Reduction Of Downtime Research Articles

Application of DFMA in lock band service modification of ATB-I3 building machine

The locking mechanism on the band service, known as the lock band service, is used to move materials to the tire-making machine. A building machine is a device that assembles components into partially finished or green tires. The components are tread, which acts as the tire tread, band/ply, which are fabric sheets coated in rubber, and bead wire. Subsequently, the materials are put together on the drum-mounted construction machine. The ATB-I3 machine has the largest downtime owing to a band service fault, with 840 minutes in January 2024, and is the problem with the highest downtime in section A2. The cause of the excessive downtime in the band service is an issue with the band service lock system, namely the pneumatic movement lock mechanism on the band service component. As a result, adjustments must be made by altering the lock band service's design. After that, a weld joint strength analysis is performed to make sure the modification can be safely put into practice. To obtain the optimal modification design, the DFMA method is employed. With the use of the DFMA approach, the updated design—which had a design efficiency value of 4.23% to 4.31% before—was improved. The welding connection in the updated design is safe to use since, according to the results of the welding connection calculation, the lock band service holder has a maximum welding load of 294,645.08 N whereas the actual welding load is 147,928 N. The reduction in service band downtime was tested with this improvement. Following the implementation of the update in March 2024, the amount of downtime resulting from damage to the band service dropped by 445 minutes, or 52.97%, to 395 minutes

Technological Aspects of using 3D Printing Software in Construction

The relevance of introducing additive technologies of 3D printing in construction is to decrease the technological cycle of construction, and minimize expenditure and material costs, while reducing the duration of the development planning process and execution, eliminating inaccuracies and defects, as well as optimizing and automate the process. The technological aspect of 3D printing lies in the optimal selection of parameters based on the created 3D model using effective software. The purpose of the study is to determine the quality indicators of bricks obtained by 3D printing from special concrete. The approach to investigating this matter is rooted in ascertaining the compressive strength of concrete through non-invasive testing techniques, gauging the density of concrete using measurement methods, and validating the efficacy of integrating 3D printing technologies in construction by the method of comparative analysis. The effectiveness of introducing additive technologies of 3D printing was proven drawing upon quality indicators of concrete obtained on a 3D printer which meets all the requirements necessary for its implementation in the construction field. The selected characteristics of concrete compressive strength are fundamental to the basics of selecting a brand of concrete to be utilized in construction. The selected software demonstrated the capacity to generate flawless 3D designs. The result of the study shows a significant reduction in the number of workers involved in the construction process by 38–45%, depending on the chosen method of using 3D printing; a decrease of construction time by 33–42% when printing individual elements, blocks used in production, and by 61–72% when using a printer directly on the construction site; reduction of raw materials costs by 14–28%; as well as reduction of construction downtime by 25%. The results obtained indicate the need to develop and enhance progressive additive technologies for 3D printing in construction.

Artificial general intelligence for the upstream geoenergy industry: A review

Artificial General Intelligence (AGI) is set to profoundly impact the traditional upstream geoenergy industry (i.e., geothermal energy, oil and gas industry) by introducing unprecedented efficiencies and innovations. This paper explores AGI's foundational principles and its transformative applications, particularly focusing on the advancements brought about by large language models (LLMs) and extensive computer vision systems in the upstream sectors of the industry. The integration of Artificial Intelligence (AI) has already begun reshaping the upstream geoenergy landscape, offering enhancements in production optimization, downtime reduction, safety improvements, and advancements in exploration and drilling techniques. These technologies streamline logistics, minimize maintenance costs, automate monotonous tasks, refine decision-making processes, foster team collaboration, and amplify profitability through error reduction and actionable insights extraction. Despite these advancements, the deployment of AI technologies faces challenges, including the necessity for skilled professionals for implementation and the limitations of model training on constrained datasets, which affects the models' adaptability across different contexts. The advent of generative AI, exemplified by innovations like ChatGPT and the Segment Anything Model (SAM), heralds a new era of high-density innovation. These developments highlight a shift towards natural language interfaces and domain-knowledge-driven AI, promising more accessible and tailored solutions for the upstream geoenergy industry. This review articulates the vast potential AGI holds for tackling complex operational challenges within the upstream geoenergy industry, requiring near-human levels of intelligence. We discussed the promising applications, the hurdles of large-scale AGI model deployment, and the necessity for domain-specific knowledge in maximizing the benefits of these technologies.

