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In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production. This approach is generally characterized by a very precise workflow, starting with project understanding and data collection and ending with the decision-making phase. This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle of maintenance projects and the challenges encountered, and presents the models associated with this type of maintenance: condition-based maintenance (CBM), prognostics and health management (PHM), and remaining useful life (RUL). Finally, a novel applied industrial workflow of predictive maintenance is presented including the decision support phase wherein a recommendation for a predictive maintenance platform is presented. This platform ensures the management and fluid data communication between equipment throughout their life cycle in the context of smart maintenance.

PurposeThe after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.Design/methodology/approachWe propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.FindingsWe found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.Research limitations/implicationsThis approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.Practical implicationsThis approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.Social implicationsSustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.Originality/valueThis is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

Industry 4.0 has revolutionized the use of physical and digital systems while playing a vital role in the digitalization of maintenance plans for physical assets in an optimal way. Road network conditions and timely maintenance plans are essential in the predictive maintenance (PdM) of a road. We developed a PdM-based approach that uses pre-trained deep learning models to recognize and detect the road crack types effectively and efficiently. We, in this work, explore the use of deep neural networks to classify roads based on the amount of deterioration. This is done by training the network to identify various types of cracks, corrugation, upheaval, potholes, and other types of road damage. Based on the amount and severity of the damage, we can determine the degradation percentage and have a PdM framework where we can identify the intensity of damage occurrence and, thus, prioritize the maintenance decisions. The inspection authorities and stakeholders can make maintenance decisions for certain types of damages using our deep learning-based road predictive maintenance framework. We evaluated our approach using precision, recall, F1-score, intersection-over-union, structural similarity index, and mean average precision measures, and found that our proposed framework achieved significant performance.

Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation.

In this paper, the concept of hybrid predictive maintenance for a single industrial machine is presented. A review of the solutions in the area of machine maintenance (especially predictive maintenance) which have been described in the literature is provided. The assumptions of the hybrid predictive maintenance model for modules, machines, or systems are presented. The methods used within the developed methodology are described. This includes the use of diagnostic data, experience, and a mathematical model. A case study of an industrial machine on which a system for collecting diag-nostic data has been pilot-implemented, using, among others, vibration sensors and drive system pa-rameters for damage detection is presented. The registered data can be used to precisely determine the time of upcoming failure after detection of the characteristic symptoms resulting from component wear In addition, an analysis of the durations of correct operation and failure events was performed and indicators describing these values were determined. The values of the aforementioned indicators were determined based on empirical data and described using a gamma distribution. The objective of the research was to prepare, implement and draw conclusions on a hybrid predictive maintenance model. A real industrial machine was used in the research study. The hybrid predictive maintenance model presented in this paper enables the use of data of different types (diagnostic, historical and mathemat-ical model-based) in scheduling machine downtime for maintenance actions. On the basis of the re-search conducted, it was determined which machine operating parameters are characterised by varia-bility that enables the detection of upcoming failure. This allows for precise planning of maintenance activities and minimization of unplanned downtime.

Purpose>Production industries are undergoing a digital transition, referred to as the fourth industrial revolution or Industry 4.0, as a result of rapidly expanding advances in information and communication technology. The purpose of this research is to provide a conceptual insight into the impact of unique capabilities from the fourth industrial revolution on production and maintenance tasks in terms of providing the existing production companies a boost by making recommendations on areas and tasks of great potential.Design/methodology/approach>A survey and a literature review are among the research methods used in the research. The survey collected empirical data using a semi-structured questionnaire, which provided a broad overview of the company's present condition in terms of production and maintenance, resulting in more comprehensive and specific information regarding the study topics.Findings>The study points out that, the implementation of I4.0-technology leads to an increase in production, asset utilization, quality, reduced machine down time in industries, and maintenance. Sensor technology, big data analysis, cloud technologies, mobile end devices, and real-time location systems are now being implemented to improve production processes and boost organizational competitiveness. Moreover, the study highlights that data acquired throughout the production process is utilized for quality control, predictive maintenance, and automatic production control. Furthermore, I4.0 solutions help companies to be more efficient with assets at each stage of the process, allowing them to have a stronger control on inventories and operational-optimization potential.Originality/value>The findings of the study was supported by empirical data collected through survey that provides an intangible understanding of the importance of distinctive capabilities from the I4.0 revolution on production and maintenance tasks. In this study, some recommendations and guidelines to enhance these tasks are provided that are vital for existing production companies.

