Zeitschriftenartikel: „Data-driven maintenance“

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Rios, Pablo. "Data-Driven Maintenance." Manufacturing Management 2023, no. 1-2 (2023): 32–33. http://dx.doi.org/10.12968/s2514-9768(23)90381-9.

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Afful-Dadzie, Anthony, and Theodore T. Allen. "Data-Driven Cyber-Vulnerability Maintenance Policies." Journal of Quality Technology 46, no. 3 (2014): 234–50. http://dx.doi.org/10.1080/00224065.2014.11917967.

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3

Ostrowski, João, and József Menyhárt. "Enhancing maintenance with a data-driven approach." International Review of Applied Sciences and Engineering 10, no. 2 (2019): 135–40. http://dx.doi.org/10.1556/1848.2019.0016.

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Constant stream of data has been generated and stored as more devices are being connected to the internet and supported with cloud technologies. The price drop of such applications along with industry 4.0 trending, triggered an explosive growth and demand for many IT modern solutions. From an industrial point of view, sensorization practices are spreading through factories and warehouses where software is constantly adapting to provide actionable insights in a data-driven configuration. The fourth industrial revolution is empowering the manufacturers with solutions for cost reduction, which translates in competitive advantage. The sector of maintenance operations is leading in engineering innovation, from reactive to planned preventive techniques the next step in history of proactive approaches is Predictive Analytics Maintenance.

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Ma, Zhiliang, Yuan Ren, Xinglei Xiang, and Ziga Turk. "Data-driven decision-making for equipment maintenance." Automation in Construction 112 (April 2020): 103103. http://dx.doi.org/10.1016/j.autcon.2020.103103.

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Wadzuk, Bridget, Bridget Gile, Virginia Smith, Ali Ebrahimian, Micah Strauss, and Robert Traver. "Moving Toward Dynamic and Data-Driven GSI Maintenance." Journal of Sustainable Water in the Built Environment 7, no. 4 (2021): 02521003. http://dx.doi.org/10.1061/jswbay.0000958.

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Lopes Gerum, Pedro Cesar, Ayca Altay, and Melike Baykal-Gürsoy. "Data-driven predictive maintenance scheduling policies for railways." Transportation Research Part C: Emerging Technologies 107 (October 2019): 137–54. http://dx.doi.org/10.1016/j.trc.2019.07.020.

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Coronel, Eduardo, Benjamín Barán, and Pedro Gardel. "A Survey on Data Mining for Data-Driven Industrial Assets Maintenance." Technologies 13, no. 2 (2025): 67. https://doi.org/10.3390/technologies13020067.

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This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. Additionally, the survey proposes four level classes of asset complexity and studies five asset types, including mechanical, electromechanical, electrical, electronic, and computing assets. The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks.

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Wolfartsberger, Josef, Jan Zenisek, and Norbert Wild. "Data-Driven Maintenance: Combining Predictive Maintenance and Mixed Reality-supported Remote Assistance." Procedia Manufacturing 45 (2020): 307–12. http://dx.doi.org/10.1016/j.promfg.2020.04.022.

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Devarajan, Vinodkumar. "Advancing Data Center Reliability Through AI-Driven Predictive Maintenance." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 102–14. https://doi.org/10.37745/ejcsit.2013/vol13n14102114.

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The evolution of data center maintenance has undergone a transformative shift from traditional reactive and scheduled maintenance to AI-driven predictive maintenance strategies. The integration of artificial intelligence and machine learning technologies enables precise failure prediction, optimizes resource allocation, and enhances operational reliability. Advanced sensor networks and sophisticated analytics pipelines process vast amounts of operational data, while machine learning models, including neural networks, support vector machines, and decision trees, provide accurate predictions of component failures. The implementation framework encompasses system integration, data management, model development, and operational integration, leading to substantial improvements in maintenance efficiency, cost reduction, and equipment longevity. The convergence of human expertise with AI capabilities marks a significant advancement in predictive maintenance, revolutionizing how organizations approach data center operations and reliability management.

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Chen, Chuang, Cunsong Wang, Ningyun Lu, Bin Jiang, and Yin Xing. "A data-driven predictive maintenance strategy based on accurate failure prognostics." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (2021): 387–94. http://dx.doi.org/10.17531/ein.2021.2.19.

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Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluating the maintenance cost online that has taken into account of the failure prognostic results of performance degradation. The feasibility and effectiveness of the proposed strategy is confirmed using the NASA data set of aero-engines. Results show that the proposed strategy outperforms the two benchmark maintenance strategies: classical periodic maintenance and emerging dynamic predictive maintenance.

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Neuhold, Johannes, Matthias Landgraf, Stefan Marschnig, and Peter Veit. "Measurement Data-Driven Life-Cycle Management of Railway Track." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (2020): 685–96. http://dx.doi.org/10.1177/0361198120946007.

