Готові списки джерел за темами / Predictive maintenance ICTs

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Добірка наукової літератури з теми "Predictive maintenance ICTs"

Автор: Grafiati

Опубліковано: 10 березня 2023

Оновлено: 11 березня 2023

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Статті в журналах з теми "Predictive maintenance ICTs"

Campos, Jaime, Pankaj Sharma, Michele Albano, Luis Lino Ferreira, and Martin Larrañaga. "An Open Source Framework Approach to Support Condition Monitoring and Maintenance." Applied Sciences 10, no. 18 (September 12, 2020): 6360. http://dx.doi.org/10.3390/app10186360.

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This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

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Maiorano, Brigida Anna, Ugo De Giorgi, Davide Ciardiello, Giovanni Schinzari, Antonio Cisternino, Giampaolo Tortora, and Evaristo Maiello. "Immune-Checkpoint Inhibitors in Advanced Bladder Cancer: Seize the Day." Biomedicines 10, no. 2 (February 9, 2022): 411. http://dx.doi.org/10.3390/biomedicines10020411.

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Background: In advanced bladder cancer (BCa), platinum-based chemotherapy represents the first-choice treatment. In the last ten years, immune checkpoint inhibitors (ICIs) have changed the therapeutic landscape of many solid tumors. Our review aims to summarize the main findings regarding the clinical use of ICIs in advanced BCa. Methods: We searched PubMed, Embase, and Cochrane databases, and conference abstracts from international congresses (ASCO, ESMO, ASCO GU) for clinical trials, focusing on ICIs as monotherapy and combinations in metastatic BCa. Results: 18 studies were identified. ICIs targeting PD1 (nivolumab, pembrolizumab), PD-L1 (avelumab, atezolizumab, durvalumab), and CTLA4 (ipilimumab, tremelimumab) were used. Survival outcomes have been improved by second-line ICIs, whereas first-line results are dismal. Avelumab maintenance in patients obtaining disease control with chemotherapy has achieved the highest survival rates. Conclusions: ICIs improve survival after platinum-based chemotherapy. Avelumab maintenance represents a new practice-changing treatment. The combinations of ICIs and other compounds, such as FGFR-inhibitors, antibody-drug conjugates, and anti-angiogenic drugs, represent promising therapeutic approaches. Biomarkers with predictive roles and sequencing strategies are warranted for best patient selection.

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Cho, SangJe, Jong-Ho Shin, Hong-Bae Jun, Ho-Jin Hwang, Chunghun Ha, and Jinsang Hwang. "A Study on Estimating the Next Failure Time of Compressor Equipment in an Offshore Plant." Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/8705796.

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The offshore plant equipment usually has a long life cycle. During its O&M (Operation and Maintenance) phase, since the accidental occurrence of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing critical offshore equipment. Nowadays, due to the emerging ICTs (Information Communication Technologies), it is possible to send health monitoring information to administrator of an offshore plant, which leads to much concern on CBM (Condition-Based Maintenance). This study introduces three approaches for predicting the next failure time of offshore plant equipment (gas compressor) with case studies, which are based on finite state continuous time Markov model, linear regression method, and their hybrid model.

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Hinojosa-Palafox, Eduardo A., Oscar M. Rodríguez-Elías, José A. Hoyo-Montaño, Jesús H. Pacheco-Ramírez, and José M. Nieto-Jalil. "An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions." Sensors 21, no. 13 (June 23, 2021): 4282. http://dx.doi.org/10.3390/s21134282.

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The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

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Lee, Gyeong-Won. "Current advances in the treatment of lung cancer with immune checkpoint inhibitors." Journal of the Korean Medical Association 64, no. 5 (May 10, 2021): 333–41. http://dx.doi.org/10.5124/jkma.2021.64.5.333.

