Relevant bibliographies by topics / Data-driven maintenance

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data-driven maintenance'

Author: Grafiati
Published: 25 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Data-driven maintenance"

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

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Ostrowski, João, and József Menyhárt. "Enhancing maintenance with a data-driven approach." International Review of Applied Sciences and Engineering 10, no. 2 (December 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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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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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 (November 2021): 02521003. http://dx.doi.org/10.1061/jswbay.0000958.

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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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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 (March 25, 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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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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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 (August 2016): 984–92. http://dx.doi.org/10.14778/2994509.2994517.

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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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Dissertations / Theses on the topic "Data-driven maintenance"

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Sedghi, Mahdieh. "Data-driven predictive maintenance planning and scheduling." Licentiate thesis, Luleå tekniska universitet, Industriell Ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80828.

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The railway track network is one of the major modes of transportation and among a country’s most valuable infrastructure assets. Maintenance and renewal of railway infrastructure have a vital role in safety performance, the quality of the ride, train punctuality, and the life cycle cost of assets. Due to the large proportion of maintenance costs, increasing the efficiency of maintenance through optimised planning can result in high amounts of cost-saving. Moreover, from a safety perspective, late maintenance intervention can result in defective track and rollingstock components, which in severe cases, can cause severe accidents such as derailments. An effective maintenance management system is required to ensure the availability of the infrastructure system and meet the increasing capacity demand. The recent rapid technological revolution and increasing deployment of sensors and connected devices created new possibilities to increase the maintenance strategy effectiveness in the railway network. The purpose of this thesis is to expand the knowledge and methods for planning and scheduling of railway infrastructure maintenance. The research vision is to find quantitative approaches for integrated tactical planning and operational scheduling of predictive condition-based maintenance which can be put to practical use and improve the efficiency of the railway system. First, a thorough literature review study is performed to identify improvement policies for maintenance planning and scheduling in the literature and also to analyse the current approaches in optimising the maintenance planning and scheduling problem. Second, a novel data-driven multi-level decision-making framework to improve the efficiency of maintenance planning and scheduling is developed. The proposed framework aims to support the selection of track segments for maintenance by providing a practical degradation prediction model based on available condition measurement data. The framework considers the uncertainty of future predictions using the probability of surpassing a maintenance limit instead of using the predicted value. Moreover, an extensive total maintenance cost formulation is developed to include both direct and indirect preventive and corrective costs to observe the effect of using cost optimisation and grouping algorithms at the operational scheduling level. The performance of the proposed framework is evaluated through a case study based on data from a track section of the iron ore line between Boden and Luleå. The results indicate that the proposed approach can lead to cost savings in both optimal and grouping plans. This framework may be a useful decision support tool in the automated planning and scheduling of maintenance based on track geometry measurements.
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Roychowdhury, Sayak. "Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091.

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Okwori, Emmanuel. "Data-driven approaches for proactive maintenance planning of sewer blockage management." Licentiate thesis, Luleå tekniska universitet, Arkitektur och vatten, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83891.

