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Journal articles on the topic 'Predictive reliability'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Mohamed Abdul Kadar Mohamed Jabarullah. "PredictNet: AI-enabled predictive maintenance system for telecommunications infrastructure reliability." World Journal of Advanced Research and Reviews 15, no. 3 (2022): 631–39. https://doi.org/10.30574/wjarr.2022.15.3.0954.

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This paper introduces PredictNet, a novel AI-enabled predictive maintenance system designed specifically for telecommunications infrastructure. The research addresses the critical challenge of maintaining reliability in increasingly complex telecom networks while reducing operational costs. Using machine learning algorithms and real-time sensor data, PredictNet demonstrates superior performance in predicting equipment failures before they occur. The system was implemented and tested on a mid-sized telecommunications network over a 12-month period, achieving 92.7% prediction accuracy with a mea

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Нікул, С., А. Дерев'янчук, О. Кравчук, Д. Максимчук та Ю. Сініло. "ВПРОВАДЖЕННЯ КОНЦЕПЦІЇ «PREDICTIVE MAINTENANCE» ДЛЯ ЗБІЛЬШЕННЯ НАДІЙНОСТІ ТА ПЕРЕДБАЧУВАНОСТІ РЕМОНТНИХ РОБІТ". Collection of scientific works of Odesa Military Academy, № 20 (14 грудня 2023): 51–55. http://dx.doi.org/10.37129/2313-7509.2023.20.51-55.

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This research article discusses the importance of implementing the Predictive Maintenance concept in the context of rocket and artillery weapons to improve the reliability and predictability of repair work. The article discusses key aspects of this concept, including its basic principles and methods, including the use of data analytics and artificial intelligence. The authors analyze the benefits of Predictive Maintenance in the military context, pointing to reduced repair costs, increased availability of military equipment, and extended equipment life. In addition, the article examines the ch

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Yang, Dezhen, Yidan Cui, Quan Xia, et al. "A Digital Twin-Driven Life Prediction Method of Lithium-Ion Batteries Based on Adaptive Model Evolution." Materials 15, no. 9 (2022): 3331. http://dx.doi.org/10.3390/ma15093331.

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Accurate life prediction and reliability evaluation of lithium-ion batteries are of great significance for predictive maintenance. In the whole life cycle of a battery, the accurate description of the dynamic and stochastic characteristics of life has always been a key problem. In this paper, the concept of the digital twin is introduced, and a digital twin for reliability based on remaining useful cycle life prediction is proposed for lithium-ion batteries. The capacity degradation model, stochastic degradation model, life prediction, and reliability evaluation model are established to descri

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Thota, Praveen Kumar. "Predictive Observability for Scalable Cloud Reliability Engineering." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–7. https://doi.org/10.55041/ijsrem28136.

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Because cloud computing environments support nearly all digital services, they need to be very dependable to maintain smooth delivery. Old-fashioned monitors only detect issues after something fails, which often causes services to be interrupted and downtime to be experienced. This framework is presented to help cloud reliability engineering by making future problems predictable. The way we do this is by reviewing active data, using special models and working with advanced techniques to find out about system problems before end users notice them. It uses flexible designs built for large cloud

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5

McCarthy, C. A., and Haijun Liu. "Predictive analysis ranks reliability improvements." IEEE Computer Applications in Power 12, no. 4 (1999): 35–40. http://dx.doi.org/10.1109/67.795136.

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Coolen, F. P. A., P. Coolen-Schrijner, and K. J. Yan. "Nonparametric predictive inference in reliability." Reliability Engineering & System Safety 78, no. 2 (2002): 185–93. http://dx.doi.org/10.1016/s0951-8320(02)00162-x.

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Grover, Arman, Debajyoti Roy Burman, Priyansh Kapaida, and Neelamani Samal. "Stock Market Price Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 591–95. http://dx.doi.org/10.22214/ijraset.2023.57184.

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Abstract: Investing in the stock market can be a convoluted and refined method of conducting business. Stock prediction is an extremely difficult and complex endeavor since stock values can fluctuate abruptly owing to a variety of reasons, making the stock market incredibly unpredictable.This paper explores predictive models for the stock market, aiming to forecast stock prices using machine learning algorithms. By analyzing historical market data and employing various predictive techniques, thestudy aims to enhance accuracy in predicting future stock movements. this paper contributes understa

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Holland, Christopher T., Shannon Tse, Cyrus P. Bateni, Dillon Chen, and Cassandra A. Lee. "MRI-Based Prediction of Meniscal Tear Repairability Demonstrates Limited Accuracy and Reliability." Journal of Clinical Medicine 14, no. 12 (2025): 4160. https://doi.org/10.3390/jcm14124160.