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Lung cancer is the leading cause of cancer-related deaths worldwide despite major advances in platinum-based chemotherapy and targeted therapy based on activating driving mutations. Immune checkpoint inhibitors (ICIs) have revolutionized the treatment paradigms in lung cancers. When used as a second-line or later treatment for non-small cell lung cancer (NSCLC), ICIs improve overall survival and exhibit better safety profiles than the standard chemotherapeutic agent, docetaxel. In front-line treatment, ICI monotherapy is significantly associated with improved clinical outcomes and fewer adverse events than platinum-based chemotherapy in patients with advanced NSCLC, who express programmed death-ligand 1 in at least 50% of all tumor cells. Moreover, ICIs combined with platinumbased chemotherapy have become the standard first-line treatment for patients with metastatic NSCLC without sensitizing mutations in the epidermal growth factor receptor gene or translocation of the anaplastic lymphoma kinase gene, regardless of programmed death-ligand 1 expression. Additionally, maintenance treatment using ICIs has also been demonstrated to improve clinical outcomes in patients with stage III unresectable NSCLC following chemoradiotherapy. Recently, the addition of ICIs to chemotherapy as the first-line treatment for extensive-stage small-cell lung cancer resulted in significantly longer overall survival and progression-free survival compared with chemotherapy alone. Although immune checkpoint inhibitors significantly improved overall survival and showed a durable response in lung cancer compared with platinum-based chemotherapy, we should foster further prospective studies to identify predictive biomarkers to determine those individuals who may benefit more from ICIs. It is also essential to overcome the development of drug resistance in patients treated with ICIs.

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Aslanifar, Lida, Sheida Sodagar, and Maryam Bahrami Hidaji. "Mediation role of cognitive regulation in predicting rumination based on distress tolerance in patients undergoing methadone maintenance therapy." Advances in Cognitive Science 21, no. 2 (September 1, 2019): 41–51. http://dx.doi.org/10.30699/icss.21.2.41.

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Лазитан, Л. Г., П. В. Шоури, and В. Г. Раджеш. "A novel prediction approach for condition-based maintenance of class II machines via optimized neural network." Вычислительные технологии, no. 1 (March 22, 2022): 70–87. http://dx.doi.org/10.25743/ict.2022.27.1.006.

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Техническое обслуживание по состоянию (condition-based maintenance, CBM) использует реальные условия эксплуатации компонентов для принятия решения об их замене и/или обслуживании, таким образом увеличивая срок службы оборудования и сводя к минимуму помехи при обслуживании. Система CBM выявляет существенные изменения и колебания сигналов и переменных на основе информации датчика, чтобы избежать или предотвратить поломку машин. Таким образом, в данной работе разрабатывается новая модель прогнозирования на CBM в машинах класса II, в которой скорость вибрации и среднее время рассматриваются в качестве входных параметров и, соответственно, прогнозируются доступность и надежность машин. Предлагаемая схема состоит из двух основных этапов, таких как (1) выделение признаков и (2) предсказание. Сначала выполняется выделение статистических и статистических признаков более высокого порядка. Найденные признаки передаются классификатору нейронной сети (NN), который предсказывает конечный результат (доступность и надежность машин). Чтобы повысить точность прогноза классификатора, веса NN точно настраиваются с помощью оптимизации Levy Flight Adopted Gray Wolf (LF_GWO). Доказано преимущество представленного подхода по отношению к различным мерам. Condition-based maintenance (CBM) utilizes the real conditions of the components to make a decision when to replace and/or maintain the components, thus maximizing the life span of the machineries, whilst minimizing the count of service interferences. Particularly, CBM system identifies the noteworthy variations and fluctuations of signals and variables on the basis of sensor information so as to avoid or prevent the breakdown in machines. Thereby, this work develops a new prediction model on CBM in class II machines, where vibration velocity and average time are considered as input parameters and accordingly, the availability and reliability are predicted. Here, the proposed scheme consists of 2 chief phases like (1) feature extraction and (2) prediction. At first, feature extraction is performed, wherein statistical and higher-order statistical features are extracted. Subsequent to this, the extracted features are given to the Neural Network (NN) classifier that predicts the final output (availability and reliability of machines). To enhance the prediction accuracy of the classifier, the weights of NN are fine-tuned via Levy Flight Adopted Grey Wolf Optimization (LF-GWO). At last, the supremacy the presented approach is proved with respect to varied measures.

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Kang, Jianxiong, Yanjun Lu, Hongbo Luo, Jie Li, Yutao Hou, and Yongfang Zhang. "Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty." Mechanics & Industry 22 (2021): 29. http://dx.doi.org/10.1051/meca/2021028.

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The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.