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Track engineers face increasing cost pressure and budget restrictions in their work today. This leads to growing difficulty in legitimizing crucial maintenance and renewal measures. As a result, infrastructure managers must ensure they invest all available financial resources as sustainably and efficiently as possible. These boundary conditions require an objective tool enabling both a component-specific condition evaluation and preventive maintenance with renewal planning. The present research introduces such a tool for railway tracks based on innovative track data analyses. This tool includes time-series analyses for predicting future quality behavior. Consequently, the technical necessity of maintenance actions can be derived for every specific track section. In addition, these technical evaluations are combined with economic and operational considerations to plan reasonable maintenance lengths for different track components in the next few years. In a further step, business evaluations by means of annuity monitoring are executed to determine whether ongoing track maintenance or complete track renewal is the most economical solution. This methodology also allows calculating the economic damage caused by neglecting the ideal point in time for reinvestment. On the basis of this economic damage, it is possible to rank projects by priority in the case of insufficient budgets and to ensure that all available resources are invested in the most reasonable manner possible. Furthermore, such analyses clearly show that when a specific degradation level of railway track is reached track renewal is more economic in relation to life-cycle costs than ongoing maintenance.

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Udo, Wisdom, and Yar Muhammad. "Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data." IEEE Access 9 (2021): 162370–88. http://dx.doi.org/10.1109/access.2021.3132684.

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Attia, Hussien Gomaa. "RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems." International Journal of Emerging Science and Engineering (IJESE) 13, no. 5 (2025): 32–43. https://doi.org/10.35940/ijese.E2595.13050425.

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<strong>Abstract: </strong>Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maintenance paradigm, transforming reactive and preventive approaches into autonomous, data-driven ecosystems that enhance operational efficiency and resilience. The proposed framework synergizes RCM principles with Lean Six Sigma’s DMAIC (Define-Measure-Analyze-Improve-Control) methodology, providing a structured, data-driven approach to failure mode classification, risk-based maintenance prioritization, and real-time performance optimization. By leveraging IIoTenabled condition monitoring, Digital Twin simulations, and machine learning-driven predictive analytics, RCM 4.0 enables real-time anomaly detection, intelligent diagnostics, and adaptive maintenance strategies. This shift eliminates inefficiencies, minimizes downtime, optimizes asset performance, and enhances cost-effective maintenance planning. This research establishes RCM 4.0 as a transformative approach to industrial maintenance, integrating cyber-physical intelligence to drive operational resilience, sustainability, and cost efficiency. Future research will explore 5G-enabled industrial communication, autonomous robotic maintenance, blockchain-secured predictive maintenance, and edge AI-powered diagnostics, further advancing nextgeneration digitalized maintenance ecosystems' scalability, cybersecurity, and self-learning capabilities.

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Liao, W. Z., and Y. Wang. "Dynamic Predictive Maintenance Model Based on Data-Driven Machinery Prognostics Approach." Applied Mechanics and Materials 143-144 (December 2011): 901–6. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.901.

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As an increasing number of manufacturers realize the importance of adopting new maintenance technologies to enable systems to achieve near-zero downtime, machinery prognostics which enables this paradigm shift from traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. Machine's condition and degradation estimated by machinery prognostics approach can be used to support predictive maintenance policy. This paper develops a novel data-driven machine prognostics approach to assess machine's health condition and predict machine degradation. With this prognostics information, a predictive maintenance model is constructed to decide machine's maintenance threshold and predictive maintenance cycles number. Through a case study, this predictive maintenance model is verified, and the computational results show that this proposed model is efficient and practical.

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Valkokari, Pasi, Toni Ahonen, Helena Kortelainen, and Jesse Tervo. "The framework for data-driven maintenance planning and problem solving in maintenance communities." IFAC-PapersOnLine 55, no. 19 (2022): 175–80. http://dx.doi.org/10.1016/j.ifacol.2022.09.203.

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Lee, Juseong, Mihaela Mitici, Henk A. P. Blom, Pierre Bieber, and Floris Freeman. "Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification." Aerospace 10, no. 2 (2023): 186. http://dx.doi.org/10.3390/aerospace10020186.

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The increasing use of on-board sensor monitoring and data-driven algorithms has stimulated the recent shift to data-driven predictive maintenance for aircraft. This paper discusses emerging challenges for data-driven predictive aircraft maintenance. We identify new hazards associated with the introduction of data-driven technologies into aircraft maintenance using a structured brainstorming conducted with a panel of maintenance experts. This brainstorming is facilitated by a prior modeling of the aircraft maintenance process as an agent-based model. As a result, we identify 20 hazards associated with data-driven predictive aircraft maintenance. We validate these hazards in the context of maintenance-related aircraft incidents that occurred between 2008 and 2013. Based on our findings, the main challenges identified for data-driven predictive maintenance are: (i) improving the reliability of the condition monitoring systems and diagnostics/prognostics algorithms, (ii) ensuring timely and accurate communication between the agents, and (iii) building the stakeholders’ trust in the new data-driven technologies.

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Bhowmick, Sourav S., Byron Choi, and Curtis Dyreson. "Data-driven visual graph query interface construction and maintenance." Proceedings of the VLDB Endowment 9, no. 12 (2016): 984–92. http://dx.doi.org/10.14778/2994509.2994517.

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Liu, Yu, Hong-Zhong Huang, and Xiaoling Zhang. "A Data-Driven Approach to Selecting Imperfect Maintenance Models." IEEE Transactions on Reliability 61, no. 1 (2012): 101–12. http://dx.doi.org/10.1109/tr.2011.2170252.

