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Journal articles on the topic 'Data-driven maintenance'

Author: Grafiati

Published: 25 June 2021

Last updated: 18 February 2022

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1

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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4

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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5

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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6

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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7

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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10

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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11

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

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12

Neuhold, Johannes, Matthias Landgraf, Stefan Marschnig, and Peter Veit. "Measurement Data-Driven Life-Cycle Management of Railway Track." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 11 (September 10, 2020): 685–96. http://dx.doi.org/10.1177/0361198120946007.

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

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13

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

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

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

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

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Wang, Hai, Su Xie, Ke Li, and M. Ahmad. "Big Data-Driven Cellular Information Detection and Coverage Identification." Sensors 19, no. 4 (February 22, 2019): 937. http://dx.doi.org/10.3390/s19040937.

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

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Corman, Francesco, Sander Kraijema, Milinko Godjevac, and Gabriel Lodewijks. "Optimizing preventive maintenance policy: A data-driven application for a light rail braking system." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 5 (June 19, 2017): 534–45. http://dx.doi.org/10.1177/1748006x17712662.

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

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17

Antomarioni, Sara, Maurizio Bevilacqua, Domenico Potena, and Claudia Diamantini. "Defining a data-driven maintenance policy: an application to an oil refinery plant." International Journal of Quality & Reliability Management 36, no. 1 (January 7, 2019): 77–97. http://dx.doi.org/10.1108/ijqrm-01-2018-0012.

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

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18

Zhang, Weiting, Dong Yang, and Hongchao Wang. "Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey." IEEE Systems Journal 13, no. 3 (September 2019): 2213–27. http://dx.doi.org/10.1109/jsyst.2019.2905565.

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19

Krueger, Minjia, Adel Haghani, Steven X. Ding, Torsten Jeinsch, and Peter Engel. "A Data-Driven Maintenance Support System for Wind Energy Conversion Systems." IFAC Proceedings Volumes 47, no. 3 (2014): 11470–75. http://dx.doi.org/10.3182/20140824-6-za-1003.02013.

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20

Langdon, W. B. "Big data driven genetic improvement for maintenance of legacy software systems." ACM SIGEVOlution 12, no. 3 (January 28, 2020): 6–9. http://dx.doi.org/10.1145/3381343.3381345.

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21

Hogland, William, Christos Katrantsiotis, and Muhammad Asim Ibrahim. "Baltic Smart Asset Management – data driven predictive maintenance methods for future." IOP Conference Series: Earth and Environmental Science 578 (November 4, 2020): 012035. http://dx.doi.org/10.1088/1755-1315/578/1/012035.

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22

Farooq, Basit, Jinsong Bao, Jie Li, Tianyuan Liu, and Shiyong Yin. "Data-Driven Predictive Maintenance Approach for Spinning Cyber-Physical Production System." Journal of Shanghai Jiaotong University (Science) 25, no. 4 (March 31, 2020): 453–62. http://dx.doi.org/10.1007/s12204-020-2178-z.

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23

Davari, Narjes, Bruno Veloso, Gustavo de Assis Costa, Pedro Mota Pereira, Rita P. Ribeiro, and João Gama. "A Survey on Data-Driven Predictive Maintenance for the Railway Industry." Sensors 21, no. 17 (August 26, 2021): 5739. http://dx.doi.org/10.3390/s21175739.

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

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24

Catelani, Marcantonio, Lorenzo Ciani, Diego Galar, and Gabriele Patrizi. "Optimizing Maintenance Policies for a Yaw System Using Reliability-Centered Maintenance and Data-Driven Condition Monitoring." IEEE Transactions on Instrumentation and Measurement 69, no. 9 (September 2020): 6241–49. http://dx.doi.org/10.1109/tim.2020.2968160.

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25

Chi, Zhexiang, Taotao Zhou, Simin Huang, and Yan-Fu Li. "A data-driven approach for the health prognosis of high-speed train wheels." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 6 (June 19, 2020): 735–47. http://dx.doi.org/10.1177/1748006x20929158.

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Polygonal wear is one of the most critical failure modes of high-speed train wheels that would significantly compromise the safety and reliability of high-speed train operation. However, the mechanism underpinning wheel polygon is complex and still not fully understood, which makes it challenging to track its evolution of the polygonal wheel. The large amount of data gathered through regular inspection and maintenance of Chinese high-speed trains provides a promising way to tackle this challenge with data-driven methods. This article proposes a data-driven approach to predict the degree of the polygonal wear, assess the reliability of individual wheels and the health index of all wheels of a high-speed train for maintenance priority ranking. The synthetic minority over-sampling technique—nominal continuous is adopted to augment the maintenance dataset of imbalanced and mixed features. The autoencoder is used to learn abstract features to represent the original datasets, which are then fed into a support vector machine classifier. The approach is coherently optimized by tuning the model hyper-parameters based on Bayesian optimization. The effectiveness of our proposed approach is demonstrated by the wheel maintenance data obtained from the year 2016 to 2017. The results can also be used to support practical maintenance priority allocation.

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Bousdekis, Alexandros, Katerina Lepenioti, Dimitris Apostolou, and Gregoris Mentzas. "A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications." Electronics 10, no. 7 (March 31, 2021): 828. http://dx.doi.org/10.3390/electronics10070828.

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Decision-making for manufacturing and maintenance operations is benefiting from the advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data, predict emerging situations, and recommend mitigating actions. The current paper reviews the literature on data-driven decision-making in maintenance and outlines directions for future research towards data-driven decision-making for Industry 4.0 maintenance applications. The main research directions include the coupling of decision-making with augmented reality for seamless interfacing that combines the real and virtual worlds of manufacturing operators; methods and techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices; integration of maintenance decision-making with other operations such as scheduling and planning; utilization of the cloud continuum for optimal deployment of decision-making services; capability of decision-making methods to cope with big data; incorporation of advanced security mechanisms; and coupling decision-making with simulation software, autonomous robots, and other additive manufacturing initiatives.