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Blockages have been reported to account for a significant proportion of reported failures in sewer networks. The malfunctioning of the sewer network from blockages and the subsequent disruption to other public services and flooding may constitute a risk to the environment and human health. Due to the complex nature of underground sewer networks, a reactive approach to blockage maintenance is typically employed. However, although proactive maintenance strategies have been developed, both approaches could be expensive and highlight the need to address the problem with analytics-based methods. Although blockage triggering mechanisms may be known, sewer blockages often appear at random. Thus, it is necessary to improve the understanding of the influential mechanisms involved in forming blockages in sewer networks to support its maintenance and guarantee adequate performance levels. The overall aim of this thesis was to contribute with new knowledge, approaches and methods that can support improved proactive maintenance planning of blockages in sewer networks. Various methods to achieve the aim have been investigated in relation to asset management planning levels. At the strategic level, blockages and associated performance indicators were employed in conjunction with Poisson and partial least squares regression to assess the performance of sewer networks, including gaining additional insights. At the tactical and operational levels, a procedure was developed. The procedure combines network k-function, geographically weighted regression and random forest ensembles. The network k-function analysis explains the significance of the spatial variation of blockages. The Geographically weighted Poisson regression (GWPR) investigates the degree of influence of explanatory factors on increased blockage propensity in differentiated segments of the sewer networks. Thirdly, the random forest ensembles was used to predict pipes with blockage recurrence likelihood. A proposed conceptual framework was applied at all asset management levels to assess the state of data-driven integrated asset management (IAM), based on data quality assessments, interoperability evaluations between IAM tools, and data collection and informational benefits analysis.  Results from demonstrating the methods with data from the Swedish waters statistical database and three Swedish municipal sewer networks, namely A, B and C, are presented. Blockage related performance indicators showed that the average blockage rate in medium sized networks was 2-3 times the rate in other sewer networks in Sweden. Furthermore, sewer maintenance strategies were suspected to be ineffective, and increased proactive strategies may improve maintenance efficiency. The procedure in networks A, B and C indicated that the clustering of recurrent blockages maybe linked to an increased need for flushing-related maintenance in sewer pipe networks. The degree of influence between investigated factors and increased blockage propensity indicated that these relationships were not global (not the same in all locations) within and between the sewer networks for networks A, B and C. These non-stationary relationships were observed to occur in various forms, i.e. adequate self-cleaning velocity showed positive and negative correlations in different locations. The networks with relatively more substantial spatial clusters of blockages, higher data quality and availability were observed to have a higher mean prediction accuracy. The applied conceptual framework showed that intuitive asset management characterised the current state of blockage management in the municipal sewer network C with medium to good data quality and low interoperability.
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Jiang, Tianyu. "Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845.

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Larsson, Olsson Christoffer, and Erik Svensson. "Early Warning Leakage Detection for Pneumatic Systems on Heavy Duty Vehicles : Evaluating Data Driven and Model Driven Approach." Thesis, KTH, Mekatronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261207.

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Modern Heavy Duty Vehicles consist of a multitude of components and operate in various conditions. As there is value in goods transported, there is an incentive to avoid unplanned breakdowns. For this, condition based maintenance can be applied.\newline This thesis presents a study comparing the applicability of the data-driven Consensus SelfOrganizing Models (COSMO) method and the model-driven patent series introduced by Fogelstrom, applied on the air processing system for leakage detection on Scania Heavy Duty Vehicles. The comparison of the two methods is done using the Area Under Curve value given by the Receiver Operating Characteristics curves for features in order to reach a verdict.\newline For this purpose, three criteria were investigated. First, the effects of the hyper-parameters were explored to conclude a necessary vehicle fleet size and time period required for COSMO to function. The second experiment regarded whether environmental factors impact the predictability of the method, and finally the effect on the predictability for the case of nonidentical vehicles was determined.\newline The results indicate that the number of representations ought to be at least 60, rather with a larger set of vehicles in the fleet than with a larger window size, and that the vehicles should be close to identical on a component level and be in use in comparable ambient conditions.\newline In cases where the vehicle fleet is heterogeneous, a physical model of each system is preferable as this produces more stable results compared to the COSMO method.
Moderna tunga fordon består av ett stort antal komponenter och används i många olika miljöer. Då värdet för tunga fordon ofta består i hur mycket gods som transporteras uppstår ett incitament till att förebygga oplanerade stopp. Detta görs med fördel med hjälp av tillståndsbaserat underhåll. Denna avhandling undersöker användbarheten av den data-drivna metoden Consensus SelfOrganizing Models (COSMO) kontra en modellbaserad patentserie för att upptäcka läckage på luftsystem i tunga fordon. Metoderna ställs mot varandra med hjälp av Area Under Curve-värdet som kommer från Receiver Operating Characteristics-kurvor från beskrivande signaler. Detta gjordes genom att utvärdera tre kriterier. Dels hur hyperparametrar influerar COSMOmetoden för att avgöra en rimlig storlek på fordonsflottan, dels huruvida omgivningsförhållanden påverkar resultatet och slutligen till vilken grad metoden påverkas av att fordonsflottan inte är identisk. Slutsatsen är att COSMO-metoden med fördel kan användas sålänge antalet representationer överstiger 60 och att fordonen inom flottan är likvärdiga och har använts inom liknande omgivningsförhållanden. Om fordonsflottan är heterogen så föredras en fysisk modell av systemet då detta ger ett mer stabilt resultat jämfört med COSMO-metoden.
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Dinmohammadi, Fateme. "Data-driven risk-based modelling approaches to maintenance optimisation of railway transport assets." Thesis, Glasgow Caledonian University, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.743925.