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Tsimberidou, Apostolia Maria, Alexandra Drakaki, Danny Khalil, Shivaani Kummar, F. Stephen Hodi, David Yoonsuk Oh, Christopher R. Cabanski, et al. "An exploratory study of nivolumab (nivo) with or without ipilimumab (ipi) according to the percentage of tumoral CD8 cells in advanced metastatic cancer." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 2573. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.2573.

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2573 Background: Immune checkpoint inhibitors (ICIs) have demonstrated durable clinical responses and improved survival in patients (pts) across numerous indications. Despite this progress, the benefit of ICIs is limited to a minority of overall metastatic cancer patients. There is a critical need for biomarkers agnostic of tumor type to inform which pts will benefit from nivo alone versus ipi/nivo combination treatment. Both pre-treatment tumoral CD8 + cells and recruitment of CD8+ T cells in response to ICIs are associated with improved clinical outcomes in patients treated with anti-PD-1 therapy. 1,2,3,4 Here we report the final results of a prospective clinical study in which pts with varying advanced solid tumors were assigned to nivo, with or without ipi, based on the percentage of tumoral CD8 cells at the time of treatment. Methods: We performed a prospective, non-randomized, open-label, multicenter study in which pts with tumoral CD8+ cells ≥ 15% (CD8+ high) received nivo 360mg IV Q3W, followed by nivo maintenance 480mg Q4W. Pts with tumoral CD8+ cells < 15% (CD8+ low) received nivo 360 mg IV Q3W, and ipi at 1 mg/kg IV Q3W for 2 doses and then Q6W for 2 doses, followed by nivo maintenance 480 mg IV Q4W until PD or intolerable toxicity. Primary endpoints were Disease Control Rate (DCR: CR, PR, or SD ≥ 6 months) and CD8 low to high conversion (< 15% to ≥ 15%). Baseline and on-treatment tumor, blood and stool samples were collected for multiomic biomarker analyses. This study was not powered for formal statistical analysis. Up to 200 pts could be enrolled to allow for adaptive exploration of response and CD8 changes. Results: N = 79 pts were enrolled:7 in CD8+ high arm (nivo) and 72 in CD8+ low arm (ipi/nivo). The study enrolled a wide variety of primary solid tumors; the most common were gynecological (n = 15), prostate (12), and head and neck (7). DCR was 14% (1/7; 95% CI 1 - 44) and 24% (17/72; 95% CI 15 - 34) in the CD8 high and CD8 low arms, respectively. Of 39 pts in CD8 low arm with an on-treatment biopsy, 14 (36%; 95% CI 22 - 51) had CD8 conversion; 7/14 pts (50%) who converted had DCR. Immune-related AEs (irAEs) were consistent with known safety profile of both drugs. Conclusions: Ipi/nivo demonstrated clinical responses and increased CD8% in a range of “cold” tumors with low tumoral CD8 cells. There may be an association between increasing CD8% and response. Baseline high CD8% alone does not appear to be sufficient as a pan-cancer predictive biomarker of response to nivo monotherapy. CD8 conversion, response, and irAEs associated with circulating and stool-based biomarkers are under evaluation as composite biomarkers may improve their predictive value. Clinical trial information: 03651271.

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Singh, Dave, John R. Hurst, Fernando J. Martinez, Klaus F. Rabe, Mona Bafadhel, Martin Jenkins, Domingo Salazar, Paul Dorinsky, and Patrick Darken. "Predictive modeling of COPD exacerbation rates using baseline risk factors." Therapeutic Advances in Respiratory Disease 16 (January 2022): 175346662211073. http://dx.doi.org/10.1177/17534666221107314.

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Background: Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual’s rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD. Methods: Exacerbation data from 20,054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNACLE-1, PINNACLE-2, and PINNACLE-4 studies were pooled. Machine learning was used to identify predictors of moderate/severe exacerbation rates. Important factors were selected for generalized linear modeling, further informed by backward variable selection. An independent test set was held back for validation. Results: Prior exacerbations, eosinophil count, forced expiratory volume in 1 s percent predicted, prior maintenance treatments, reliever medication use, sex, COPD Assessment Test score, smoking status, and region were significant predictors of exacerbation risk, with response to inhaled corticosteroids (ICSs) increasing with higher eosinophil counts, more prior exacerbations, or additional prior treatments. Model fit was similar in the training and test set. Prediction metrics were ~10% better in the full model than in a simplified model based only on eosinophil count, prior exacerbations, and ICS use. Conclusion: These models predicting rates of moderate/severe exacerbations can be applied to a broad range of patients with COPD in terms of airway obstruction, eosinophil counts, exacerbation history, symptoms, and treatment history. Understanding the relative and absolute risks related to these factors may be useful for clinicians in evaluating the benefit: risk ratio of various treatment decisions for individual patients. Clinical trials registered with www.clinicaltrials.gov (NCT02465567, NCT02497001, NCT02766608, NCT02727660, NCT01854645, NCT01854658, NCT02343458, NCT03262012, NCT02536508, and NCT01970878)