Active mission assignment for improving the effectiveness of opportunistic maintenance in a fleet of mission-critical systems

In a fleet of mission-critical systems, maintaining high availability for each individual system is vital to ensure consistent fleet readiness to meet mission demands. This study introduces a novel mission assignment strategy aimed at actively aligning maintenance schedules within each system to maximise opportunistic maintenance potential, thus enhancing system availability. However, solely focusing on system-level assignments may result in simultaneous maintenance needs across the fleet, leading to resource conflicts. The proposed strategy addresses this issue by coordinating mission assignments to consider both the alignment of schedules within a system and the even distribution of schedules among systems to prevent fleet-level conflicts. Two operational conditions, intra-system and inter-system balance, are defined to evaluate the criticality of each system at both system and fleet levels. Based on these conditions, scheduling rules are developed to determine assignment priority across the fleet in a two-phase process. Simulation studies demonstrate the effectiveness of this approach, showing a 2% improvement in system availability, equivalent to a two-month reduction in downtime across the fleet over a one-year period. The results underscore the advantages of the proposed strategy in optimising maintenance operations in complex fleet environments with diverse mission requirements.

Parametric multi-level modeling of Lean Six Sigma

Lean Six Sigma is a methodology that combines Lean manufacturing and Six Sigma into a single framework for process improvement. This combination merges their tools and techniques to overcome their deficiencies while achieving bottom-line improvement. However, existing literature emphasizes general LSS implementation with very little work on modeling the interaction between Leanness and Six Sigma. This work developed a multi-level model containing leanness and Six Sigma levels to generalize LSS implementation. Three hierarchical levels—Six Sigma (level 3), leanness (level 2), and a fattening level (level 1—Sigma quality level) are defined for two models (parametrized and non-parametrized). Aggregate measures (process efficiency, asset intensity, production time, etc.) are considered at levels 2 and 3 to achieve the desired optimization goal at that level. For testing, three process measures [asset intensity (AI), planned stoppages (PS), wastes and rework (WR)] from the manufacturing data of a global brand spanning 18 months were analyzed. The maximum log-likelihood method was used to estimate the model parameters and they converged to 85.2%, 11.2%, and 8.87% for AI, PS, and WR respectively, with a strong correlation between AI and PS. A metaheuristic algorithm was extended to solve both models. The obtained process yield (0.111111 and 0.4132471), defects per million opportunities (DPMO) (12,426 and 18,046), and sigma level (3.6 and 3.74) for the parametrized and non-parametrized models respectively indicated optimal model performance and a need for the strategic use of LSS tools in improving the production output. Also, further reduction in wastes, stoppages, and downtime was observed upon tuning the parametric variables. This model provides a mathematical approach to analyzing LSS implementation and provides a basis for future adaptation to any organization.

A Deep Learning-Based Approach for Identifying Defects in Solar Panels

In the solar energy sector, the task of monitoring and maintaining large photovoltaic (PV) system portfolios is essential for ensuring optimal performance and reliability. Prominent solar energy companies face challenges with their current fault detection methods, which are inefficient and resource- intensive. This paper addresses the critical need for improved fault detection in solar PV systems to maximize uptime and minimize maintenance costs. We employed advanced data preprocessing and augmentation techniques using Roboflow and developed a YOLOv8 segmentation model in Google Colab with GPU. This model was then deployed using Streamlit, providing a robust solution for identifying faulty solar modules. The proposed approach significantly enhances fault detection accuracy, achieving a minimum accuracy rate of 85%, thus ensuring reliable operation of the PV systems. Additionally, the implementation of this model contributes to a 15% reduction in system downtime and a 10% reduction in maintenance costs. By leveraging advanced machine learning techniques, our solution transforms the maintenance process, making it more efficient and cost-effective. Consequently, this work not only improves the reliability and performance of solar PV systems but also supports the broader goal of sustainable energy through more efficient resource usage.