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Background: While magnetic resonance imaging (MRI) is commonly used to identify meniscal tears, intraoperative assessment typically dictates repairability. This study evaluated whether a simplified MRI-based scoring system could reliably predict meniscal repair versus meniscectomy. Methods: Patients who underwent meniscectomy or meniscal repair between 2010 and 2018 were retrospectively identified. Preoperative MRIs were independently reviewed in a blinded fashion by two radiologists and one orthopedic sports surgeon. Reviewers scored images based on four arthroscopic criteria for tear repaira

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Yang, Zhao Jun, Yin Kai Wang, Fei Chen, et al. "Prediction of Reliability Model for CNC Machine Tool Based on Exponential Smoothing Model." Advanced Materials Research 548 (July 2012): 495–99. http://dx.doi.org/10.4028/www.scientific.net/amr.548.495.

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In order to manage equipment maintenance work and reduce enterprise cost, a new prediction method of reliability parameters is proposed based on failure time in this paper. The reliability model was built based on failure time, and the reliability parameters were obtained by the empirical modeling method. Then parameters from historical data were used as predictive model parameters, which applied the exponential smoothing methods to establish predictive models based on historical data. Finally, prediction model of reliability was built by the predicted parameters used the above method. With fa

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Chandorkar, A., and A. Kharbanda. "Divergent Ensemble Networks : Improving Predictive Reliability and Computational Efficiency." International Journal of Artificial Intelligence & Applications 16, no. 1 (2025): 21–31. https://doi.org/10.5121/ijaia.2025.16102.

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The effectiveness of ensemble learning in improving prediction accuracy and estimating uncertainty is wellestablished. However, conventional ensemble methods often grapple with high computational demands and redundant parameters due to independent network training. This study introduces the Divergent Ensemble Network (DEN), a novel framework designed to optimize computational efficiency while maintaining prediction diversity. DEN achieves superior predictive reliability with reduced parameter overhead by leveraging shared representation learning and independent branching. Our results demonstra

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AIRCC. "Divergent Ensemble Networks : Improving Predictive Reliability and Computational Efficiency." International Journal of Artificial Intelligence & Applications (IJAIA) 16, no. 1 (2025): 21–31. https://doi.org/10.5121/ijaia.2025.16102.

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The effectiveness of ensemble learning in improving prediction accuracy and estimating uncertainty is wellestablished. However, conventional ensemble methods often grapple with high computational demands and redundant parameters due to independent network training. This study introduces the Divergent Ensemble Network (DEN), a novel framework designed to optimize computational efficiency while maintaining prediction diversity. DEN achieves superior predictive reliability with reduced parameter overhead by leveraging shared representation learning and independent branching. Our results demonstra

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Nordin, Noratikah, Zurinahni Zainol, Mohd Halim Mohd Noor, and Chan Lai Fong. "A comparative study of machine learning techniques for suicide attempts predictive model." Health Informatics Journal 27, no. 1 (2021): 146045822198939. http://dx.doi.org/10.1177/1460458221989395.

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Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depress

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Okoye, Martin Onyeka, Junyou Yang, Zhenjiang Lei, et al. "Predictive Reliability Assessment of Generation System." Energies 13, no. 17 (2020): 4350. http://dx.doi.org/10.3390/en13174350.

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Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test syste

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Tang, Zhongjun, and Lang Ni. "An Interval Reliability Demand Prediction Method Combined with XGBoost and D-S Evidence Theory in Film Preparation Period." Journal of Physics: Conference Series 2025, no. 1 (2021): 012022. http://dx.doi.org/10.1088/1742-6596/2025/1/012022.

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Abstract The lack of historical sales data and word-of-mouth information in the film preparation period, the few available variables and the uncertainty in the prediction process lead to the difficulty in predicting the total box office demand of films. To solve this problem, this paper constructed and verified the prediction method of interval reliability demand in the film preparation period, which combined XGBoost algorithm and D-S evidence theory. Firstly, the total box office interval was effectively divided according to the sample data of the training set, and XGBoost was used to complet

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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

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1, N. Bensaid Amrani, L. Saintis 2, Sarsri 3D., and M. Barreau 4. "EVALUATING THE PREDICTED RELIABILITY OF MECHATRONIC SYSTEMS: STATE OF THE ART." Mechanical Engineering: An International Journal (MEIJ) 03, no. 2 (2023): 12. https://doi.org/10.5281/zenodo.7801706.