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Lundgren, Andreas. "Data-Driven Engine Fault Classification and Severity Estimation Using Residuals and Data." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165736.

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Recent technological advances in the automotive industry have made vehicularsystems increasingly complex in terms of both hardware and software. As thecomplexity of the systems increase, so does the complexity of efficient monitoringof these system. With increasing computational power the field of diagnosticsis becoming evermore focused on software solutions for detecting and classifyinganomalies in the supervised systems. Model-based methods utilize knowledgeabout the physical system to device nominal models of the system to detect deviations,while data-driven methods uses historical data to come to conclusionsabout the present state of the system in question. This study proposes a combinedmodel-based and data-driven diagnostic framework for fault classification,severity estimation and novelty detection. An algorithm is presented which uses a system model to generate a candidate setof residuals for the system. A subset of the residuals are then selected for eachfault using L1-regularized logistic regression. The time series training data fromthe selected residuals is labelled with fault and severity. It is then compressedusing a Gaussian parametric representation, and data from different fault modesare modelled using 1-class support vector machines. The classification of datais performed by utilizing the support vector machine description of the data inthe residual space, and the fault severity is estimated as a convex optimizationproblem of minimizing the Kullback-Leibler divergence (kld) between the newdata and training data of different fault modes and severities. The algorithm is tested with data collected from a commercial Volvo car enginein an engine test cell and the results are presented in this report. Initial testsindicate the potential of the kld for fault severity estimation and that noveltydetection performance is closely tied to the residual selection process.
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Kullenda, Kuben. "Enabling firm performance through data driven decision making in maintenance management : a dynamic capabilities view." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/79594.

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Maintenance management is seen as a “necessary evil”, rather than a profit contributing resource that could intensify competitive advantage for the organisation. With the world facing the fourth industrial revolution, a radical increase in the reshaping of companies and competition within asset intensive industries is being observed. Organisations in these industries are being forced to rethink traditional ways of working and gearing the workforce with higher and more diversified competency profiles. This suggests that the traditional way of executing maintenance management, being predominantly reactive with the lack of data driven decision making, is certainly inadequate for a sustainable competitive advantage. An improved way of managing maintenance should be through developing and applying dynamic capabilities within the maintenance domain of the organisation. This research draws on theories of dynamic capabilities (DC), decision making performance (DMP), business process performance (BPP) and firm performance (Fper), in the context of data driven decision making in organisations heavily reliant on good maintenance management practices. The aim of this study was to explore and understand the relationships between these constructs, for insight into further improvement and development of a competitive advantage. The findings presented a statistically significant relationship between DC and Fper, DC and BPP, DC and DMP, but most importantly, a multiple full indirect mediation role was observed, which provides insights for both business and for further studies in academia.
Mini Dissertation (MBA)--University of Pretoria, 2020.
pt2021
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted
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Ponomarenko, Maksym. "Maintenance of the Quality Monitor Web-Application." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-28816.

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Applied Research in System Analysis (ARiSA) is a company specialized in the development of the customer-specific quality models and applied research work. In order to improve the quality of the projects and to reduce maintenance costs, ARiSA developed Quality Monitor (QM) – a web application for quality analysis. QM application has been originally developed as a basic program to enable customers to evaluate the quality of the sources. Therefore, the business logic of the application was simplified and certain limitations were imposed on it, which in its turn leads to a number of issues related to user experience, performance and architecture design. These aspects are important for both application as a product, and for its future promotion. Moreover, this is important for customers, as end users. Main application issues, which were added to the maintenance list are: manual data upload, insufficient server resources to handle long-running and resource consuming operations, no background processing and status reporting, simplistic presentation of analysis results and known usability issues, weak integration between analysis back-ends and front-end. ­­­­­­­­­­­In order to address known issues and to make improvements of the existing limitations, a maintenance phase of QM application is initiated. First of all, it is intended to stabilize current version and improve user experience. It also needed for refactoring and implementation of more efficient data uploads processing in the background. In addition, extended functionality of QM would fulfill customer needs and transform application from the project into a product. Extended functionality includes: automated data upload from different build processes, new data visualizations, and improvement of the current functionality according to customer comments. Maintenance phase of QM application has been successfully completed and master thesis goals are met. Current version is more stable and more responsive from user experience perspective. Data processing is more efficient, and now it is implemented as background analysis with automatic data import. User interface has been updated with visualizations for client-side interaction and progress reporting. The solution has been evaluated and tested in close cooperation with QM application customers. This thesis describes requirements analysis, technology stack with choice rationale and implementation to show maintenance results.
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Lembke, Benjamin. "Bearing Diagnosis Using Fault Signal Enhancing Teqniques and Data-driven Classification." Thesis, Linköpings universitet, Fordonssystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158240.