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Sharma, Siddhartha, Yu Cui, Qing He, Reza Mohammadi, and Zhiguo Li. "Data-driven optimization of railway maintenance for track geometry." Transportation Research Part C: Emerging Technologies 90 (May 2018): 34–58. http://dx.doi.org/10.1016/j.trc.2018.02.019.

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Zhang, Zijun, Xiaofei He, and Andrew Kusiak. "Data-driven minimization of pump operating and maintenance cost." Engineering Applications of Artificial Intelligence 40 (April 2015): 37–46. http://dx.doi.org/10.1016/j.engappai.2015.01.003.

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Kabashkin, Igor, and Vitaly Susanin. "Unified Ecosystem for Data Sharing and AI-Driven Predictive Maintenance in Aviation." Computers 13, no. 12 (2024): 318. http://dx.doi.org/10.3390/computers13120318.

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The aviation industry faces considerable challenges in maintenance management due to the complexities of data standardization, data sharing, and predictive maintenance capabilities. This paper introduces a unified ecosystem for data sharing and AI-driven predictive maintenance designed to address these challenges by integrating real-time and historical data from diverse sources, including aircraft sensors, maintenance logs, and operational records. The proposed ecosystem enables predictive analytics and anomaly detection, enhancing decision-making processes for airlines, maintenance, repair, and overhaul providers, and regulatory bodies. Key elements of the ecosystem include a modular design with feedback loops, scalable AI models for predictive maintenance, and robust data-sharing frameworks. This paper outlines the architecture of a unified aviation maintenance ecosystem built around multiple data sources, including aircraft sensors, maintenance logs, flight data, weather data, and manufacturer specifications. By integrating various components and stakeholders, the system achieves its full potential through several key use cases: monitoring aircraft component health, predicting component failures, receiving maintenance alerts, performing preventive maintenance, and generating compliance reports. Each use case is described in detail and supported by illustrative dataflow diagrams. The findings underscore the transformative impact of such an ecosystem on aviation maintenance practices, marking a significant step toward safer, more efficient, and sustainable aviation operations.

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Ketoeva, N. L., M. A. Znamenskaya, and I. O. Borzykh. "Road map for Data-Driven approach implementation in maintenance and wearing management system of electric power equipment in management decision making." Surgut State University Journal 12, no. 4 (2024): 44–60. https://doi.org/10.35266/2949-3455-2024-4-4.

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The aim of the study is to develop a road map for implementing the Data-Driven approach in the maintenance and wearing management system of electric power equipment. The subject of the study is the Data-Driven approach in the maintenance and wearing management system of electric power equipment in management decisions making in an electric power company. The authors used the following materials and methods: dialectical, scientific knowledge and private scientific (analysis, synthesis, comparison, logical and system-structural analysis, formalization, analysis of regulatory documents), modeling. Enterprises in the electric power industry can use the study’s results to implement the Data-Driven approach in their maintenance and repair management systems for electric power equipment using the provided road map. The road map will help enterprises reduce the loss of financial and material resources caused by equipment downtime and breakdowns.

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Bethaz, Paolo, Sara Cavaglion, Sofia Cricelli, et al. "Empowering Commercial Vehicles through Data-Driven Methodologies." Electronics 10, no. 19 (2021): 2381. http://dx.doi.org/10.3390/electronics10192381.

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In the era of “connected vehicles,” i.e., vehicles that generate long data streams during their usage through the telematics on-board device, data-driven methodologies assume a crucial role in creating valuable insights to support the decision-making process effectively. Predictive analytics allows anticipation of vehicle issues and optimized maintenance, reducing the resulting costs. In this paper, we focus on analyzing data collected from heavy trucks during their use, a relevant task for companies due to the high commercial value of the monitored vehicle. The proposed methodology, named TETRAPAC, offers a generalizable approach to estimate vehicle health conditions based on monitored features enriched by innovative key performance indicators. We discussed performance of TETRAPAC in two real-life settings related to trucks. The obtained results in both tasks are promising and able to support the company’s decision-making process in the planning of maintenance interventions.

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Venkatesh Peruthambi, Lahari Pandiri, Pallav Kumar Kaulwar, Hara Krishna Reddy Koppolu, Balaji Adusupalli, and Avinash Pamisetty. "Big Data-Driven Predictive Maintenance for Industrial IoT (IIoT) Systems." Metallurgical and Materials Engineering 31, no. 3 (2025): 21–30. https://doi.org/10.63278/1316.

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Big data-driven predictive maintenance is becoming a fundamental component of IIoT systems to enable failure predication proactively and streamline the scheduling process. This work examines the intersection of machine learning, digital twin technology, and optimization techniques in the context of increasing predictive maintenance efficiency and effectiveness. Four algorithms were evaluated via live IIoT sensor reading inputs: Random Forest, XGBoost, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). The performance outcome indicates that XGBoost achieved the highest in fault detection accuracy at 96.4%, followed by CNN at 94.8%, LSTM at 92.3%, and then Random Forest at 90.1%. A blockchain-based federated learning framework was also utilized to facilitate secure and decentralized predictive maintenance and minimize false alarms by 28% compared to conventional methods. Optimization methods such as Koopman observables and Dynamic Mode Decomposition with Control (DMDc) also enhanced system efficiency, reducing computing cost by 35%. Scalability issues with predictive maintenance in large-scale industries are confirmed as part of this study, as well as edge AI integration and reinforcement learning as probable future trends. These results form the basis of the significance of data-driven predictive maintenance in minimizing downtime, optimizing resource utilization, and facilitating cost-efficient industrial processes.