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Reliability analysis of mechatronic systems is one of the most young field and dynamic branches of research. It is addressed whenever we want reliable, available, and safe systems. The studies of reliability must be conducted earlier during the design phase, in order to reduce costs and the number of prototypes required in the validation of the system. The process of reliability is then deployed throughout the full cycle of development; this process is broken down into three major phases: the predictive reliability, the experimental reliability and operational reliability. The main objective o

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Yastrebov, A. P. "APPLICATION OF PREDICTIVE CONTROL TO IMPROVE THE QUALITY OF MANUFACTURING PROCESSES." Kontrol'. Diagnostika, no. 309 (March 2024): 58–62. http://dx.doi.org/10.14489/td.2024.03.pp.058-062.

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A progressive method of condition -based maintenance of production processes is considered. It is shown that an effective means for its implementation is the use of predictive control. Proposals have been formulated for the use of a reliability-cost criterion that characterizes the effectiveness of maintenance with predictive performance, quantitative relationships have been derived that make it possible to evaluate the reduction in the number of gradual failures when using maintenance with predictive control, as well as establishing the relationship between the effectiveness of maintenance an

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Tang, Youmin, Richard Kleeman, and Andrew M. Moore. "Reliability of ENSO Dynamical Predictions." Journal of the Atmospheric Sciences 62, no. 6 (2005): 1770–91. http://dx.doi.org/10.1175/jas3445.1.

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Abstract In this study, ensemble predictions were constructed using two realistic ENSO prediction models and stochastic optimals. By applying a recently developed theoretical framework, the authors have explored several important issues relating to ENSO predictability including reliability measures of ENSO dynamical predictions and the dominant precursors that control reliability. It was found that prediction utility (R), defined by relative entropy, is a useful measure for the reliability of ENSO dynamical predictions, such that the larger the value of R, the more reliable the prediction. The

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Wu, Jianjun, Yuxue Hu, Zhongqiang Huang, Junsong Li, Xiang Li, and Ying Sha. "Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks." Applied Sciences 13, no. 22 (2023): 12437. http://dx.doi.org/10.3390/app132212437.

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Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that inclu

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Kaladevi R. "Health Insurance Recommendation System using Optimized Grid Search and Regression Models." Journal of Information Systems Engineering and Management 10, no. 2 (2025): 432–43. https://doi.org/10.52783/jisem.v10i2.2147.

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Introduction: Health insurance schemes help cover medical expenses by distributing financial risk among many individuals. With various insurance options available, choosing the right provider and predicting costs can be challenging. Predictive modeling and machine learning techniques play a important role in analyzing past data, identifying patterns in customer behavior, and supporting informed decision-making for new insurance plans. Objectives: The main aim of this research is to assist individuals in selecting appropriate medical insurance providers and estimating associated costs using pre

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SivaMani, Medam, Pinisetty Sushmanth, Matli Mokshagni, and Dr Sampath A. "Predictive analysis of pharmaceutical equipment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40164.

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he pharmaceutical industry demands high standards of equipment reliability to ensure product quality and operational efficiency. This study explores a predictive maintenance framework that integrates machine learning and real-time video analysis to monitor equipment health and prevent failures. The system comprises three main functionalities: training a machine learning model to predict the Remaining Useful Life (RUL) of equipment based on historical sensor data, manual input for RUL prediction, and real-time video monitoring to detect equipment malfunctions. A RandomForestRegressor is employe

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Josef Baumgartner, Alexandra Schneider, Ulugbek Zhenis, Franz Jager, and Josef Winkler. "Mastering Neural Network Prediction for Enhanced System Reliability." Fusion of Multidisciplinary Research, An International Journal 3, no. 1 (2022): 261–74. https://doi.org/10.63995/rvzf7165.

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Mastering neural network prediction is crucial for enhancing system reliability across various fields, from healthcare to autonomous driving. Neural networks, with their ability to learn and generalize from vast datasets, offer unparalleled predictive capabilities. By accurately forecasting system behaviors and potential failures, neural networks can significantly improve reliability and efficiency. For instance, in predictive maintenance, neural networks can analyze sensor data to predict equipment failures before they occur, reducing downtime and maintenance costs. Similarly, in healthcare,

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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (2024): 45–77. http://dx.doi.org/10.69610/j.ases.20240515.