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Rolling element bearings are a vital part in many rotating machinery, including vehicles. A defective bearing can be a symptom of other problems in the machinery and is due to a high failure rate. Early detection of bearing defects can therefore help to prevent malfunction which ultimately could lead to a total collapse. The thesis is done in collaboration with Scania that wants a better understanding of how external sensors such as accelerometers, can be used for condition monitoring in their gearboxes. Defective bearings creates vibrations with specific frequencies, known as Bearing Characteristic Frequencies, BCF [23]. A key component in the proposed method is based on identification and extraction of these frequencies from vibration signals from accelerometers mounted near the monitored bearing. Three solutions are proposed for automatic bearing fault detection. Two are based on data-driven classification using a set of machine learning methods called Support Vector Machines and one method using only the computed characteristic frequencies from the considered bearing faults. Two types of features are developed as inputs to the data-driven classifiers. One is based on the extracted amplitudes of the BCF and the other on statistical properties from Intrinsic Mode Functions generated by an improved Empirical Mode Decomposition algorithm. In order to enhance the diagnostic information in the vibration signals two pre-processing steps are proposed. Separation of the bearing signal from masking noise are done with the Cepstral Editing Procedure, which removes discrete frequencies from the raw vibration signal. Enhancement of the bearing signal is achieved by band pass filtering and amplitude demodulation. The frequency band is produced by the band selection algorithms Kurtogram and Autogram. The proposed methods are evaluated on two large public data sets considering bearing fault classification using accelerometer data, and a smaller data set collected from a Scania gearbox. The produced features achieved significant separation on the public and collected data. Manual detection of the induced defect on the outer race on the bearing from the gearbox was achieved. Due to the small amount of training data the automatic solutions were only tested on the public data sets. Isolation performance of correct bearing and fault mode among multiplebearings were investigated. One of the best trade offs achieved was 76.39 % fault detection rate with 8.33 % false alarm rate. Another was 54.86 % fault detection rate with 0 % false alarm rate.
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Books on the topic "Data-driven maintenance"

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Gama, Joao, Sepideh Pashami, Albert Bifet, Moamar Sayed-Mouchawe, Holger Fröning, Franz Pernkopf, Gregor Schiele, and Michaela Blott, eds. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66770-2.

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Kiritsis, Dimitris, Melinda Hodkiewicz, Oscar Lazaro, Jay Lee, and Jun Ni, eds. Data-Driven Cognitive Manufacturing - Applications in Predictive Maintenance and Zero Defect Manufacturing. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-583-9.

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Book chapters on the topic "Data-driven maintenance"

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Alshakhshir, Fadi, and Marvin T. Howell. "Different Maintenance Types and the Need for Energy Centered Maintenance." In Data Driven Energy Centered Maintenance, 21–30. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-2.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process – Data Collection." In Data Driven Energy Centered Maintenance, 45–51. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-5.

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Alshakhshir, Fadi, and Marvin T. Howell. "Energy Centered Maintenance in Data Centers." In Data Driven Energy Centered Maintenance, 155–57. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-11.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process — Measuring Equipment Current Performance." In Data Driven Energy Centered Maintenance, 81–87. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-7.

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Alshakhshir, Fadi, and Marvin T. Howell. "Conclusion." In Data Driven Energy Centered Maintenance, 223–34. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-16.

