Predictive Maintenance System Research Articles

Latest Innovations in Internet of Things (IoT): Digital Transformation Across Industries

This research article explores recent advances in IoT technology and its huge impact on various sectors. This research method uses a qualitative research approach involving in-depth interviews and thematic analysis. This research explores key IoT innovations such as wearable devices, smart medical devices, predictive maintenance systems, and IoT-based transportation solutions. The research findings highlight how these innovations are driving digital transformation across industries, leading to improved healthcare delivery, increased manufacturing efficiency, optimized transportation operations, sustainable agricultural practices, and personalized retail experiences. This study also aligns these empirical findings with theoretical frameworks regarding IoT, digital transformation, and industry-specific applications, emphasizing the strategic importance of leveraging IoT as a catalyst for innovation, competitiveness, and value creation. This research provides valuable insights for businesses, policymakers and researchers looking to leverage IoT technology to drive digital transformation and achieve sustainable growth across industries.

A Novel Advanced IoT System for Predictive Maintenance Systems and Supply Chain Optimization Solutions for Manufacturing Industries

This paper presents a pioneering approach to revolutionize manufacturing industries through the development of a novel advanced Internet of Things (IoT) system tailored for predictive maintenance and supply chain optimization. Leveraging the vast array of data generated within manufacturing environments, the proposed system integrates cutting-edge IoT technologies with advanced predictive analytics to enable proactive identification of equipment failures and optimization of supply chain operations. The research encompasses a comprehensive dataset comprising sensor data, maintenance records, and supply chain information sourced from industrial pumps, facilitating the creation of robust predictive maintenance models and insightful supply chain optimization strategies. Experimental procedures involve data pre-processing, feature engineering, and model development, followed by rigorous evaluation against ground truth data. The results showcase significant enhancements in equipment up-time; maintenance cost reduction, and supply chain responsiveness, demonstrating the efficacy and scalability of the IoT-based solutions. Through this innovative approach, manufacturing industries can achieve unprecedented levels of operational efficiency, cost savings, and competitive advantage, paving the way for transformative advancements in predictive maintenance and supply chain management paradigms.

Predictive maintenance system for high-end equipment in nuclear power plant under limited degradation knowledge

To ensure the safe operation of high-end equipment, the three-stage maintenance strategy comprising unplanned shutdown, temporary shutdown, and scheduled shutdown is currently employed in nuclear power plants. However, this strategy hinders the acquisition of degradation knowledge (run-to-failure data or degradation mechanism), thereby impeding the application of traditional predictive maintenance systems. Hence, the responsibility for determining the maintenance stage primarily lies with experienced field engineers, and an incorrect decision could potentially result in an unplanned shutdown. To this challenge, we integrate the three-stage maintenance strategy and prognosis methods to form a predictive maintenance system for nuclear power plants. The system framework is first established, and prognosis methods, including sensor selection, degradation trend prediction for short-term prognosis, and remaining useful life (RUL) prediction for long-term prognosis, are then developed under limited degradation knowledge. Finally, the system is deployed in the circulating water pump of a nuclear power plant utilizing an Internet of Things (IoT) architecture. An industry case study verifies that the proposed system can provide decision-making support and further achieve predictive maintenance for high-end equipment in nuclear power plants.

THEORETICAL FRAMEWORKS OF ECOPFM PREDICTIVE MAINTENANCE (ECOPFM) PREDICTIVE MAINTENANCE SYSTEM

The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation.
 Keywords: Predictive Maintenance, System, ECOPFM, Technology.