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<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of s

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Farid Agayev, Stanislav Agamatov, Farid Agayev, Stanislav Agamatov. "ADVANCED MONITORING IN WATER COOLING SYSTEMS USING LSTM NETWORKS AND EXTERNAL FACTOR INTEGRATION." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 145, no. 05 (2024): 44–53. https://doi.org/10.36962/pahtei149052024-44.

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This paper presents an advanced approach to monitoring and predicting the performance of water cooling systems in industrial settings. Traditional methods often relied on fixed setpoints and overlooked the impact of external factors like wind speed, treating these variations as errors. By leveraging Long Short-Term Memory (LSTM) networks, this study integrates these external influences into the predictive model, transforming what was previously seen as noise into actionable data. The result is a more accurate prediction of potential system failures and an improved understanding of how various

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Francisco, Alexandre Santos, Marcos Vinícius Cardoso Lacerda, and Luis Alberto Duncan Rangel. "Assessment of the Effect of Predictive Maintenance on the System Reliability." VETOR - Revista de Ciências Exatas e Engenharias 34, no. 1 (2024): 94–102. http://dx.doi.org/10.14295/vetor.v34i1.17320.

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In this work, we assess the effect of predictive maintenance on the reliability of repairable systems. In reparable systems, the reliability assessment considering corrective and preventive maintenance is well defined, but not considering predictive one. The effect of predictive maintenance is taken into account through a finite probability that the system is found in degraded condition. If the predictive maintenance evidences the degradation, then the repair is carried out. All the repairs are considered perfects, which means that the system is restored to an as-good-as-new condition each tim

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Nneka Adaobi Ochuba, Favour Oluwadamilare Usman, Enyinaya Stefano Okafor, Olatunji Akinrinola, and Olukunle Oladipupo Amoo. "PREDICTIVE ANALYTICS IN THE MAINTENANCE AND RELIABILITY OF SATELLITE TELECOMMUNICATIONS INFRASTRUCTURE: A CONCEPTUAL REVIEW OF STRATEGIES AND TECHNOLOGICAL ADVANCEMENTS." Engineering Science & Technology Journal 5, no. 3 (2024): 704–15. http://dx.doi.org/10.51594/estj.v5i3.866.

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Predictive analytics is transforming the maintenance and reliability of satellite telecommunications infrastructure, offering proactive solutions to prevent downtime and enhance operational efficiency. This conceptual review explores key strategies and technological advancements driving the adoption of predictive analytics in this field. The integration of IoT devices and sensors enables real-time monitoring, providing valuable data on equipment performance and environmental conditions. Advanced algorithms, such as AI and ML, analyze this data to predict equipment failures and optimize mainten

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Bozzo, Luciana. "La misura dell'attendibilitŕ dei modelli di previsione e l'unitŕ del sapere." FUTURIBILI, no. 1 (May 2009): 98–107. http://dx.doi.org/10.3280/fu2008-001006.

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- The reliability of predictive models is assured by the ability to establish a unity of knowledge, or rather of many branches of knowledge. This is the idea that leads the author to reflect on the prediction derived first of all from the "science café", defined as "a talking shop for scholars from a range of disciplines", who represent many branches of knowledge which are in fact a complete whole - "knowledge". The background for the predictive model discussed here is territorial planning, which encompasses an instrumental-explanatory component, a predictive component and an ideal. The constr

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Hernández-Murúa, José, Ena Romero-Pérez, Jorge Guajardo-Cruztitla, et al. "Intra-Session Reliability and Predictive Value of Maximum Voluntary Isometric Contraction for Estimating One-Repetition Maximum in Older Women: A Randomised Split-Sample Study." Journal of Functional Morphology and Kinesiology 10, no. 2 (2025): 160. https://doi.org/10.3390/jfmk10020160.

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Background: Ageing is associated with a progressive decline in muscle strength, particularly in the lower limbs, which compromises functional independence. While both maximum voluntary isometric contraction (MVIC) and one-repetition maximum (1RM) are widely employed to assess muscle strength, the intra-session reliability and predictive capacity of MVIC for estimating 1RM in older women remain insufficiently explored. Objectives: This study aims to evaluate the intra-session reliability of MVIC in knee extensors, analyse its correlation with 1RM, and develop a predictive model for estimating 1

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Pagadala, Srivyshnavi, Sony Bathala, and B. Uma. "An Efficient Predictive Paradigm for Software Reliability." Asian Journal of Computer Science and Technology 8, S3 (2019): 114–16. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2051.

