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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.

Electric motors represent critical assets in industrial systems, where reliability and longevity directly influence operational continuity. Although the nominal service life of induction motors is typically 20–25 years, many units remain in operation beyond this threshold under effective maintenance. This study evaluates the continued performance and insulation health of a 40-year-old, 150 kW low-tension induction motor deployed in a water transfer pump system. A comprehensive diagnostic protocol was applied, including insulation resistance, polarization index, dielectric absorption ratio, leakage current, and DC winding resistance measurements. Results indicated insulation resistance values between 2.39 GΩ and 10.3 GΩ, an R-phase polarization index of 1.87, and marginal performance in Y and B phases. Infrared thermography identified localized temperature gradients associated with incipient faults. AI-assisted analytics using a Random Forest classifier achieved an overall accuracy of 86.7% and ROC-AUC of 0.81, demonstrating moderate predictive capability. The framework illustrates the potential for integrating conventional diagnostics with data-driven decision support in condition-based maintenance applications. The motor maintained an availability of 99.94%, confirming its extended viability under structured monitoring. The combined framework merging conventional electrical diagnostics, thermal imaging, and machine-learning inference provides a scalable approach for condition-based maintenance and life-extension assessment of aged assets.

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.

Predictive maintenance (PdM) has seen significant advances through machine learning, yet its practical deployment remains challenged by data scarcity, system complexity, and uncertainty in cost-related decisions. The majority of current PdM strategies are concerned with enhancing Remaining Useful Life. Prediction accuracy of (RUL) in isolation and with maintenance scheduling as a problem (secondary or fixed). This study proposes a component based, decision oriented predictive maintenance (PdM) approach that links Remaining Useful Life (RUL) to optimization of maintenance. The two-stage framework proposed anticipates the component-specific RUL prediction where Long Short-Term Memory (LSTM) models was used to predict the Remaining Useful Life (RUL) of individual components. To address sparsity in failure data, Wasserstein Generative Adversarial Networks Gradient Penalty (WGAN-GP) were utilised to fill in run-to-failure sequences, stabilizing downstream modeling. In the second step, similar components in terms of Remaining Useful Life (RUL) degradation are clustered together by Density-Based Clustering Space (DBSCAN), which allows opportunistic maintenance. A decision on maintenance is then optimized cost-conscious grid search which works on fitted RUL distributions and a normalized. Not only based on point RUL estimates, but on a risk proxy. Empirical experimentation across multiple industrial components of a water bottling plant system indicates that the proposed approach continually reduces corrective failures and normalized maintenance costs as opposed to non-clustering approaches such as random choice and fixed choice of maintenance point. Sensitivity analysis also indicates that the optimal maintenance levels be consistent over a vast spectrum of cost assumptions, which emphasizes the resilience of the structure in economic uncertainty. Overall, this study contributes a robust and scalable maintenance literature that combines data augmentation, component based clustering, and risk aware optimization. This helps advance predictive maintenance practices into a more practical, cost aware decisions.

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.

The research examines how RL and DRL models can be used to enhance the prediction of maintenance needs in the IIoT setting. The purpose is to assess the accuracy, precision, recall, F1 score and the AUC-ROC of adaptive models against non-adaptive models. It is clear from the results that adaptive models outperform traditional models in fault prediction, providing better accuracy and more accurate predictions. Furthermore, adaptive models can handle changes in the environment and the equipment better than other models. Moreover, when these models are used with edge and cloud computing, they make sure that decisions are applied quickly and that the models can be easily integrated into industrial systems. The research also demonstrates that adaptive machine learning models can improve the accuracy of the model and reduce both false positive and false negative cases. When compared to non-adaptive baselines, adaptive models increased recall by up to 11.2% points and precision by up to 10.2% points. The Adaptive Ensemble performed best overall (93.4% accuracy, 95.2% AUC-ROC). Experimental assessment reveals consistent and statistically significant enhancements in performance for adaptive models across all criteria. The Adaptive Ensemble attains superior performance, achieving 93.4% accuracy and 95.2% AUC-ROC. In comparison to the most robust non-adaptive baseline (Random Forest), it enhances memory by 8.5% points, precision by 7.8% points, and F1-score by 8.2% points. In comparison to SVM, recall increases by 11.2% points and precision by 10.2% points, signifying significant decreases in undetected faults and false positives.The study provides information about how adaptive learning can be used in IIoT-based PdM systems and offers advice to industries that want to make their PdM systems more reliable, effective and cost-efficient.

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.