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Venkat, Kalyan Uppala. "Leveraging AI for Scalable Data Dictionaries: Enhancing Data Management and Governance in Complex Data Environments." Journal of Scientific and Engineering Research 8, no. 6 (2021): 190–96. https://doi.org/10.5281/zenodo.13758620.

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As organizations increasingly rely on data-driven strategies, the need for an adaptable and scalable data dictionary has become paramount. Traditional methods of managing data dictionaries, which involve manual documentation and maintenance, are becoming inadequate in the face of rapidly expanding and complex data environments. This paper explores how Artificial Intelligence (AI) can revolutionize the development and expansion of data dictionaries, making them more dynamic, accurate, and responsive to real-time changes. By automating metadata management, enhancing data quality, and providing predictive analytics for data integration, AI-driven data dictionaries offer a robust solution for maintaining consistent and comprehensive documentation across diverse data pipelines. The paper presents case studies of organizations that have successfully implemented AI-driven data dictionaries, demonstrating the tangible benefits in improving data governance, facilitating real-time decision-making, and promoting data literacy across the enterprise. Ultimately, this paper provides a roadmap for organizations to leverage AI in modernizing their data dictionaries, ensuring they remain a critical tool in navigating the complexities of contemporary data management and governance.

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Corman, Francesco, Sander Kraijema, Milinko Godjevac, and Gabriel Lodewijks. "Optimizing preventive maintenance policy: A data-driven application for a light rail braking system." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 5 (2017): 534–45. http://dx.doi.org/10.1177/1748006x17712662.

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This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.

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Farahani, Saeed, Vinayak Khade, Shouvik Basu, and Srikanth Pilla. "A data-driven predictive maintenance framework for injection molding process." Journal of Manufacturing Processes 80 (August 2022): 887–97. http://dx.doi.org/10.1016/j.jmapro.2022.06.013.

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张, 晓东. "Application of Data-Driven Tire Life Cycle Maintenance Management System." Modern Management 14, no. 08 (2024): 1747–52. http://dx.doi.org/10.12677/mm.2024.148203.

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Liu, Jing, Yong Feng Dong, Yan Li, Si Yuan Lei, and Shu Qun He. "Composite Fault Diagnosis and Intelligent Maintenance Based on Data Driven." Key Engineering Materials 693 (May 2016): 1357–60. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1357.

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For composite fault is difficult to diagnose, the characteristics of the large amount of data. This paper presents a method of The Prediction method of Composite Fault Based on data driven to establish intelligence unit Based on a collection of virtual individuals associated with the virtual failure associated collection and virtual behavior associated collection. Composite fault warning engine modeling is proposed, and give the warning value of composite fault finally. This method is fully assessing the future "dominant state" on the basis of the fully aware of current "hidden state". The impact of factors such as disturbance of hidden failures on composite fault prediction are fully considered, to some extent, the long-span composite failure prediction problem is solved, and the experiments show that the method effectively increases the accuracy of composite fault prediction.

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Tichy, Tomas, Jiri Broz, Jiri Stefan, and Rastislav Pirnik. "Failure analysis and data-driven maintenance of road tunnel equipment." Results in Engineering 18 (June 2023): 101034. http://dx.doi.org/10.1016/j.rineng.2023.101034.

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Akash Vijayrao Chaudhari and Pallavi Ashokrao Charate. "Proactive Data Pipeline Maintenance via Machine Learning-Driven Anomaly Detection." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 1041–53. https://doi.org/10.32628/ijsrst251222663.

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Modern data pipelines are the backbone of data-driven enterprises, feeding analytics and machine learning systems with timely and accurate data. Ensuring these pipelines operate reliably is critical, as failures or data quality issues can propagate downstream and lead to significant business losses. Traditional pipeline maintenance is largely reactive—engineers respond to broken jobs or corrupted data after the fact. In this paper, we propose a proactive maintenance framework that leverages machine learning-driven anomaly detection to continuously monitor data pipelines and address issues before they escalate. The approach integrates real-time anomaly detection on both pipeline operational metrics and data quality indicators to flag deviations from normal behavior. We outline how advanced algorithms (including time-series models, unsupervised outlier detection, and reinforcement learning agents) can identify subtle pipeline issues such as data schema changes, upstream delays, and data drift. The framework further incorporates automated diagnosis and remediation strategies, aiming for self-healing pipelines that reduce downtime. We demonstrate the effectiveness of this approach using synthetic data pipeline experiments, where an anomaly detection model achieves 100% recall in identifying injected pipeline faults with minimal false alarms. We also survey relevant literature and industry solutions, including recent works by Chaudhari and colleagues on AI-driven ETL and multi-agent anomaly resolution, to situate our contributions. Results from both our experiments and prior studies show that ML-driven monitoring can intercept issues in real-time – enabling maintenance that is not only reactive but truly proactive. The proposed approach can significantly improve pipeline reliability, reduce manual intervention, and ultimately ensure the consistent delivery of high-quality data for critical applications.