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Більше джерел

Дисертації з теми "Predictive maintenance ICTs"

Navicelli, Andrea, Mario Tucci, and Filippo De Carlo. "Analisi ed applicazione di modelli diagnostici e prognostici per guasti e prestazioni di componenti di impianti industriali nell’era I4.0." Doctoral thesis, 2021. http://hdl.handle.net/2158/1234822.

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Il ruolo fondamentale che la manutenzione gioca nei costi di esercizio e nella produttività degli impianti industriali ha portato le aziende e i ricercatori a spostare il loro interesse su questo tema. L'ultima frontiera dell'innovazione in campo manutentivo, resa possibile anche dall'avvento della quarta rivoluzione industriale che promuove la sensorizzazione e l’interconnessione di tutti i macchinari di impianto, è la manutenzione predittiva. Essa mira ad ottenere una previsione accurata della vita utile dei componenti degli impianti industriali al fine di ottimizzare la schedulazione degli interventi sul campo. Lo studio parte da una accurata revisione della letteratura scientifica di settore riguardante le tecniche diagnostiche e prognostiche applicate a componenti di impianti industriali, necessaria alla comprensione dei diversi modelli sviluppati in funzione della tipologia di componente e modo di guasto in analisi. Successivamente ho spostato l’attenzione sul concetto di manutenzione 4.0 al fine di mappare tutte le caratteristiche associate al paradigma dell'Industria 4.0 e le loro possibili applicazioni alla manutenzione. Lo studio condotto ha portato poi alla progettazione, sviluppo e validazione delle metodologie necessarie all’applicazione in real-time di modelli diagnostici e prognostici avanzati, sia statistici che machine learning, necessari all’implementazione sul campo di un sistema di manutenzione predittiva. Grazie all’applicazione delle metodologie proposte ad un caso studio è stato possibile non solo validare i modelli proposti ma anche definire l’architettura informatica necessaria alla loro corretta implementazione sul sistema distribuito di controllo (Distributed Control System - DCS) di impianto in funzione della tipologia del componente e del guasto in analisi. I modelli testati e validati hanno mostrato elevate prestazioni diagnostiche soprattutto per quanto riguarda i modelli ML che sfruttano le Support Vector Machine (SVM). In definitiva, questo lavoro di tesi mostra nel dettaglio tutti i passaggi necessari allo sviluppo di un sistema di manutenzione predittiva efficace in impianto: partendo dall’analisi dei modi di guasto e dalla sensorizzazione dei componenti, passando poi allo sviluppo dei modelli diagnostici e prognostici real-time fino alla costruzione dell’interfaccia di visualizzazione dei risultati delle analisi svolte, analizzando anche l’architettura informatica necessaria al suo corretto funzionamento. The fundamental role that maintenance plays in the operating costs and productivity of industrial plants has led companies and researchers to shift their interest in this issue. The last frontier of innovation in the maintenance field, made possible also by the advent of the fourth industrial revolution which promotes the sensorisation and interconnection of all plant machinery, is predictive maintenance. It aims to obtain an accurate forecast of the useful life of the industrial plants’ components in order to optimise the scheduling of interventions in the field. The study starts from an accurate review of the scientific literature concerning the diagnostic and prognostic techniques applied to industrial plant components, necessary to understand the different models developed according to the type of component and failure mode under analysis. Subsequently I shifted the focus to the maintenance 4.0 concept in order to map all the characteristics associated with the Industry 4.0 paradigm and their possible applications to maintenance operations. The study then led to the design, development and validation of the methodologies necessary for the real-time application of advanced diagnostic and prognostic models, both statistical and machine learning, necessary for the field implementation of a predictive maintenance system. Thanks to the application of the proposed methodologies to a case study, it was possible not only to validate the proposed models but also to define the IT architecture necessary for their correct implementation on the plant's Distributed Control System (DCS) according to the type of component and the fault under analysis. The tested and validated models showed high diagnostic performance, especially regarding the Support Vector Machine (SVM) Machine Learning models. Ultimately, this thesis shows in detail all the steps necessary for the development of an effective predictive maintenance system in the plant: starting from the analysis of failure modes and component sensorisation, then moving on to the development of real-time diagnostic and prognostic models up to the build-up of the interface for visualising the results of the analyses carried out, also analysing the IT architecture necessary for its correct operation.