Modernizing Mainframe Monitoring: A Comparative Analysis of Observability Dashboarding Tools

Abstract: This article explores the critical role of observability dashboarding in mainframe environments, focusing on the integration and support of modern observability tools. It examines the functionalities and benefits of leading solutions such as Prometheus with Grafana, Dynatrace, Datadog, AppDynamics, IBM Instana, and Checkmk, providing insights into how organizations can enhance their mainframe monitoring capabilities. The article emphasizes the significance of a multi-tool observability strategy, highlighting its advantages in flexibility, redundancy, and comprehensive coverage. Key benefits of effective observability, including proactive issue detection, downtime reduction, improved capacity planning, and enhanced security monitoring, are thoroughly discussed. The article also outlines best practices for implementing observability in mainframes, covering unified dashboards, standardized metrics, automation, training, and continuous improvement. By analyzing these tools and strategies, the research offers valuable guidance for organizations seeking to streamline their observability approaches, ultimately enhancing customer experience and operational efficiency in mainframe-dependent environments. This comprehensive examination of mainframe observability techniques provides a roadmap for leveraging legacy systems alongside cutting-edge monitoring solutions, addressing the evolving needs of modern IT infrastructures.

Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model

One of the preconditions for efficient cloud computing services is the continuous availability of services to clients. However, there are various reasons for temporary service unavailability due to routine maintenance, load balancing, cyber-attacks, power management, fault tolerance, emergency incident response, and resource usage. Live Virtual Machine Migration (LVM) is an option to address service unavailability by moving virtual machines between hosts without disrupting running services. Pre-copy memory migration is a common LVM approach used in cloud systems, but it faces challenges due to the high rate of frequently updated memory pages known as dirty pages. Transferring these dirty pages during pre-copy migration prolongs the overall migration time. If there are large numbers of remaining memory pages after a predefined iteration of page transfer, the stop-and-copy phase is initiated, which significantly increases downtime and negatively impacts service availability. To mitigate this issue, we introduce a prediction-based approach that optimizes the migration process by dynamically halting the iteration phase when the predicted downtime falls below a predefined threshold. Our proposed machine learning method was rigorously evaluated through experiments conducted on a dedicated testbed using KVM/QEMU technology, involving different VM sizes and memory-intensive workloads. A comparative analysis against proposed pre-copy methods and default migration approach reveals a remarkable improvement, with an average 64.91% reduction in downtime for different RAM configurations in high-write-intensive workloads, along with an average reduction in total migration time of approximately 85.81%. These findings underscore the practical advantages of our method in reducing service disruptions during live virtual machine migration in cloud systems.

Unlocking the potential of Industry 4.0 in BRICS nations: a systematic literature review and meta-analysis

PurposeThis study is intended to introduce and summarise Industry 4.0 practices in BRICS nations (the abbreviation “BRICS” is made up of the first letters of the member countries: Brazil, Russia, India, China and South Africa) and determine each nation’s current contribution to Industry 4.0 practice implementation based on past literature. As the BRICS countries continue to play an essential role in the global economy, it is significant to understand Industry 4.0, focussing on these emerging economies.Design/methodology/approachTo assess the present research work on Industry 4.0 practices and research studies in BRICS nations, a systematic literature review (SLR) is performed using the articles available on the SCOPUS database. This study is a descriptive analysis based on the frequency and year of publications, the most influential universities, most influential journals and most influential articles. Similarly, this study consists of category analysis based on multi-criteria decision-making (MCDM) methods, research design used, research method utilised, different data analysis techniques and different Industry 4.0 technologies were used to solve different applications in the BRICS nations.FindingsAccording to the analysis of past literature, the primary identified practices are centred on operations productivity, waste management, energy reduction and sustainable processes. It also found that despite the abundance of research on Industry 4.0, the major academic journal publications are restricted to a small number of industries and issues in which the manufacturing and automotive industries are front runners. The categorisation of selected papers based on the year of publication demonstrates that the number of publications has been rising. It is also found that China and India, out of the BRICS countries, have contributed significantly to Industry 4.0-related publications by contributing 61 percent of the total articles identified. Similarly, this study identified that qualitative research design is the most adopted framework for research, and empirical triangulation is the least adopted framework in this field. The categorisation of selected articles facilitates the identification of numerous gaps, such as that 67.14% of the literature research is qualitative.Practical implicationsUnderstanding Industry 4.0 in the BRICS nations helps to identify opportunities for international collaboration and future cooperation possibilities. This study helps to promote collaboration between BRICS countries and other nations, organisations or businesses interested in capitalising on these growing economies' assets and capabilities related to Industry 4.0 technologies. This study helps to provide essential insights into the economic, technological and societal impacts, allowing for effective decision-making and strategic planning for a sustainable and competitive future. So, this contribution links the entire world in terms of the better utilisation of resources, the reduction of downtime, improving product quality, personalised products and the development of human resource capabilities through the application of cutting-edge technologies for nearly half of the world’s population.Originality/valueIn this study, BRICS nations are selected due to their significant impact on the world regarding social, economic and environmental contributions. In the current review, 423 articles published up to August 2022 were selected from the SCOPUS database. The comparison analysis of each BRICS nation in the form of applications of Industry 4.0, the primary area of focus, leading industry working, industry involvement with universities and the area that needs attention are discussed. To the best of our knowledge, this is the most recent SLR and meta-analysis study about Industry 4.0 in BRICS nations, which analysed the past available literature in nine different descriptive and category-wise classifications, considering a total of 423 articles. Based on this SLR, this study makes some important recommendations and future directions that will help achieve social, economic and environmental sustainability in BRICS nations.