THE ROLE OF PRECISION METROLOGY IN ENHANCING MANUFACTURING QUALITY: A COMPREHENSIVE REVIEW

Precision metrology plays a pivotal role in modern manufacturing processes by ensuring the attainment of high-quality standards and the optimization of production efficiency. This comprehensive review delves into the significance of precision metrology in enhancing manufacturing quality across diverse industries. The review encompasses an examination of advanced metrological techniques, their applications, challenges, and future prospects. The review begins by elucidating the fundamental principles of precision metrology, emphasizing its critical role in quantifying dimensional accuracy, surface finish, and other crucial parameters in manufacturing. It explores the evolution of metrology from conventional methods to state-of-the-art technologies such as coordinate measuring machines (CMMs), optical metrology, and 3D scanning systems. The integration of these techniques into modern manufacturing environments has revolutionized quality control processes, enabling real-time monitoring and rapid feedback mechanisms. Furthermore, the review highlights the multifaceted applications of precision metrology across various industries, including automotive, aerospace, electronics, and medical devices. It elucidates how metrological advancements have facilitated the production of intricate components with tight tolerances, thereby enhancing product performance, reliability, and safety. Additionally, the review examines the role of metrology in ensuring compliance with regulatory standards and certifications, crucial for industries with stringent quality requirements. Despite its numerous benefits, precision metrology also poses challenges, such as measurement uncertainties, calibration complexities, and data interpretation issues. The review discusses these challenges in detail and explores emerging strategies and technologies aimed at mitigating them. Furthermore, it addresses the need for standardized metrological practices and international collaboration to foster interoperability and harmonization in global manufacturing ecosystems. Looking ahead, the review outlines future directions and opportunities in precision metrology, including advancements in artificial intelligence, machine learning, and Internet of Things (IoT) integration for predictive maintenance and autonomous quality control systems. It underscores the importance of continued research and innovation in metrology to address evolving manufacturing demands and ensure sustained improvements in product quality and competitiveness. In conclusion, this comprehensive review underscores the indispensable role of precision metrology in enhancing manufacturing quality, driving innovation, and shaping the future of modern industry.
 Keywords: Precision, Metrology, Manufacturing, Quality, Review.

A SPECIALTY LITERATURE REVIEW OF THE PREDICTIVE MAINTENANCE SYSTEMS

Within the industrial standard 4.0, predictive maintenance has an essential role in production activities, by increasing equipment uptime and decreasing maintenance costs. Predictive maintenance monitors assets through sensors for optimal planning of maintenance operations to keep assets functional. In this paper, the authors perform an analysis of predictive maintenance system, proposed in the specialized literature, highlighting their component elements.

Screening of Machine Learning Techniques on Predictive Maintenance: a Scoping Review

Predictive maintenance (PdM) is a set of actions and techniques to early detect failures and defects on machines before they occur, and the usage of machine learning and deep learning techniques in predictive maintenance has increased during the last years. Even with this increase of the literature, there is still a gap concerning the application of such techniques for PdM in the industry, as there are no clear guidelines about which information to use for a PdM system, how to process the information, and what machine learning techniques should be used in order to obtain acceptable results. This scoping review is performed in order to observe the current status on the use of Machine Learning and Deep Learning in predictive maintenance in academia and provide answer to the questions related to these guidelines. For this purpose, a literature review of the last five years is carried out, using those articles that cover information about sources of information used for PdM, the treatment given to such data and the machine learning (ML) methods or techniques used. The Web of Science: Core Collection database is used as a source of information, specifically the Science Citation Index Expanded (SCIE). The review shows that there are different information sources used for machine learning and deep learning in PdM, depending on the origin of the data and the availability of it, and as well whether the data sets are private or public. Also, we can observe that data used for both training and making predictions does not only use traditional pre-processing techniques, but that about one fifth of the articles even propose new techniques in this field. Additionally, we compare a wide range of techniques and algorithms which are used in Deep Learning -being ANN the most used- and in Machine Learning, being SVM the most used algorithm, closely followed by Random Forest. Based on the results, we provide indications about how to apply ML for PdM in industry. Keywords: machine learning, predictive maintenance, artificial intelligence, deep learning, data processing, data collection

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems.

Predictive maintenance of rotational machinery using deep learning

This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability.

A Type-2 Fuzzy-Based Explainable AI System for Predictive Maintenance Within the Water Pumping Industry