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Software Estimation gives solution for complex problems in the software industry which gives estimates for cost and schedule. Software Estimation provides a comprehensive set of tips and heuristics that Software Developers, Technical Leads, and Project Managers can apply to create more accurate estimates. It presents key estimation strategies and addresses particular estimation challenges. In the planning of a software development project, a major challenge faced by project managers is to predict the defects and effort. The Software defect plays critical role in software product development. T

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Clarotti, C. A., and F. Spizzichino. "The Bayes predictive approach in reliability theory." IEEE Transactions on Reliability 38, no. 3 (1989): 379–82. http://dx.doi.org/10.1109/24.44186.

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Salazar, Jean C., Philippe Weber, Fatiha Nejjari, Ramon Sarrate, and Didier Theilliol. "System reliability aware Model Predictive Control framework." Reliability Engineering & System Safety 167 (November 2017): 663–72. http://dx.doi.org/10.1016/j.ress.2017.04.012.

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Tran, Anthony Tri, Q. P. Ha, and Robert Hunjet. "Reliability enhancement with dependable model predictive control." ISA Transactions 106 (November 2020): 152–70. http://dx.doi.org/10.1016/j.isatra.2020.06.027.

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Fedukhin, O. V., and A. A. Mukha. "A conceptual framework for a comprehensive industrial equipment reliability management system using predictive analytics." Mathematical machines and systems 2 (2025): 67–75. https://doi.org/10.34121/1028-9763-2025-2-67-75.

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An analysis of existing solutions was conducted to support the unification of a newly developed industrial equipment reliability management system. Based on a review of the advantages and disadvantages of current predictive analytics platforms, conclusions regarding the future devel-opment prospects of such systems were drawn. The article presents a general concept of a com-prehensive reliability management system for industrial equipment. The proposed approach is grounded in the application of predictive analytics, machine learning, probabilistic-physics deg-radation models, and IoT component

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Yaghoobi, Tahere, and Man-Fai Leung. "Modeling Software Reliability with Learning and Fatigue." Mathematics 11, no. 16 (2023): 3491. http://dx.doi.org/10.3390/math11163491.

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Software reliability growth models (SRGMs) based on the non-homogeneous Poisson process have played a significant role in predicting the number of remaining errors in software, enhancing software reliability. Software errors are commonly attributed to the mental errors of software developers, which necessitate timely detection and resolution. However, it has been observed that the human error-making mechanism is influenced by factors such as learning and fatigue. In this paper, we address the issue of integrating the fatigue factor of software testers into the learning process during debugging

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Oliver, W., J. Nicholson, K. Bell, et al. "ULTRASOUND ASSESSMENT OF HUMERAL SHAFT FRACTURE HEALING: A PROOF OF CONCEPT STUDY." Orthopaedic Proceedings 105-B, SUPP_8 (2023): 112. http://dx.doi.org/10.1302/1358-992x.2023.8.112.

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The primary aim was to assess the reliability of ultrasound in the assessment of humeral shaft fracture healing. The secondary aim was to estimate the accuracy of ultrasound assessment in predicting humeral shaft nonunion.Twelve patients (mean age 54yrs [20–81], 58% [n=7/12] female) with a non-operatively managed humeral diaphyseal fracture were prospectively recruited and underwent ultrasound scanning at six and 12wks post-injury. Scans were reviewed by seven blinded observers to evaluate the presence of sonographic callus. Intra- and inter-observer reliability were determined using the weigh

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Gomez-Nunez, Marta, Javier Pinilla-Ibarz, Tao Dao, et al. "Reliability of Computer Algorithm-Based Binding Predictions for the Identification of Leukemia Antigens." Blood 106, no. 11 (2005): 4483. http://dx.doi.org/10.1182/blood.v106.11.4483.4483.