ADAPTIVE PROCESS MANAGEMENT USING DEEP LEARNING ON A PROGRAMMABLE LOGIC CONTROLLER (PLC)

In the era of Industry 4.0, the integration of advanced digital technologies into manufacturing processes has become paramount for enhancing operational efficiency and adaptability. This study introduces a groundbreaking approach to adaptive process management through the integration of deep learning algorithms within Programmable Logic Controllers (PLCs), thus addressing the limitations of traditional PLCs in dynamically adjusting to new operational conditions without manual intervention. By leveraging the inherent capabilities of deep learning for realtime data analysis and decision-making, this research develops a novel framework that enables PLCs to autonomously learn from process data, adapt control strategies in real-time, and optimize manufacturing operations. The methodology encompasses the design and implementation of deep learning models tailored for PLC environments, the development of a data-driven learning mechanism directly on the PLC, and a comprehensive evaluation of the system’s adaptability, efficiency, and performance in real-world industrial settings. The findings reveal significant improvements in process efficiency, reduction in downtime, and enhanced adaptability to changing operational conditions, demonstrating the potential of combining deep learning with PLC-based systems for fostering intelligent and flexible manufacturing processes. This study not only provides a viable solution to the challenges of static PLC programming but also opens new ways for research and development in smart manufacturing technologies, offering insights into the practical implications of deploying intelligent automation systems in Industry 4.0.

Analyisis Of Boiler Reliability Maintenance System With Availability Ratio At PT. XXX

This study investigates a food manufacturing firm's efforts to enhance operational efficiency by optimizing its maintenance strategy and spare parts management within the context of noodle production. The research focuses on reducing prolonged machine downtimes caused by boiler malfunctions, which negatively impact production targets. A novel approach combining total minimum downtime calculations and a hybrid corrective-preventive maintenance methodology has been proposed. To bolster this initiative, an efficient spare parts procurement scheme was implemented. Results demonstrate a substantial reduction in unplanned downtime following implementation, thereby improving overall productivity and profitability. Implications highlight the potential for widespread application across similar industries seeking to minimize disruptions while maximizing equipment lifetimes. highlight : Reduced downtime through improved boiler maintenance. Hybrid corrective-preventive maintenance for efficient operations. Optimized spare parts procurement for enhanced productivity. Keywords: noodle production, boiler maintenance, downtime reduction, spare parts management, hybrid maintenance.

User Study to Validate the Performance of an Offline Robot Programming Method That Enables Robot-Independent Kinesthetic Instruction through the Use of Augmented Reality and Motion Capturing

The paper presents a novel offline programming (OLP) method based on programming by demonstration (PbD), which has been validated through user study. PbD is a programming method that involves physical interaction with robots, and kinesthetic teaching (KT) is a commonly used online programming method in industry. However, online programming methods consume significant robot resources, limiting the speed advantages of PbD and emphasizing the need for an offline approach. The method presented here, based on KT, uses a virtual representation instead of a physical robot, allowing independent programming regardless of the working environment. It employs haptic input devices to teach a simulated robot in augmented reality and uses automatic path planning. A benchmarking test was conducted to standardize equipment, procedures, and evaluation techniques to compare different PbD approaches. The results indicate a 47% decrease in programming time when compared to traditional KT methods in established industrial systems. Although the accuracy is not yet at the level of industrial systems, users have shown rapid improvement, confirming the learnability of the system. User feedback on the perceived workload and the ease of use was positive. In conclusion, this method has potential for industrial use due to its learnability, reduction in robot downtime, and applicability across different robot sizes and types.

