Transformer Maintenance Skills Required byElectrical Engineering Technology Students ofPolytechnics inNorth-East Nigeria

Electric power distribution industry in North-East, Nigeria is faced with inadequate number of power transformer maintenance personnel which has led to numerous abandonment of broken down power transformers. Transformer faults have been blamed for many power outages of long duration in many towns and communities in North-East, Nigeria.It is against this backdrop that thisstudy determined the power distribution transformer maintenance skills required by Electrical Engineering Technology students of polytechnics in North-East Nigeria. Three research questions and three hypotheses guided the study which employed a descriptive survey research design. The population of the study was 144 comprising of 135 Electrical Engineering Technology Lecturers, 3 Power Transformer Maintenance Technicians and 6 Electric Power Distribution Company-Based Supervisors. The entire population was used hence, there was no sampling. The instrument used for data collection was a structured questionnaire titled: Power Distribution Transformer Maintenance Skills Required Questionnaire (PDTMSRQ) developed by the researchers. The instrument was validated by three experts and a reliability of 0.74 was obtained using Cronbach Alpha reliability method. Mean statistic was used to answer the research questions while ANOVA was used to test the two null hypotheses of the study at 0.05 level of significance. The findings of the study revealed that the Electrical Engineering Technology students of polytechnics in North-East Nigeria required preventive, predictive and corrective maintenance skills. The study recommended the following: Lecturers should encourage the students’ skill acquisition process by exposing them to various maintenance activities in order for them to imbibe the different methods of power transformer maintenance; Students should be trained to recognize sounds and sights of faulty equipment in order to predict the parts or components that needed replacement or repairs.

Leveraging Topological Data Analysis and AI for Advanced Manufacturing: Integrating Machine Learning and Automation for Predictive Maintenance and Process Optimization

Leveraging Topological Data Analysis and AI for Advanced Manufacturing: Integrating Machine Learning and Automation for Predictive Maintenance and Process Optimization

A Study on the Hybrid Application of Classifiers and Predictors for the Predictive Maintenance of Cooling System

A Study on the Hybrid Application of Classifiers and Predictors for the Predictive Maintenance of Cooling System

Cybersecurity in Electric and Flying Vehicles: Threats, Challenges, AI Solutions & Future Directions

Electric and Flying Vehicles (EnFVs) represent a transformative shift in transportation, promising enhanced efficiency and reduced environmental impact. However, their integration into interconnected digital ecosystems poses significant cybersecurity challenges, including cyber-physical threats, privacy vulnerabilities, and supply chain risks. This paper comprehensively explores these challenges and investigates artificial intelligence (AI)-driven solutions to bolster EnFV cybersecurity. The study begins with an overview of EnFV cybersecurity issues, emphasizing the increasing complexity of threats in digital transportation systems. Methodologically, the paper reviews existing literature to identify gaps and assesses recent advancements in AI for cybersecurity applications. Key methodologies include AI-powered intrusion detection, threat analysis leveraging machine learning algorithms, predictive maintenance strategies, and enhanced authentication protocols. Results underscore the effectiveness of AI technologies in mitigating EnFV cybersecurity risks, demonstrating improved threat detection and response capabilities. The study concludes by outlining future research directions, highlighting the need for continued innovation in AI, quantum computing resilience, blockchain applications, and ethical considerations. These findings contribute to a clearer understanding of EnFV cybersecurity dynamics and provide a roadmap for enhancing the security and reliability of future transportation systems.

A governance and risk management framework for project management in the oil and gas industry

The oil and gas industry operates in an environment marked by high risks, complex regulations, and significant financial investments. Effective project management in this sector requires a robust governance and risk management framework to address operational, regulatory, financial, and environmental challenges. This review proposes a comprehensive governance and risk management framework tailored specifically to the unique needs of oil and gas projects. The framework integrates governance structures that define clear roles, responsibilities, and decision-making processes, ensuring that projects align with corporate objectives and compliance standards. Central to this framework is a risk management model that identifies, assesses, and mitigates potential project risks. The model emphasizes continuous risk monitoring, utilizing advanced technologies such as predictive analytics, AI, and digital twins to forecast risks and optimize decision-making. Additionally, it advocates for proactive stakeholder engagement strategies to ensure alignment among diverse stakeholders, including government agencies, investors, and local communities. The governance aspect of the framework promotes transparency, accountability, and sustainability, ensuring that projects meet both operational goals and regulatory requirements. It also introduces agile project management techniques to enhance flexibility and responsiveness throughout the project lifecycle. By integrating technological tools like blockchain for contract management and AI for predictive maintenance, the framework addresses both governance and risk management challenges. Ultimately, the review serves as a guide for oil and gas companies to enhance project efficiency, minimize risks, and meet the demands of an increasingly complex industry landscape.