Industrial maintenance has undergone a paradigm shift due to the emergence of artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. Rather than accepting the drawbacks of reactive maintenance, leading firms worldwide are embracing "predict-and-prevent" maintenance. However, opaque box AI models are sophisticated and complex for the average user to comprehend and explain. This limits the AI employment in predictive maintenance, where it is vital to understand and evaluate the model before deployment. In addition, it's also important to comprehend the maintenance system's decisions. This paper presents a type-2 fuzzy-based Explainable AI (XAI) system for predictive maintenance within the water pumping industry. The proposed system is optimised via Big-Bang Big-Crunch (BB-BC), which maximises the model accuracy for predicting faults while maximising model interpretability. We evaluated the proposed system on water pumps using real-time data obtained by our hardware placed at real-world locations around the United Kingdom and compared our model with Type-1 Fuzzy Logic System (T1FLS), a Multi-Layer Perceptron (MLP) Neural Network, an effective deep learning method known as stacked autoencoders (SAEs) and an interpretable model like decision trees (DT). The proposed system predicted water pumping equipment failures with good accuracy (outperforming the T1FLS accuracy by 8.9% and DT by 529.2% while providing comparable results to SAEs and MLPs) and interpretability. The system predictions comprehend why a specific problem may occur, which leads to better and more informed customer visits to reduce equipment failure disturbances. It will be shown that 80.3% of water industry specialists strongly agree with the model's explanation, determining its acceptance.

Navigating Flood Resilience: Challenges, Solutions, and Lessons Learnt from the Dominican Republic

Recent unprecedented events worldwide, such as floods in Dubai, recurring heavy rainfall in Santo Domingo, and abrupt temperature changes in the United Kingdom (UK), underscore the tangible impacts of climate change. In response to escalating threats from natural disasters, global communities prioritise resilience and effective disaster management systems. This paper addresses best practices for managing abnormal floods, laying the foundation for the next generation of preparedness and mitigation plans. Focusing on flood risk in Santo Domingo, the study employs the Community Disaster Resilience Framework, conducting a workshop with over 100 stakeholders from government, private, and academic sectors. The assessment spans physical, economic, environmental, and social aspects, revealing common challenges in infrastructure upkeep, public awareness, urban planning, drainage, and economic disparities. The paper proposes technological solutions like predictive maintenance and smart drainage systems, emphasising the potential for implementation. Recognising the importance of community involvement and preparedness, insights from the United Kingdom guide initial steps in strategy development. The conclusions advocate for collaborative efforts among government, academia, and society to navigate the complexities of disaster management and community resilience, ultimately proposing a framework to address these challenges. Further research is suggested in expanding online platforms for disaster risk reduction education in the Caribbean region.

Machine learning and IoT – Based predictive maintenance approach for industrial applications

Unplanned outage in industry due to machine failures can lead to significant production losses and increased maintenance costs. Predictive maintenance methods use the data collected from IoT-enabled devices installed in working machines to detect incipient faults and prevent major failures. In this study, a predictive maintenance system based on machine learning algorithms, specifically AdaBoost, is presented to classify different types of machines stops in real-time with application in knitting machines. The data collected from the machines include machine speeds and steps, which were pre-processed and fed into the machine learning model to classify six types of machines stops: gate stop, feeder stop, needle stop, completed roll stop, idle stop, and lycra stop. The model is trained and optimized using a combination of hyperparameter tuning and cross-validation techniques to achieve an accuracy of 92% on the test set. The results demonstrate the potential of the proposed system to accurately classify machine stops and enable timely maintenance actions; thereby, improving the overall efficiency and productivity of the textile industry.

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

Development of a hybrid timber and aluminum based unitized façade system resilient to the future weather conditions in Europe via monitoring campaigns and computational models

The StaticusCare project aims to develop a hybrid timber and aluminum unitized façade system (HUF) equipped with a predictive maintenance system (PMS) for Nordic climates, which will be based on the digital twin concept and fed by an Internet-of-Things system. The use of timber in the structure elements of the façade system aims to reduce the typical system's CO2 footprint by 70–75 %, and the non-renewable energy consumption by 53–56 %. Nonetheless, ensuring that this novel system is durable in the Nordic current and future climate conditions is necessary. For this purpose, the HUF system will be installed in a two-floor building, monitored by a multi-sensor campaign, and replicated computationally to assess the energy use and indoor environmental quality, as well as the hygrothermal performance of the building elements for contemporary climate and under various climate change scenarios. The maintenance of the buildings with the façade system installed will be based on a PMS that is backed by an open-source python heat, air, and moisture transport (HAM) software. This one-dimensional software will be validated using a commercial one. To analyze specific problems, such as air infiltration and moisture entrapment, a two-dimensional HAM model will also be developed. In addition, building energy simulations will be performed to test several parameters affecting the indoor climate quality and energy use. Finally, the current outdoor weather files for the HAM simulations will be built based on multi-year datasets following the ISO 15927-4 methodology and the Perez model, whilst the future weather files will be based on multi-year datasets following the same methodology for two future scenarios. This multi-step methodology will allow to thoroughly test and design the HUF façade system whilst minimizing the risk, e.g., mold growth, for current and future conditions.