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Wang, Hai, Su Xie, Ke Li, and M. Ahmad. "Big Data-Driven Cellular Information Detection and Coverage Identification." Sensors 19, no. 4 (2019): 937. http://dx.doi.org/10.3390/s19040937.

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As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and cellular coverage identification is proposed. With the help of network-related data crowd sensed from the massive number of smartphone users in the live network, the algorithm can estimate more parameters of BSA with higher accuracy than conventional methods. The coverage capability of each cell was also identified in a granularity of small geographical grids. Computational results validate the proposed algorithm with higher performance and detection ability over the existing ones. The new method can be expected to improve the scope, accuracy, and timeliness of BSA, serving for wireless network optimization and maintenance as well as LBS service.

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Venkata Siva Prasad Maddala. "Data-Driven Manufacturing: Leveraging Analytics for Operational Excellence." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 884–93. https://doi.org/10.32628/cseit25111291.

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This comprehensive article explores the transformative impact of data-driven analytics on modern manufacturing operations, emphasizing its role in enhancing operational excellence and innovation. The article examines five key areas: production intelligence, supply chain optimization, sustainable manufacturing analytics, data-driven innovation, and implementation frameworks. Through detailed analysis, the article demonstrates how advanced analytics capabilities are revolutionizing manufacturing processes by enabling predictive maintenance, optimizing supply chains, promoting sustainable practices, and accelerating innovation cycles. The article reveals how manufacturing organizations leverage real-time monitoring, machine learning algorithms, and artificial intelligence to achieve improved operational efficiency, reduced maintenance costs, enhanced product quality, and increased market responsiveness. The article also addresses organizational readiness and technical infrastructure requirements for successfully implementing data-driven manufacturing solutions. By examining technical and organizational dimensions, this study provides valuable insights into how manufacturers can effectively transition to data-driven operations while maintaining competitive advantage in an increasingly dynamic market environment.

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Naveen Reddy Singi Reddy. "AI-driven data integration: Transforming enterprise data pipelines through machine learning." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 729–38. https://doi.org/10.30574/wjaets.2025.15.1.0245.

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This article examines the transformative impact of artificial intelligence on enterprise data integration processes, with a particular focus on how machine learning algorithms are revolutionizing traditional approaches to data mapping, transformation, and maintenance. The article explores the evolution from manual integration methodologies to intelligent, self-adjusting data pipelines that automatically respond to changing data patterns and requirements. The article identifies key machine learning techniques enabling automated schema matching, intelligent anomaly detection, and advanced data cleaning capabilities that significantly reduce human intervention while improving accuracy and throughput. By analyzing several enterprise case studies, the article demonstrates how AI-driven integration systems substantially reduce implementation timeframes and maintenance overhead compared to traditional ETL processes. The article also addresses emerging architectural frameworks for adaptive data pipelines and provides a forward-looking perspective on self-healing integration systems. The article suggests that organizations implementing AI-powered data integration solutions gain substantial competitive advantages through increased operational efficiency, improved data quality, and enhanced ability to scale data operations in response to growing business demands.

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M, Monisha. "Predictive Maintenance of Aircraft Components Based on Sensor Data-Driven Approach: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1338–45. http://dx.doi.org/10.22214/ijraset.2023.53843.

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Abstract: Predictive maintenance has gained significant attention in the aviation industry as a proactive strategy for enhancing aircraft safety, reducing downtime, and optimizing maintenance costs. Ensuring the reliability and efficiency of aircraft components has consistently been a significant focus in the aviation industry. Accurately anticipating possible malfunctions can significantly improve the dependability of these components and system fault detection and prediction in the aircraft industry play a critical role in preventing failures, minimizing maintenance expenses, and maximizing fleet availability. Unforeseen aircraft maintenance can cause flight cancellations or delays when spare parts are not readily available at the location of the failure. This leads to undesired downtime, thereby increasing operational costs for airlines. By employing predictive modelling, airlines can reduce unscheduled maintenance activities, resulting in cost savings and improved fleet availability. Implementing health monitoring and predictive maintenance practices for aircraft can also minimize unplanned groundings by implementing more systematic maintenance intervals, thereby avoiding situations where an aircraft is grounded (known as "aircraft on ground" or AOG) and the subsequent operational disruptions. This survey paper provides a comprehensive review of the state – of – the – art deep learning techniques employed in the field of predictive maintenance for aircraft components

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Reddy Katta, Srikanth. "Leveraging Power BI for Data-Driven Decision-Making in Pharma Maintenance Operations." Journal of Research in Business and Management 13, no. 2 (2025): 61–70. https://doi.org/10.35629/3002-13026170.