A Hybrid GAN-Inception Deep Learning Approach for Enhanced Coordinate-Based Acoustic Emission Source Localization

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies.

Enhancing machine optimization through AI-driven data analysis and gathering: leveraging integrated systems and hybrid technology for industrial efficiency

Industrial automation has long been a driving force in enhancing manufacturing efficiency and productivity. However, traditional systems often rely heavily on human intervention, which can introduce errors and inefficiencies. This article explores the revolutionary potential of deep learning in transforming industrial automation by minimizing human involvement and optimizing operational performance. We present a comprehensive methodology for integrating deep learning models into automation systems, focusing on improving throughput and managing downtime and failures more effectively. The study employs advanced deep learning algorithms to analyse real-time data from industrial processes, enabling predictive maintenance and automated decision-making. Key findings reveal that incorporating deep learning significantly enhances system performance by reducing downtime, preventing failures, and increasing overall throughput. Additionally, the research highlights how minimizing human intervention can lead to more reliable and efficient automation systems. The implications of these findings suggest a paradigm shift in industrial automation, where intelligent algorithms drive process optimization and operational reliability. This shift promises to enhance manufacturing capabilities, reduce operational costs, and improve overall system resilience.

Promoting Synergies to Improve Manufacturing Efficiency in Industrial Material Processing: A Systematic Review of Industry 4.0 and AI

The manufacturing industry continues to suffer from inefficiency, excessively high prices, and uncertainty over product quality. This statement remains accurate despite the increasing use of automation and the significant influence of Industry 4.0 and AI on industrial operations. This review details an extensive analysis of a substantial body of literature on artificial intelligence (AI) and Industry 4.0 to improve the efficiency of material processing in manufacturing. This document includes a summary of key information (i.e., various input tools, contributions, and application domains) on the current production system, as well as an in-depth study of relevant achievements made thus far. The major areas of attention were adaptive manufacturing, predictive maintenance, AI-driven process optimization, and quality control. This paper summarizes how Industry 4.0 technologies like Cyber-Physical Systems (CPS), the Internet of Things (IoT), and big data analytics have been utilized to enhance, supervise, and monitor industrial activities in real-time. These techniques help to increase the efficiency of material processing in the manufacturing process, based on empirical research conducted across different industrial sectors. The results indicate that Industry 4.0 and AI both significantly help to raise manufacturing sector efficiency and productivity. The fourth industrial revolution was formed by AI, technology, industry, and convergence across different engineering domains. Based on the systematic study, this article critically explores the primary limitations and identifies potential prospects that are promising for greatly expanding the efficiency of smart factories of the future by merging Industry 4.0 and AI technology.

Thermal Monitoring of an Internal Combustion Engine for Lightweight Fixed-Wing UAV Integrating PSO-Based Modelling with Condition-Based Extended Kalman Filter

The internal combustion engines of long-endurance UAVs are optimized for cruises, so they are prone to overheating during climbs, when power requests increase. To counteract the phenomenon, step-climb maneuvering is typically operated, but the intermittent high-power requests generate repeated heating–cooling cycles, which, over multiple missions, may promote thermal fatigue, performance degradation, and failure. This paper deals with the development of a model-based monitoring of the cylinder head temperature of the two-stroke engine employed in a lightweight fixed-wing long-endurance UAV, which combines a 0D thermal model derived from physical first principles with an extended Kalman filter capable to estimate the head temperature under degraded conditions. The parameters of the dynamic model, referred to as nominal condition, are defined through a particle-swarm optimization, minimizing the mean square temperature error between simulated and experimental flight data (obtaining mean and peak errors lower than 3% and 10%, respectively). The validated model is used in a so-called condition-based extended Kalman filter, which differs from a conventional one for a correction term in section prediction, leveraged as degradation symptom, based on the deviation of the model-state derivative with respect to the actual measurement. The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes.