Optimizing Maintenance Efficiency with OEE Method

This study investigates the effectiveness of roving machines in a production environment by employing the Overall Equipment Effectiveness (OEE) methodology. High downtime of the roving machine significantly hampers production processes, leading to reduced machine performance and speed, resulting in suboptimal OEE values. Through OEE calculations, a value of 66.92% was obtained, highlighting considerable room for improvement towards achieving world-class standards. The findings underscore the necessity for targeted enhancements to mitigate machine downtime, thereby enhancing overall production efficiency and competitiveness in the manufacturing sector. Highlights : Efficient utilization of roving machines through OEE optimization is crucial for enhancing production performance. High downtime of roving machines significantly impedes production processes, leading to decreased efficiency and competitiveness. Addressing downtime issues can lead to substantial improvements in overall production efficiency, aligning operations with global standards. Keywords: Roving machine, Overall Equipment Effectiveness (OEE), Production efficiency, Downtime reduction, Manufacturing competitiveness

Enhancing fault tolerance: dual Q-learning with dynamic scheduling

<span>Cloud computing has revolutionized IT delivery by offering scalable on-demand internet services encompassing software, platforms, and infrastructure. However, cloud services face significant performance challenges due to their susceptibility to failures given their vast operational scale. Implementing fault tolerance in dynamic cloud services is a key challenge, with complex configurations and dependencies complicating deployment. This paper introduces an innovative approach that combines double deep Q-learning (DDQL) with a dynamic fault-tolerant real-time scheduling algorithm (DFTRTSA) to enhance fault tolerance in real-time systems. DDQL, an extension of deep Q-learning, optimizes the fault-tolerance decision-making process. The algorithm adjusts scheduling strategies dynamically based on system conditions and errors. The fusion of DDQL and DFTRTSA aims to create a resilient and adaptive fault-tolerant mechanism, ensuring uninterrupted operation while meeting real-time requirements. This adaptive approach efficiently manages resources, meets deadlines, and gracefully handles errors, as demonstrated through experiments. Our DDQL-DFTRTSA method outperforms conventional fault-tolerant mechanisms in defect tolerance, energy efficiency, downtime reduction, and system dependability. It proves to be an ideal solution for real-time systems in dynamic and unpredictable environments.</span>

МЕТОДОЛОГИЯ PIEF ПО ПОВЫШЕНИЮ ЭФФЕКТИВНОСТИ БИЗНЕС-ПРОЦЕССОВ И ИХ АВТОМАТИЗАЦИИ В МЕЖДУНАРОДНОЙ ОПТОВОЙ ТОРГОВЛЕ

The article is devoted to the study of modern approaches to increasing the efficiency of business processes and their automation in a company. As an example, the Precise Instructions For Employee To Follow methodology, compiled in the practical activities of the author of this study, is given - “precise instructions that an employee must follow,” or PIEF. The views of modern authors on solving problems of increasing the efficiency of business processes and their automation are systematized. The necessity of organizing labor processes (labor) in a company is substantiated as a primary factor in increasing the efficiency of business processes. The significant role of precise, simple and accessible instructions in the quality organization of personnel work is highlighted. The potential benefits are analyzed, including: reduction of losses and downtime due to clear regulation of actions; accessibility of the activity and its phased nature; maintaining the creative functions of employees; focus on a specific goal. The effectiveness of applying the author's methodology in the tasks of the international wholesale trade business is emphasized, since PIEF allows you to implement with high efficiency the entire range of typical tasks that arise in the activities of a wholesale trade enterprise.

Modernisation of the Ponsse Ergo SMRM articulated joint at Mobile Service LLC

The review of feller-delimber-buncher (FDB) “Ponsse Ergo” (element of sorting (Scandinavian) technology of timber harvesting) in natural-production conditions of the enterprise “Mobile Service” LLC (Syktyvkar, Komi Republic) is carried out in the article and modernisation of the hinge joint by installation of expander pins of new design is proposed. The design of the articulated joint of the pivoting system of FDB “Ponsse Ergo” half-frame is considered, on the basis of which the information about the advantages of the expander pin over the cylindrical axial pin is obtained. The design of the expander pin of a new type is presented on the basis of the made and given calculation for the strength of the axial pin of the pivot joint attachment. The hinge joint of the half-frame pivoting system is presented, the axial expander pin of a new design is proposed, the course of dismantling of the cylindrical axial pin of the hinge joint of the known design is presented, followed by the sequence of installation of the expander pin of the new proposed design. Strength calculation of the hydraulic cylinder mounting pin, strength calculation of the axial pivot pin, strength calculation of the connecting bolts are given. The possibility of scaling the solution in the conditions of the Russian Federation is estimated. Advantages of the expander pin over the cylindrical axial pin are given. The results of comparison of cylindrical pin and expander pin of new design are given. As a result of research, the increase of productivity of FDB “Ponsse Ergo” at the expense of increase of inter-service interval and reduction of downtime at the expense of reduction of time required for service maintenance of the unit (preparatory works, main works) is made. Economic indicators of the proposed solution are given.