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The pharmaceutical industry thus has many pieces of equipment that are very critical and very sensitive to breakdowns, and these are used in almost all the steps of production and are very crucial in ensuring that they meet the set regulatory requirements as well as achieve the best quality in production. Most of the maintenance undertakings are carried out following the ‘break and fix’ or ‘run and correct’ method, which results in poor performance, unanticipated machine failure, and high costs. The use of business intelligence tools such as Power BI is a revolution in the business fraternity as it enhances the ability to monitor data in real time, implement real-life maintenance, and result in the formulation of data-based decisions. General maintenance data from various sources, including mechanical or electrical equipment sensors, ERP systems, or maintenance logs, is integrated into Power BI platforms and provides detailed insights into KPIs that cover operational readability and failure anticipation and schedules for the maintenance department. With the help of Power BI and its sophisticated capabilities for data analysis and artificial intelligence, the pharmaceutical industry can easily change from a periodical maintenance regimen to a prescriptive and predictive one – thus improving machinery performance and reducing the chances of overall production failures. The framework suggested in this research leverages Power BI’s functionality to enhance the management of maintenance through the consolidation of various data sets and storage of the data in a single location. The infrastructure allows the real-time monitoring of equipment health status reporting and improves or even provides decision-making with the use of trend analysis and analytics. By using Power BI, maintenance teams in the pharmaceutical industry are able to determine failure trends and usage frequency of the pieces of equipment. Consequently, they are able to determine where to channel their efforts and minimize costs. Findings shown in the case of this study, therefore, show a considerable improvement in equipment availability or use and costs of maintenance, affirming the benefits that could be obtained from the BI-driven approach. Also, due to the specific focus on the field of pharmaceuticals, the impact of Power BI in compliance reporting and audit purposes of maintenance operations is investigated. Thus, through the implementation of this technology, pharmaceutical organizations will be able to optimize their performance and even gain a competitive advantage of running uninterrupted, high-quality production lines

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Yao, Siya, Qi Kang, Mengchu Zhou, Abdullah Abusorrah, and Yusuf Al-Turki. "Intelligent and Data-Driven Fault Detection of Photovoltaic Plants." Processes 9, no. 10 (2021): 1711. http://dx.doi.org/10.3390/pr9101711.

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Most photovoltaic (PV) plants conduct operation and maintenance (O&M) by periodical inspection and cleaning. Such O&M is costly and inefficient. It fails to detect system faults in time, thus causing heavy loss. To ensure their operations are at an ideal state, this work proposes an unsupervised method for intelligent performance evaluation and data-driven fault detection, which enables engineers to check PV panels in time and implement timely maintenance. It classifies monitoring data into three subsets: ideal period A, transition period S, and downturn period B. Based on A and B datasets, we build two non-continuous regression prediction models, which are based on a tree ensemble algorithm and then modified to fit the non-continuous characteristic of PV data. We compare real-time measured power with both upper and lower reference baselines derived from two predictive models. By calculating their threshold ranges, the proposed method achieves the instantaneous performance monitoring of PV power generation and provides failure identification and O&M suggestions to engineers. It has been assessed on a 6.95 MW PV plant. Its evaluation results indicate that it is able to accurately determine different functioning states and detect both direct and indirect faults in a PV system, thereby achieving intelligent data-driven maintenance.

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Shen, Bo, Jiyang Lin, and Yingqi Jia. "Data-driven urban rail vehicle critical parts maintenance system based on cloud-edge collaboration." Journal of Physics: Conference Series 2649, no. 1 (2023): 012057. http://dx.doi.org/10.1088/1742-6596/2649/1/012057.

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Abstract In view of the limitations of traditional maintenance methods for rail vehicles, this study proposes a data-driven maintenance system for critical parts of rail vehicles based on a cloud-side collaborative framework. Currently, there are several major issues with the maintenance of critical parts of rail vehicles, including long maintenance cycles, difficult troubleshooting, high maintenance costs, low maintenance efficiency, irregularities in data management, and a low level of informatization. To address these problems, a cloud-edge collaboration approach is adopted. The maintenance information of the vehicles is synchronized and exchanged in real-time with the cloud data center. Sensor and Internet of Things (IoT) technologies are used to collect real-time operational and status data of rail vehicles. The data is then analyzed and modelled in the cloud data center. The system achieves fault prediction and remaining useful life prediction for key components of the rail vehicles by applying machine learning algorithms. The experimental results demonstrate that compared to traditional maintenance methods, the system enables more effective troubleshooting and remaining useful life prediction of critical components of the vehicles. It improves maintenance efficiency and safety while also offering practical application value.

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Zhou, Juzhen, and Lihua Wang. "Application of a Nursing Data-Driven Model for Continuous Improvement of PICC Care Quality." Journal of Healthcare Engineering 2022 (March 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/7982261.

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A PICC catheter maintenance network was established and managed to monitor the maintenance of catheters in placed patients throughout the process, providing homogeneous PICC catheter continuity of care for patients. Model-driven thinking is an idea for simulation system development. Model-driven architecture (MDA) is a design methodology that implements model-driven thinking and is widely used in simulation system development. Based on the requirements of nursing, the data-driven model is mainly divided into interface layer and functional service layer; this study adopts MDA technology which can detach the functions of the system from the platform, based on domain knowledge, and the metamodel adopts XSD-based data model to generate the PIM model, which is stored in the model library. The results showed that the number of nurses at maintenance sites increased from 79 to 232, the PICC placement rate for oncology patients increased from 35.0% to 76.0%, the nurse maintenance operation pass rate increased from 53.9% to 88.4%, and the maintenance default rate decreased from 40.0% to 10.9%.