ML-Based Maintenance and Control Process Analysis, Simulation, and Automation—A Review

Automation and digitalization in various industries towards the Industry 4.0/5.0 paradigms are rapidly progressing thanks to the use of sensors, Industrial Internet of Things (IIoT), and advanced fifth generation (5G) and sixth generation (6G) mobile networks supported by simulation and automation of processes using artificial intelligence (AI) and machine learning (ML). Ensuring the continuity of operations under different conditions is becoming a key factor. One of the most frequently requested solutions is currently predictive maintenance, i.e., the simulation and automation of maintenance processes based on ML. This article aims to extract the main trends in the area of ML-based predictive maintenance present in studies and publications, critically evaluate and compare them, and define priorities for their research and development based on our own experience and a literature review. We provide examples of how BCI-controlled predictive maintenance due to brain–computer interfaces (BCIs) play a transformative role in AI-based predictive maintenance, enabling direct human interaction with complex systems.

Predictive Maintenance with Machine Learning: A Comparative Analysis of Wind Turbines and PV Power Plants

The transition to renewable energy requires innovations in new renewable energy sources, such as wind turbines and photovoltaic (PV) systems. Challenges arise in ensuring efficient and reliable performance in their operation and maintenance. Predictive maintenance using machine learning (PdM-ML) is relevant for addressing these challenges by enhancing failure predictions and reducing downtime. This study examines the effectiveness of PdM-ML in wind turbine and PV systems by analyzing operational data, performing data preprocessing, and developing machine learning models for each system. The results indicate that the model for wind turbines can predict failures in critical components such as gearboxes and blades with high accuracy. In contrast, the model for PV systems is effective in predicting efficiency declines in inverters and solar panels. Regarding operational complexity, each model has advantages and disadvantages of its own, but when compared to conventional maintenance techniques, both provide lower costs with greater operational efficiency. In conclusion, machine learning-based predictive maintenance is a promising solution for enhancing the reliability and efficiency of renewable energy systems.

A Comprehensive Review of SCADA-Based Wind Turbine Performance and Reliability Modeling with Machine Learning Approaches

The increasing reliance on wind energy to meet global energy demands has made wind turbine performance optimization and reliability a critical area of research. Supervisory Control and Data Acquisition (SCADA) data, which provides real-time operational insights into wind turbines, plays a pivotal role in predictive maintenance, fault detection, and energy output optimization. This review explores the current methodologies and advancements in wind turbine performance modeling and reliability analysis, with a particular emphasis on machine learning (ML) approaches. Existing studies that utilize SCADA data to implement various ML models, such as decision trees, neural networks, and ensemble learning techniques (bagging, boosting, stacking), are analyzed for their effectiveness in predicting turbine failures, improving energy efficiency, and optimizing maintenance schedules. Key findings from multiple studies are synthesized, highlighting the strengths, limitations, and real-world applications of these models. Challenges in data quality, model generalization, and the implementation of real-time ML-driven systems in wind farms are also addressed. This review aims to provide a comprehensive overview of the current state of SCADA-based wind turbine analysis and to offer a roadmap for future research that bridges the gap between data-driven models and their practical deployment in wind energy systems.

Future Trends and Technological Innovations of Private AI Deployment

As data protection laws like GDPR tighten, companies must ensure their AI systems comply with regulations, leading to widespread use of encryption, federated learning, and differential privacy to protect data. Privatized AI deployment will transform enterprise operations by integrating AI with core business processes, automating and enhancing efficiency and decision-making. For instance, manufacturing firms can use privatized AI for intelligent monitoring and predictive maintenance, reducing costs and improving quality. The market landscape will shift as companies reevaluate technology and service providers, favoring those offering comprehensive privatization solutions for system security and stability. The growing demand for AI expertise will also drive educational institutions to offer more AI-related courses and training. In summary, the trends in privatized AI deployment will deeply affect business models and market structures, necessitating that companies stay abreast of technological advancements and actively embrace privatized AI opportunities for sustained innovation and growth.