This paper introduces a predictive maintenance model based on Machine Learning (ML) in the context of a smart factory. It addresses a critical aspect within factories which is the health assessment of vital machinery. This case study specifically focuses on two brass accessories assembly robots and predicts the degradation of their power transmitters, which operate under severe mechanical and thermal conditions. The paper presents a predictive model based on ML and Artificial Intelligence (the Discrete Bayes Filter) to estimate and foresee the gradual deterioration of robots’ power transmitters. It aims at empowering operators to make informed decisions regarding maintenance interventions. The model is based on a Discrete Bayesian Filter (DBF) in comparison to a model based on Naïve Bayes Filter (NBF). The findings indicate that the DBF model demonstrates superior predictive performance compared to the NBF model. The predictive model’s investigation results were validated during testing on robots. This model enables the company to establish an informed and efficient schedule for maintenance interventions.

In this paper we introduce a systemic approach for predictive and prescriptive maintenance framed within the larger system of systems, as exemplified by a use case in mining. Developments are presented as systematic, while there is a focus on improved availability modeling using the time usage model with a corresponding UML/SysML StateMachine representation. Data becomes connected with a more elaborated definition of time, with failure modes and analytics in reliability engineering being supported by improved underlying information structures.

Predictive maintenance (PdM) is one of the major methods used in modern manufacturing to realize downtime minimization, lower the cost of maintenance and maximize machine service life by analyzing the collected data using data mining methodologies. However existing works mainly focus on conventional ML models without provide systems design real world applications systems and do not include any dimension related to network security dimension, cost and benefit analyzing dimension utility dimension and light weight A.I model for edge computing. In this paper, we contribute with a systematic literature review of state-of-the-art data-mining techniques for predictive maintenance with emphasis on hybrid AI frameworks, deep learning and online data processing approaches, as well as, privacy-aware methods. We contribute by providing a number of real-world industrial use case which differentiate us from previous researched; we discuss details of cybersecurity issues in IoT-enabled PdM; and we discuss use of XAI (Explainable AI) to build interpretable models. Moreover, this survey introduces marginal AI applications in edge computing, predictive maintenance frameworks with scalability, and AI-powered anomaly identification for enhancing predictions in industrial-scale production. It also covers a review of predictive maintenance methodologies in addition to a future research agenda, highlighting emerging patterns such as digital twins, Industry 5.0, and reinforcement learning in predictive maintenance. The current study aims to bridge critical gaps in the literature and support valuable direction for researchers, industry practitioners and policymakers for effective predictive maintenance strategies and task performance.

This research paper presents the development of an AI-based predictive maintenance application for CNC machines. The objective of this research is to optimize the maintenance process and reduce unplanned downtime through real-time analysis of sensor data. It incorporates a Random Forest algorithm. This application uses long short-term memory networks for fault classification to estimate the remaining useful life. Both models have F1-scores of 0.90 to 0.92 and perform well in fault classification. Experimental results show that LSTM achieves superior RUL prediction with an MAE of 72 hours. The applicability of the application at an industrial scale has been proven, which brings significant practical benefits. The developed system reduces unplanned downtime by 25–30% and reduces maintenance costs by 15–20%. The app uses real-time sensor data to predict potential machine failures. This shows significant practical benefits for industrial deployment. It has excellent ability to capture temporal dependencies in time-series data, while both models show performance in fault classification. This work demonstrates the deployment of AI for industrial PDM, highlighting the benefits and challenges encountered during development, and suggests future improvements to enhance its wider applicability.