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Abstract Major Histocompatibility Complex class I (MHC-I) molecules present antigenic peptides to T cells on the cell surface as a prerequisite for stimulating cytotoxic T cell response. Thus, the ability to reliably identify the peptides that can bind to MHC molecules is of practical importance for rapid vaccine development. Several computer-based prediction methods have been applied to study the interaction of MHC class I/peptide binding. Here we have compared three of the most commonly used predictive algorithms BIMAS, SYFPEITHI and Rankpep with actual binding of HLA-A*0201 peptides in vitr

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Lin, Yadi, and Wendi Lin. "The Impact of Predictability and Fault Tolerance on Reliability in Microelectronic Device Design and Manufacturing." International Journal of Engineering and Technology 16, no. 1 (2024): 57–60. http://dx.doi.org/10.7763/ijet.2024.v16.1255.

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This article thoroughly examines the crucial role of predictive analytics and fault tolerance mechanisms in enhancing the reliability of microelectronic devices throughout their design and manufacturing processes. Emphasizing the importance of implementing these measures across the entire lifecycle, including design, manufacturing, and application phases, the study adopts a comprehensive approach. The methodology integrates predictive analytics tools and fault tolerance mechanisms, proactively identifying and mitigating potential issues early on. Results demonstrate a significant reduction in

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Chatterjee, Pushpalika. "Proactive Infrastructure Reliability: AI-Powered Predictive Maintenance for Financial Ecosystem Resilience." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 7, no. 01 (2024): 291–303. https://doi.org/10.60087/jaigs.v7i01.358.

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Financial institutions rely on complex, high-availability infrastructure such as payment gateways, ATM networks, trading servers, and blockchain nodes. Unexpected downtime can lead to severe financial losses, reputational damage, customer dissatisfaction, and regulatory penalties. Traditional reactive maintenance models are insufficient for today's high-speed financial environments where uptime and reliability are critical. This paper proposes a predictive maintenance framework using machine learning (ML), deep learning (DL), and time-series analysis to anticipate and prevent infrastructure fa

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Wu, Hongyi, Jinwen Jin, and Zhiwei Li. "NGBoost algorithm-based prediction of mechanical properties of a hot-rolled strip and its interpretability research with ANOVA values." AIMS Mathematics 9, no. 11 (2024): 33000–33022. http://dx.doi.org/10.3934/math.20241578.

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<p>Hot-rolled strip steel is an essential material extensively used in various industrial fields, with its mechanical properties being critical to product quality and engineering design. This article presents a method for predicting the mechanical properties of hot-rolled strip steel using the NGBoost (natural gradient boosting) algorithm. The study focused on predicting tensile strength, yield strength, and elongation of hot-rolled strip steel and compared the predictive results with those obtained from the gradient boosting algorithm, Lasso regression, and decision tree algorithms. The

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Ekunke Onyeka Virginia, Okiemute Richards Obada, Bioluwatife Oluwaferanmi Oke, et al. "Leveraging machine learning and data analytics for equipment reliability in oil and gas using predictive maintenance." World Journal of Advanced Research and Reviews 25, no. 1 (2025): 2212–18. https://doi.org/10.30574/wjarr.2025.25.1.0295.

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The oil and gas industry operates under very extreme conditions, posing a huge challenge when it comes to equipment reliability. Predictive maintenance; which is now possible through machine learning and data analytics, has transformed the way one looks at equipment management by making real-time failure prediction possible, reducing unplanned downtime, and optimizing maintenance schedules. The review of the technological advances in predictive maintenance methodology focuses on supervised and unsupervised machine learning, deep learning models, and integration with IoT-big data analytics. The

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Mourtzis, Dimitris, Sofia Tsoubou, and John Angelopoulos. "Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance." Electronics 12, no. 9 (2023): 1999. http://dx.doi.org/10.3390/electronics12091999.

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Robotic systems have become a standard tool in modern manufacturing due to their unique characteristics, such as repeatability, precision, and speed, among others. One of the main challenges of robotic manipulators is the low degree of reliability. Low reliability increases the probability of disruption in manufacturing processes, minimizing in this way the productivity and by extension the profit of the company. To address the abovementioned challenges, this research work proposes a robotic cell reliability optimization method based on digital twin and predictive maintenance. Concretely, the

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Tito Ayyalasomayajula, Madan Mohan, and Sailaja Ayyalasomayajula. "Improving Machine Reliability with Recurrent Neural Networks." International Journal for Research Publication and Seminar 11, no. 4 (2020): 253–79. http://dx.doi.org/10.36676/jrps.v11.i4.1500.