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Antomarioni, Sara, Maurizio Bevilacqua, Domenico Potena, and Claudia Diamantini. "Defining a data-driven maintenance policy: an application to an oil refinery plant." International Journal of Quality & Reliability Management 36, no. 1 (2019): 77–97. http://dx.doi.org/10.1108/ijqrm-01-2018-0012.

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Purpose The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements. Design/methodology/approach The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns. Findings The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested. Originality/value This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems.

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Pampana, Ashish Kumar, JungHo Jeon, Soojin Yoon, Theodore J. Weidner, and Makarand Hastak. "Data-Driven Analysis for Facility Management in Higher Education Institution." Buildings 12, no. 12 (2022): 2094. http://dx.doi.org/10.3390/buildings12122094.

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Planned Preventive Maintenance (PPM) and Unplanned Maintenance (UPM) are the most common types of facility maintenance. This paper analyzes current trends and status of Facility Management (FM) practice at higher education institutions by proposing a systematic data-driven methodology using Natural Language Process (NLP) approaches, statistical analysis, risk-profile analysis, and outlier analysis. This study utilizes a descriptive database entitled “Facility Management Unified Classification Database (FMUCD)” to conduct the systematic data-driven analyses. The 5-year data from 2015 to 2019 was collected from eight universities in North America. A preprocessing step included but was not limited to identifying common data attributes, cleaning noisy data, and removing unnecessary data. The outcomes of this study can facilitate the decision-making process by providing an understanding of various aspects of educational facility management trends and risks. The methodology developed gives decision makers of higher education institutions, including facility managers and institution administrators, effective strategies to establish long-term budgetary goals, which will lead to the enhancement of the asset value of the institutions.

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Carlos Eduardo Rodriguez. "Optimizing business aviation operations through predictive maintenance: A data-driven approach to aircraft lifecycle management." World Journal of Advanced Research and Reviews 23, no. 1 (2024): 3162–72. https://doi.org/10.30574/wjarr.2024.23.1.2146.

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Predictive analytics transforms aircraft lifecycle management by integrating predictive maintenance systems into business aviation. Predictive maintenance analyzes current data alongside machine learning algorithms with IoT sensors to anticipate equipment faults, which helps organizations reduce their expenses and increase their operation reliability. This investigation uses predictive data models to evaluate how predictive maintenance methods minimize unplanned breaks, maximize operational efficiency, and minimize total maintenance expenses. The studied outcomes demonstrate how reducing unexpected maintenance activities generates elevated aircraft readiness rates for operational business needs. These predictive maintenance strategies empower managers to make superior fleet and asset lifetime management decisions. Predictive maintenance is a powerful instrument for modernizing business aviation operations because it delivers cost reductions alongside safety improvements and fewer interruptions from maintenance work, creating smooth operations and raising profits for operators.

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Sala, Roberto, Fabiana Pirola, Giuditta Pezzotta, and Sergio Cavalieri. "Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study." Applied Sciences 12, no. 15 (2022): 7395. http://dx.doi.org/10.3390/app12157395.

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Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it.

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Gomaa, Prof Dr Attia Hussien. "RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems." International Journal of Emerging Science and Engineering 13, no. 5 (2025): 32–43. https://doi.org/10.35940/ijese.e2595.13050425.

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Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maintenance paradigm, transforming reactive and preventive approaches into autonomous, data-driven ecosystems that enhance operational efficiency and resilience. The proposed framework synergizes RCM principles with Lean Six Sigma’s DMAIC (Define-Measure-Analyze-Improve-Control) methodology, providing a structured, data-driven approach to failure mode classification, risk-based maintenance prioritization, and real-time performance optimization. By leveraging IIoT enabled condition monitoring, Digital Twin simulations, and machine learning-driven predictive analytics, RCM 4.0 enables real-time anomaly detection, intelligent diagnostics, and adaptive maintenance strategies. This shift eliminates inefficiencies, minimizes downtime, optimizes asset performance, and enhances cost-effective maintenance planning. This research establishes RCM 4.0 as a transformative approach to industrial maintenance, integrating cyber-physical intelligence to drive operational resilience, sustainability, and cost efficiency. Future research will explore 5G-enabled industrial communication, autonomous robotic maintenance, blockchain-secured predictive maintenance, and edge AI-powered diagnostics, further advancing next generation digitalized maintenance ecosystems' scalability, cybersecurity, and self-learning capabilities.

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Chen, Jinwei, Zhenchao Hu, Jinzhi Lu, Xiaochen Zheng, Huisheng Zhang, and Dimitris Kiritsis. "A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis." Applied Sciences 12, no. 12 (2022): 5961. http://dx.doi.org/10.3390/app12125961.