Optimizing the Simulation of Conveyor Systems through Digital Shadow Integration to Increase Assembly Efficiency

In today's highly competitive industrial environment, continuous improvement of efficiency and optimization of processes is crucial. This paper presents an approach to the optimization of conveyor systems that uses the concept of a digital shadow. A digital shadow, as an exact digital replica of a physical conveyor system, enables detailed simulation and analysis of real operational data, providing a basis for in-depth analysis and identification of areas for improvement. The aim of this approach is not only to improve the understanding of the dynamics and performance of existing conveyor systems, but also to increase the overall efficiency through predictive simulations and optimization algorithms. In this work, we demonstrate how the integration of a digital shadow into the simulation process can contribute to a better reaction to changes in the production environment, to the reduction of downtime and to the optimization of production flows. Our methodology combines data collection / analysis, and enables the creation of accurate and flexible models of conveyor systems. These models are then used in simulations that help identify optimal settings for different production scenarios and predict potential problems before they occur. The results of applying our approach on a test laboratory line show a significant improvement in efficiency and a reduction in operating costs. This study provides important insights and practical guidelines for engineers and production managers focused on the use of digital shadow to increase the efficiency of conveyor systems. It also contributes to the development of intelligent production technologies in the era of Industry 4.0.

Development and Evaluation of an Intelligent Control System for Sustainable and Efficient Energy Management

This paper presents a comprehensive study on the integration of Intelligent Control Systems in the global industrial sector, focusing on enhancing energy management through the synergy of Supervisory Control and Data Acquisition (SCADA), Machine Learning (ML), and Digital Twin technologies. We elaborate on a novel ICS architecture designed to optimize energy consumption, reduce operational costs, and minimize environmental impacts. Our system leverages SCADA for real-time monitoring and control, ML algorithms for predictive analytics and optimization, and Digital Twin technology for advanced simulation and operational efficiency. The implementation of the system in a mid-scale industrial facility demonstrated significant improvements: a 15% reduction in energy consumption, an 18% decrease in peak energy demand, a 30% reduction in CO2 emissions, and a 15% reduction in operational downtime, with predictive accuracy standing at 90%. These results underline the potential of integrating advanced digital technologies in industrial energy management, offering a scalable model for sustainable and efficient industrial practices. Future work will explore broader applications and the incorporation of emerging technologies to further enhance the system's capabilities and applicability in diverse industrial settings.

Program for the implementation of strategies in downtime and impediments to failures in long drilling developed at TECNM Campus Fresnillo

In the present project of the Higher Technological Institute of Fresnillo, in the area of ​​mining engineering, a theoretical-practical research project is developed that focuses on the loss of time and processes in long drilling. In operations carried out with this long drilling procedure, production costs increase and profits are reduced. Training programs will provide better dexterity to operators, making their practical tasks easier and safer. This will guarantee growth and profits at a level. personal and business in the mining field, with the reduction of accidents, downtime and bad practices with the correct training of operators.

Internet of things for smart manufacturing based on advanced encryption standard (AES) algorithm with chaotic system

Smart manufacturing using the Internet of Things (IoT) ensures uninterrupted and human intervention-less automation in industries for precision outcomes. As the smart manufacturing encloses chaotic systems the point of security is always demandable due to external threats. For mitigating the authorization issues in chaotic systems, a Smart Reviving Authorization Model using Advanced Encryption Standard (SRAM-AES) is designed in this article. This model is selective for chaotic systems for reviving their conventional operation cycles and preventing failures. A machine/controller’s performance is monitored for its point of instability through differential access. The malicious access and its cause for controller unstableness are verified using IoT elements (remotely) and deep recurrent learning algorithms. Such identified instances are recovered by providing alternate controller recommendations from the IoT platform. In the recurrent learning process, the unstable to stable point possibilities are verified; the passing controllers are equipped with AES mitigating the previous authorizations. For a stable-functioning controller, the AES deficiency in authorization is verified in its completion cycles for consecutive production instances. Thus this model stands reliable for preventing unauthorized access, controller downtime reduction, and production failures.