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This study explores the application of recurrent neural networks (RNNs) to enhance machine reliability in industrial settings, specifically in predictive maintenance systems. Predictive maintenance uses previous sensor data to identify abnormalities and forecast machine breakdowns before they occur, lowering downtime and maintenance costs. RNNs are ideal with their unique capacity to handle sequential input while capturing temporal relationships. RNN-based models may reliably foresee machine breakdowns and detect early malfunction indicators, allowing for appropriate interventions. The paper i

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Montesinos-López, Osval Antonio, José Crossa, Paolo Vitale, et al. "GBLUP Outperforms Quantile Mapping and Outlier Detection for Enhanced Genomic Prediction." International Journal of Molecular Sciences 26, no. 8 (2025): 3620. https://doi.org/10.3390/ijms26083620.

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Genomic selection (GS) accelerates plant breeding by predicting complex traits using genomic data. This study compares genomic best linear unbiased prediction (GBLUP), quantile mapping (QM)—an adjustment to GBLUP predictions—and four outlier detection methods. Using 14 real datasets, predictive accuracy was evaluated with Pearson’s correlation (COR) and normalized root mean square error (NRMSE). GBLUP consistently outperformed all other methods, achieving an average COR of 0.65 and an NRMSE reduction of up to 10% compared to alternative approaches. The proportion of detected outliers was low (

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Dumitrescu, Diana, Nicolae Bobitan, Adriana Florina Popa, Daniela Nicoleta Sahlian, and Cosmina Adela Stanila. "Signaling Financial Distress Through Z-Scores and Corporate Governance Compliance Interplay: A Random Forest Approach." Electronics 14, no. 11 (2025): 2151. https://doi.org/10.3390/electronics14112151.

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This paper investigates the effectiveness of machine learning algorithms in enhancing the accuracy and reliability of predicting financial distress. The dataset includes Altman Z-Scores and Corporate Governance Compliance (CGC) indicators calculated for manufacturing firms listed on the Bucharest Stock Exchange (BSE) from 2016 to 2022. Leveraging Signaling Theory, the study analyzes financial and governance data for 60 non-financial firms, comprising 420 firm-year observations. Financial distress is classified into three categories: no distress, moderate distress, and severe distress. The stud

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Aven, T., and A. Hjorteland. "A Predictive Bayesian Approach to Multistate Reliability Analysis." International Journal of Reliability, Quality and Safety Engineering 10, no. 03 (2003): 221–34. http://dx.doi.org/10.1142/s0218539303001123.

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In this paper we discuss how to implement a Bayesian thinking for multistate reliability analysis. The Bayesian paradigm comprises a unified and consistent framework for analysing and expressing reliability, but in our view the standard Bayesian procedures gives too much emphasis on probability models and inference on fictional parameters. We believe that there is a need for a rethinking on how to implement the Bayesian approach, and in this paper we present and discuss such a rethinking for multistate reliability analysis. The starting point of the analysis should be observable quantities, ex

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Liu, Zhi-Feng, Ling-Ling Li, Ming-Lang Tseng, Raymond R. Tan, and Kathleen B. Aviso. "Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm." Applied Sciences 9, no. 18 (2019): 3788. http://dx.doi.org/10.3390/app9183788.

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In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to mod

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Li, Meng, Sheng Shen, Vahid Barzegar, Mohammadkazem Sadoughi, Chao Hu, and Simon Laflamme. "Kriging-based reliability analysis considering predictive uncertainty reduction." Structural and Multidisciplinary Optimization 63, no. 6 (2021): 2721–37. http://dx.doi.org/10.1007/s00158-020-02831-w.

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Li, Changyou, Yimin Zhang, and Minqiang Xu. "Reliability-based maintenance optimization under imperfect predictive maintenance." Chinese Journal of Mechanical Engineering 25, no. 1 (2012): 160–65. http://dx.doi.org/10.3901/cjme.2012.01.160.

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Dababneh, Amer, and Ibrahim T. Ozbolat. "Predictive reliability and lifetime methodologies for circuit boards." Journal of Manufacturing Systems 37 (October 2015): 141–48. http://dx.doi.org/10.1016/j.jmsy.2015.08.001.

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Aboalkhair, Ahmad M., Frank P. A. Coolen, and Iain M. MacPhee. "Nonparametric predictive reliability of series of voting systems." European Journal of Operational Research 226, no. 1 (2013): 77–84. http://dx.doi.org/10.1016/j.ejor.2012.11.001.

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