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Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas path analysis (GPA) method with a fault diagnosis ontology model. Firstly, a physical model-based GPA method is used to extract the fault features from the field data. Secondly, a virtual distance mapping algorithm is developed to map the GPA result to a specific fault feature criteria individual described in the ontology model. Finally, a fault diagnosis ontology model is built to support the automatic reasoning of the maintenance strategy from the mapped fault feature criteria individual. To enhance the ability of selecting a proper maintenance strategy, the ontology model represents more abundant knowledge from several sources, such as fault criteria analysis, physical structure analysis, FMECA (failure mode, effects, and criticality analysis), and the maintenance logic decision tool. The availability of the proposed hybrid driven method is verified by the field fault data from a real GE LM2500 PLUS gas turbine unit. The results indicate that the hybrid driven method is effective in detecting the path fault in advance. Furthermore, diversified fault information, such as fault effects, fault criticality, fault consequence, and fault detectability, could be provided to support selecting a proper maintenance strategy. It is proven that the data-knowledge hybrid driven method can improve the capability of the gas path fault detection, fault analysis, and maintenance strategy selection.

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Madhukar Dharavath. "AI-Driven Predictive Maintenance in Data Infrastructure: A Multi-Modal Framework for Enhanced System Reliability." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 824–34. http://dx.doi.org/10.32628/cseit241061118.

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This article presents a comprehensive framework for implementing artificial intelligence-driven predictive maintenance in modern data infrastructure environments. While traditional maintenance approaches have relied on reactive or scheduled interventions, the proposed framework leverages multiple AI technologies, including machine learning, natural language processing, and reinforcement learning, to create a proactive maintenance ecosystem. The methodology integrates diverse data streams from infrastructure components, including sensor data, system logs, and historical maintenance records, to predict potential failures and optimize maintenance schedules. The approach combines time series analysis for trend identification, natural language processing for unstructured data analysis, and reinforcement learning for dynamic schedule optimization. Implementation across multiple case studies, including cloud service providers and manufacturing environments, demonstrates significant improvements in system reliability, reduction in unplanned downtime, and optimization of maintenance resource allocation. The results indicate that AI-driven predictive maintenance substantially outperforms traditional approaches in both accuracy and cost-effectiveness. This article contributes to the growing field of intelligent infrastructure management and provides practical guidelines for organizations seeking to enhance their data infrastructure reliability through advanced predictive maintenance strategies.

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Vishal Goyal, Kamal Sharma, Amit Jain,. "Enhancing Reliability of Advanced Driver-Assistance Systems through Predictive Maintenance and Data-Driven Insights." Journal of Electrical Systems 20, no. 4s (2024): 508–23. http://dx.doi.org/10.52783/jes.2061.

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The advancement of Advanced Driver Assistance Systems (ADAS) marks a pivotal evolution in automotive technology, aiming to enhance road safety and driving efficiency through a wide array of functionalities like blind spot detection, emergency braking, and adaptive cruise control. This research paper delves into the operational integrity, performance metrics, and maintenance strategies of ADAS components, underpinned by a comprehensive methodology involving data collection, pre-processing, feature engineering, machine learning model development, and rigorous validation processes. Systematic inspection of ADAS components indicates their importance in vehicle safety and reliability. The visibility, distance, speed, and steering angle of front cameras, LiDAR, radar, and ultrasonic sensors are carefully evaluated. Maintenance logs show proactive error code management, boosting efficiency. SVM, Gradient Boosting, and Random Forest machine learning models predicted ADAS component failures during validation and testing. Random Forest scored 90% accuracy, 92% precision, 88% recall, and 90% F1. Gradient Boosting was the most accurate, with 93% accuracy, 94% precision, 91% recall, and 92% F1. SVM predicted ADAS component failures with 88% accuracy, 90% precision, 85% recall, and 87% F1 score. Machine learning helps shift from reactive to proactive maintenance. Modelling sensor signal quality, actuator reaction times, error code frequencies, and maintenance intervals enables predictive maintenance and failure detection. Feature engineering builds predictive models using maintenance logs and operational KPIs. The models predict ADAS component failures, boosting vehicle safety and dependability. Using external data improves predictive maintenance models. The maintenance model's adaptability and forecast accuracy are proved by ADAS operation after traffic, accident, and manufacturer upgrades. Predictive maintenance and machine learning improve ADAS dependability and safety, the study found. Advanced analytics and data-driven insights can reduce automotive system failures, improving safety and reliability.

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Dazok Donald Jambol, Oludayo Olatoye Sofoluwe, Ayemere Ukato, and Obinna Joshua Ochulor. "Transforming equipment management in oil and gas with AI-Driven predictive maintenance." Computer Science & IT Research Journal 5, no. 5 (2024): 1090–112. http://dx.doi.org/10.51594/csitrj.v5i5.1117.

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The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.

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Davari, Narjes, Bruno Veloso, Gustavo de Assis Costa, Pedro Mota Pereira, Rita P. Ribeiro, and João Gama. "A Survey on Data-Driven Predictive Maintenance for the Railway Industry." Sensors 21, no. 17 (2021): 5739. http://dx.doi.org/10.3390/s21175739.

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In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events—anomaly detection in time-series—can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.

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Jiang, Yali, Gang Yang, Haijiang Li, and Tian Zhang. "Knowledge driven approach for smart bridge maintenance using big data mining." Automation in Construction 146 (February 2023): 104673. http://dx.doi.org/10.1016/j.autcon.2022.104673.

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