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Частини книг з теми "Predictive maintenance ICTs"

Kavitha, B. C., and R. Vallikannu. "IoT Assisted Predictive Maintenance and Worker Safety: An Initiative." In Information and Communication Technology for Competitive Strategies (ICTCS 2020), 719–27. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0739-4_68.

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Berkhout, Volker, Carsten Frey, Philipp Hertweck, David Nestle, and Manuel Wickert. "Energy Data Space." In Designing Data Spaces, 329–41. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93975-5_20.

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AbstractThe energy sector is in a dynamic transition from centralized systems with large fossil power plants to a decentralized system with a high number of renewable energy assets and a rapidly increasing number of additional flexible loads from storage solutions, e-mobility, or power-to-heat applications.To operate the system reliably, demand and supply have to be matched at all times very closely. Thus, the sector is very data and communication intensive and requires advanced ICT solutions to automate processes and deal with the enormous complexity.The Energy Data Space can enable the digitalization of the energy transition by providing an architecture to make data available in order to increase the efficiency in asset and system operation.Data provision and market communication within the system operations of electricity grids is a key use case due to its central role in the sector. Next, the integration of data from the smart meter rollout could as well be built on Data Space technology. Further use cases include predictive maintenance and the energy supply of buildings.Initial research projects have demonstrated the feasibility of basic use cases. On the European level, the Platoon project will provide seven pilot applications by 2024.

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John, Jobish, Amrita Ghosal, Tiziana Margaria, and Dirk Pesch. "DSLs and Middleware Platforms in a Model-Driven Development Approach for Secure Predictive Maintenance Systems in Smart Factories." In Lecture Notes in Computer Science, 146–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89159-6_10.

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AbstractIn many industries, traditional automation systems (operating technology) such as PLCs are being replaced with modern, networked ICT-based systems as part of a drive towards the Industrial Internet of Things (IIoT). The intention behind this is to use more cost-effective, open platforms that also integrate better with an organisation’s information technology (IT) systems. In order to deal with heterogeneity in these systems, middleware platforms such as EdgeX Foundry, IoTivity, FI-WARE for Internet of Things (IoT) systems are under development that provide integration and try to overcome interoperability issues between devices of different standards. In this paper, we consider the EdgeX Foundry IIoT middleware platform as a transformation engine between field devices and enterprise applications. We also consider security as a critical element in this and discuss how to prevent or mitigate the possibility of several security risks. Here we address secure data access control by introducing a declarative policy layer implementable using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Finally, we tackle the interoperability challenge at the application layer by connecting EdgeX with DIME, a model-driven/low-code application development platform that provides methods and techniques for systematic integration based on layered Domain-Specific Languages (DSL). Here, EdgeX services are accessed through a Native DSL, and the application logic is designed in the DIME Language DSL, lifting middleware development/configuration to a DSL abstraction level. Through the use of DSLs, this approach covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable. We validate our approach with an example IIoT use case in smart manufacturing.

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Castañeda, William Alberto Cruz, and Renato Garcia Ojeda. "Applications in Predictive Analytics." In Advances in Data Mining and Database Management, 42–62. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5063-3.ch003.

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According to the World Health Organization, Healthcare Technology (HT) is defined as the application of techniques and knowledge in the way of devices, medicaments, vaccines, procedures, and systems in order to develop solutions for healthcare problems and enhance the quality of life. Clinical Engineering has emerged as an interdisciplinary profession in the areas of medical equipment and technology management. With the correct support of Information and Communication Technologies (ICTs), these and others questions may be resolved through the ubiquitous environments and services that allow the acquisition, processing, diagnostic, transmission, and information-sharing in real time. Ubiquitous healthcare is a new paradigm that allows developing models and tools that improve the processes through monitoring, evaluation, prediction, and decision-making of the medical equipment condition. This chapter presents an ubiquitous management methodology for predictive maintenance with support of ICT and predictive analysis techniques that enhance decision-making in medical equipment.