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Alshakhshir, Fadi, and Marvin T. Howell. "Energy Centered Maintenance to avoid Low Delta T Syndrome in Chilled Water Systems." In Data Driven Energy Centered Maintenance, 145–53. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-10.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process — Updating Preventative Maintenance Plans." In Data Driven Energy Centered Maintenance, 95–143. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-9.

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Alshakhshir, Fadi, and Marvin T. Howell. "Building Energy Centered Behavior Leading to an Energy Centered Culture." In Data Driven Energy Centered Maintenance, 195–99. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-14.

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Alshakhshir, Fadi, and Marvin T. Howell. "Energy Reduction." In Data Driven Energy Centered Maintenance, 1–19. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-1.

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Alshakhshir, Fadi, and Marvin T. Howell. "Measures of Equipment and Maintenance Efficiency and Effectiveness." In Data Driven Energy Centered Maintenance, 159–69. New York: River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-12.

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Conference papers on the topic "Data-driven maintenance"

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Thompson, M. "Implementing a Data Driven Maintenance Approach." In 8th International Conference on Railway Engineering (ICRE 2018). Institution of Engineering and Technology, 2018. http://dx.doi.org/10.1049/cp.2018.0054.

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Kabir, Farzana, Brandon Foggo, and Nanpeng Yu. "Data Driven Predictive Maintenance of Distribution Transformers." In 2018 China International Conference on Electricity Distribution (CICED). IEEE, 2018. http://dx.doi.org/10.1109/ciced.2018.8592417.

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Raoslash;dseth, Harald, and Per Schjaoslash;lberg. "Data-driven Predictive Maintenance for Green Manufacturing." In 6th International Workshop of Advanced Manufacturing and Automation. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/iwama-16.2016.7.

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Misra, Janardan, Shubhashis Sengupta, Divya Rawat, Milind Savagaonkar, and Sanjay Podder. "Data-Driven Application Maintenance: Experience from the Trenches." In 2017 IEEE/ACM 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP). IEEE, 2017. http://dx.doi.org/10.1109/ser-ip.2017..8.

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Liu, Yu, Hong-Zhong Huang, and Xiaoling Zhang. "Data-driven approach for imperfect maintenance model selection." In Integrity (RAMS). IEEE, 2011. http://dx.doi.org/10.1109/rams.2011.5754499.

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Abonyi, Janos, Barbara Farsang, and Tibor Kulcsar. "Data-driven development and maintenance of soft-sensors." In 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2014. http://dx.doi.org/10.1109/sami.2014.6822414.

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Apostolov, A. P. "Event driven maintenance based on protective relays data." In 7th International Conference on Developments in Power Systems Protection (DPSP 2001). IEE, 2001. http://dx.doi.org/10.1049/cp:20010091.

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Li, Lin, Dragan Djurdjanovic, and Jun Ni. "Maintenance Task Prioritization Using Data Driven Bottleneck Detection and Maintenance Opportunity Windows." In ASME 2007 International Manufacturing Science and Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/msec2007-31150.

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Maintenance operations have a direct influence on production performance in manufacturing systems. Maintenance task prioritization is crucial and important, especially when availability of maintenance resources is limited. The decision on task assignment is often made through heuristic methods or experience, which could cause more downtime and the production losses. In this paper, a new maintenance task prioritization policy based on data driven bottleneck detection and reliability-based maintenance opportunity window calculation is introduced. An experiment in simulation of a real production line shows the proposed policy is able to improve the system reliability, increase the throughput and minimize the total cost of system operation.
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Agrawal, G. "Simultaneous demand-driven data-flow and call graph analysis." In Proceedings IEEE International Conference on Software Maintenance - 1999 (ICSM'99). 'Software Maintenance for Business Change' (Cat. No.99CB36360). IEEE, 1999. http://dx.doi.org/10.1109/icsm.1999.792643.

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Yang, Shuai, Chaoqin Liu, Xue Zhou, Wei Liang, and Qiang Miao. "Investigation on data-driven life prediction methods." In 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). IEEE, 2012. http://dx.doi.org/10.1109/icqr2mse.2012.6246322.

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Reports on the topic "Data-driven maintenance"

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, December 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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Abstract:
The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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