Resource-efficient Edge AI solution for predictive maintenance

Data-driven predictive maintenance (PM) is an approach that leverages advanced analytics, artificial intelligence (AI), and sensor data to predict when equipment failure might occur, and to perform maintenance just in time to prevent it, reducing downtime and maintenance costs. In manufacturing, one of the biggest potential applications for intelligent PM systems is tool condition monitoring (TCM). TCM aims to monitor tool wear in real-time, ensuring the quality of the manufactured products and the safety of the surrounding people and equipment. In recent decades, many studies have been carried out on tool condition monitoring for different machining operations such as milling, drilling or turning, and have demonstrated the effectiveness of various AI algorithms. Recent advances in hardware have made it possible for edge devices to run complex AI algorithms locally. This technology is called Edge AI. The Edge AI approach has several key benefits such as reduced latency, scalability, data privacy and security that can accelerate the integration of the PM solution for TCM at the production level. This paper presents the design of an Edge AI system for tool condition monitoring, consisting of state-of-the-art, low-cost components and using open-source software. Based on the proposed design, a prototype was built and tested during milling process. Four machine learning (ML) models and one deep learning (DL) model were run on a low performance edge device. Their predictions were validated. Challenges faced in the implementation are concluded along with directions and suggestions for future research.

Real-Time Predictive Maintenance System of Industrial Equipment without Historical Failure Data

Real-Time Predictive Maintenance System of Industrial Equipment without Historical Failure Data

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.

Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system.

The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks' data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system's development and validation processes. Rigorous validation confirms the system's accuracy, performance, and security through an experimental testbed. The results demonstrate the system's dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system's robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.

Development of an energy harvesting system based on a thermoelectric generator for use in online predictive maintenance systems of industrial electric motors

In the era of the Fourth Industrial Revolution, improvements in maintenance routines for industrial electric motors are continuously being made, allowing for the development of Predictive Maintenance (PdM) Online. PdM can be executed by employing small embedded systems that are affixed to electrical machines. Although a small-capacity battery can effectively power an ultra-low consumption sensor, the extensive use of batteries can contribute to increased environmental pollution. An effective approach to obviate the necessity for batteries is to employ energy-harvesting techniques from electric motors. In this regard, this paper aims to contribute to advancements in research concerning the harvesting of thermoelectric energy from industrial electric motors to create a self-sustaining Internet of Things (IoT) device. To achieve energy autonomy, the construction of a mechanical coupling for the attachment of Thermoelectric Generators (TEGs) was proposed, along with the maximization of small TEG production and the optimization of the electronic circuit for energy management. The mechanical prototype for affixing TEGs to low-power electric motors enabled a maximization of the generated energy by the TEGs, and the electronic circuit optimization process resulted in the generation of 370.10 μW, exceeding the energy requirements for making the IoT device self-sustaining by 54.21%.

Predictive Maintenance System for Rotating Machinery Onboard Ships for Detecting Performance Degradation

Maintenance of rotating machinery is crucial for extending the lifespan and increasing the reliability of equipment onboard ships. Presently, breakdown and preventive methodologies are used for the maintenance of equipment. Further, dataloggers collect critical machinery parameters, and parameter data is used for real-time parameter monitoring. The availability of such extensive monitoring data has also led to the adoption predictive maintenance methodologies in the industry, wherein machine learning-based analysis of recorded data is used to predict impending defects and prompt required maintenance. In this paper, we propose a predictive maintenance system that records data through a network of sensors installed over multiple electrical motor pump sets onboard the ship and uses statistical analysis to detect equipment degradation. Our system has been deployed onboard a ship to undertake real-time predictive maintenance of electrical motor pump sets used in firemain, AC plants, stabilizers, steering pumps and other auxiliary engine room machinery.