Artificial Intelligence and its Applications in the Field of Internet of Things (Iot)

Artificial Intelligence (AI) and the Internet of Things (IoT), are innovative technologies that have revolutionized many fields independently. Altogether, this convergence offers a daunting possibility for innovation of a number of aspects of smartness in a range of domains on the one hand and the optimization of those aspects for sustainability on the other hand. Machine learning and deep learning help IoT devices and networks perform analytics on big data and achieve real-time decision making and automation. In smart cities AI based IoT helps in the self-organizing and self-optimizing of traffic management, and utilization of energy, and increases public safety by applying predictive analytics to the actual management of a city. The telemedicine receiving aid from AI IoT includes the detecting of sickness onset with a device that monitors patients and creates corresponding customized treatment regimens. The same way applied to IIoT where AI is implemented for predictive maintenance, quality assurance, inventory control and in turn, the achievement of alleviated downtime and improved production. Ethical issues related to bias in algorithms, and subsequent decision making via the use of Artificial Intelligence add another layer of concern in terms of deployment and utilization. Prospects for the future include such trends as edge computing, the AIoT is AI + IoT amalgamation, and the disruptive effect of quantum computing on AI applications for the IoT. The mitigation of these difficulties and the exploitation of these opportunities will open the path to proper optimizations of AI-based IoT systems as concerns effectiveness, security and/or ethics. This article stresses how AI is mandatory to the implementation of an ideal IoT system by exploring current uses, future developments, and problems that are vital for the advancement of IoT.

Optimizing hierarchical classifiers with parameter tuning and confidence scoring

Hierarchical classifiers play a crucial role in addressing complex classification tasks by breaking them down into smaller, more manageable sub-tasks. This paper continues a series of works, focused on the technical Ukrainian texts hierarchical classification, specifically the classification of repair works and spare parts used in automobile maintenance and servicing. We tackle the challenges posed by multilingual data inputs – specifically Ukrainian, Russian, and their hybrid – and the lack of standard data cleaning models for the Ukrainian language. We developed a novel classification algorithm, which employs TF-IDF victimization with unigrams and bigrams, keyword selection, and cosine similarity for classification. This paper describes a method for training and evaluating a hierarchical classification model using parameter tuning for each node in a tree structure. The training process involves initializing weights for tokens in the class tree nodes and input strings, followed by iterative parameter tuning to optimize classification accuracy. Initial weights are assigned based on predefined rules, and the iterative process adjusts these weights to achieve optimal performance. The paper also addresses the challenge of interpreting multiple confidence scores from the classification process, proposing a machine learning approach using Scikit-learn's GradientBoostingClassifier to calculate a unified confidence score. This score helps assess the classification reliability, particularly for unlabeled data, by transforming input values, generating polynomial parameters, and using logarithmic transformations and scaling. The classifier is fine-tuned using hyper parameter optimization techniques, and the final model provides a robust confidence score for classification tasks, enabling the verification and classification results optimization across large datasets. Our experimental results demonstrate significant improvements in classification performance. Overall classification accuracy nearly doubled after training, reaching 92.38 %. This research not only advances the theoretical framework of hierarchical classifiers but also provides practical solutions for processing large-scale, unlabeled datasets in the automotive industry. The developed methodology can enhance various applications, including automated customer support systems, predictive maintenance, and decision-making processes for stakeholders like insurance companies and service centers. Future work will extend this approach to more complex tasks, such as extracting and classifying information from extensive text sources like telephone call transcriptions.

THE STRATEGIC ROLE OF FPSO IN DEEPWATER EXPLORATION: INTEGRATING SOCIAL MANUFACTURING SYSTEMS WITHIN INDUSTRY 5.0

This study analyzes the role of Floating Production, Storage, and Offloading units (FPSO) in deepwater oil and gas exploration within the context of Industry 5.0, focusing on integrating social manufacturing systems. Reviewing common offshore exploration methods, the study identifies FPSOs as key catalysts for innovation, energy efficiency, and sustainability. The research concludes that combining FPSO with Industry 5.0 can optimize horizontal and vertical production processes, enhancing predictive maintenance, advanced safety, and waste management. Continuous innovation is highlighted as vital for addressing future challenges in deepwater exploration.