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Gurbeta Pokvic, Lejla, Lemana Spahic, and Almir Badnjevic. "Implementation of Industry 4.0 in Transformation of Medical Device Maintenance Systems." In Advances in Business Information Systems and Analytics, 512–32. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2725-2.ch023.

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Due to the development of information communication technologies (ICT), the number of medical devices (MDs) with telemetric possibilities is rising, so the concept of homecare is gaining importance. Also, new generation medical devices are equipped with artificial intelligence that is able to perform real-time analysis of measurement result and provide diagnosis prediction. This is the Industry 4.0 happening now. However, there is still traditional approach in management of medical devices. As medical devices have been sophisticated, management systems should improve so they can encompass all the important aspects regarding safety of patients and quality of care. This chapter presents how the technology of Industry 4.0 can be used to improve medical device maintenance systems by application of artificial intelligence (AI). Clinical engineering and health technology management departments benefit from such systems in terms of increase of safety and quality of patient diagnosis and treatments, and cost optimization in medical device management.

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Тези доповідей конференцій з теми "Predictive maintenance ICTs"

Zhu, Meiling, Chen Liu, Shouli Zhang, and Yanbo Han. "(WIP) Correlation-Driven Service Event Routing for Predictive Industrial Maintenance." In 2018 IEEE International Conference on Web Services (ICWS). IEEE, 2018. http://dx.doi.org/10.1109/icws.2018.00044.

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Hermawan, Ade Pitra, Dong-Seong Kim, and Jae-Min Lee. "Predictive Maintenance of Aircraft Engine using Deep Learning Technique." In 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2020. http://dx.doi.org/10.1109/ictc49870.2020.9289466.

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De las Morenas, Javier, and Francisco Moya-Fernandez. "Predictive Maintenance in Electrical Machines: An Edge Computing Approach." In 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE, 2022. http://dx.doi.org/10.1109/icps51978.2022.9816921.

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Yang, Fang-Ning, and Huei-Yung Lin. "Development of A Predictive Maintenance Platform for Cyber-Physical Systems." In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS). IEEE, 2019. http://dx.doi.org/10.1109/icphys.2019.8780144.

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Putra, Hafid Galih Pratama, Suhono Harso Supangkat, I. Gusti Bagus Baskara Nugraha, Fadhil Hidayat, and PT Kereta. "Designing Machine Learning Model for Predictive Maintenance of Railway Vehicle." In 2021 International Conference on ICT for Smart Society (ICISS). IEEE, 2021. http://dx.doi.org/10.1109/iciss53185.2021.9533201.

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Pratama, Zastra Alfarezi, and Fadhil Hidayat. "Predictive Maintenance on Railway Turnout System: A Systematic Literature Review." In 2022 International Conference on ICT for Smart Society (ICISS). IEEE, 2022. http://dx.doi.org/10.1109/iciss55894.2022.9915046.

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Xayyasith, Sisavath, Anucha Promwungkwa, and Kanchit Ngamsanroaj. "Application of Machine Learning for Predictive Maintenance Cooling System in Nam Ngum-1 Hydropower Plant." In 2018 16th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2018. http://dx.doi.org/10.1109/ictke.2018.8612435.

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Priatmadja, Rizally, Ahmad Dhou'ut Taufiq, and Muhammad Nurdin. "Corona Measurement Test in Correlation with Predictive Maintenance on Extra High Voltage Transmission Line." In 2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP). IEEE, 2020. http://dx.doi.org/10.1109/ict-pep50916.2020.9249882.

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Amornsrivarakul, Pornnapat, and Phatham Loahavilai. "Predictive Bearing Maintenance Based on Transfer Learning with Preprocessing and Machine Learning Models Analysis." In 2022 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP). IEEE, 2022. http://dx.doi.org/10.1109/ict-pep57242.2022.9988804.

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Ekene, S. Mbonu, C. Ndinechi Micheal, C. Okafor Kennedy, and Ezekwe Chinwe Genevra. "Self diagnostic system for Predictive Maintenance of traffic light control system." In 2013 IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON). IEEE, 2013. http://dx.doi.org/10.1109/nigercon.2013.6715669.

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