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Escobet, Antoni, Teresa Escobet, Joseba Quevedo, and Adoración Molina. "Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant." Applied System Innovation 3, no. 3 (August 11, 2020): 34. http://dx.doi.org/10.3390/asi3030034.

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This paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.

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Gordon, Christopher Ampofo Kwadwo, Baris Burnak, Melis Onel, and Efstratios N. Pistikopoulos. "Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling." Industrial & Engineering Chemistry Research 59, no. 44 (October 26, 2020): 19607–22. http://dx.doi.org/10.1021/acs.iecr.0c03241.

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Kumar, Ajay, Ravi Shankar, and Lakshman S. Thakur. "A big data driven sustainable manufacturing framework for condition-based maintenance prediction." Journal of Computational Science 27 (July 2018): 428–39. http://dx.doi.org/10.1016/j.jocs.2017.06.006.

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Day, Christopher M., Howell Li, James R. Sturdevant, and Darcy M. Bullock. "Data-Driven Ranking of Coordinated Traffic Signal Systems for Maintenance and Retiming." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 18 (August 29, 2018): 167–78. http://dx.doi.org/10.1177/0361198118794042.

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Automated traffic signal performance measures (ATSPMs) have been deployed with increasing frequency. At present, the existing ATSPMs are focused on the performance of individual movements or intersections. As the number of ATSPM users has increased, a need for system-level metrics has emerged. This paper proposes a method of evaluating corridor performance at the system level using high-resolution data. The method is demonstrated for eight signalized corridors in Indiana, including 87 intersections. This method develops five subscores for the areas of communication, detection, safety, capacity allocation, and progression; these five interrelated aspects of performance are each given a category subscore based on quantitative performance measures, with scales appropriate to the context of the operation. An overall score for each corridor is determined from the lowest subscore of each of the five areas. This approach simplifies the analysis process, as opposed to examining several hundred individual movements as currently would be required using ATSPM tools that are commonly available at present. The methodology is presented as a prototype for further development and adaptation to individual agency objectives and data sources.

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Subramaniyan, Mukund, Anders Skoogh, Azam Sheikh Muhammad, Jon Bokrantz, Björn Johansson, and Christoph Roser. "A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective." Computers & Industrial Engineering 150 (December 2020): 106851. http://dx.doi.org/10.1016/j.cie.2020.106851.

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Filz, Marc-André, Jonas Ernst Bernhard Langner, Christoph Herrmann, and Sebastian Thiede. "Data-driven failure mode and effect analysis (FMEA) to enhance maintenance planning." Computers in Industry 129 (August 2021): 103451. http://dx.doi.org/10.1016/j.compind.2021.103451.

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Macchi, Marco, Adolfo Crespo Márquez, Maria Holgado, Luca Fumagalli, and Luis Barberá Martínez. "Value-driven engineering of E-maintenance platforms." Journal of Manufacturing Technology Management 25, no. 4 (April 29, 2014): 568–98. http://dx.doi.org/10.1108/jmtm-04-2013-0039.

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Purpose – The purpose of this paper is to propose a methodology for the engineering of E-maintenance platforms that is based on a value-driven approach. Design/methodology/approach – The methodology assumes that a value-driven engineering approach would help foster technological innovation for maintenance management. Indeed, value-driven engineering could be easily adopted at the business level, with subsequent positive effects on the industrial applications of new information and communication technologies solutions. Findings – The methodology combines a value-driven approach with the engineering in the maintenance scope. The methodology is tested in a manufacturing case to prove its potential to support the engineering of E-maintenance solutions. In particular, the case study concerns the investment in E-maintenance solutions developed in the framework of a Supervisory Control and Data Acquisition system originally implemented for production purposes. Originality/value – Based on literature research, the paper presents a methodology that is implemented considering three different approaches (business theories, value-driven engineering and maintenance management). The combination of these approaches is novel and overcomes the traditional view of maintenance as an issue evaluated from a cost-benefit perspective.

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Bunakov, Vasily, Catherine Jones, Brian Matthews, and Michael Wilson. "Data authenticity and data value in policy-driven digital collections." OCLC Systems & Services: International digital library perspectives 30, no. 4 (November 10, 2014): 212–31. http://dx.doi.org/10.1108/oclc-07-2013-0025.

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Purpose – The purpose of this paper is to suggest an approach to data value considerations that is related to the generalized notion of authenticity and can be applied to the design of preservation policies. There has been considerable progress in the scalable architectures for policy-driven digital collection preservation as well as in modeling preservation costs. However, modeling the value of both digital artifacts and collections seems a more elusive topic that has yet to find a proper methodology and means of expression. Design/methodology/approach – A top-down conceptual analysis was developed and the principles of information technology service management and quality management were applied to the domain of digital preservation. Then, in a bottom-up analysis, the various notions of authenticity in digital preservation projects, reference models and conceptual papers were reviewed. Findings – The top-down and bottom-up analyses have a meeting point, establishing a close relation between the concepts of data authenticity and data value. Originality/value – The generalized understanding of authenticity can support the design of sensible preservation policies and their application to the formation and long-term maintenance of digital collections.

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Belov, Sergei, Sergei Nikolaev, and Ighor Uzhinsky. "Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics." International Journal of Turbomachinery, Propulsion and Power 5, no. 4 (November 9, 2020): 29. http://dx.doi.org/10.3390/ijtpp5040029.

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This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.

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Cao, Xiangang, Pengfei Li, and Song Ming. "Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven." Sustainability 13, no. 15 (July 31, 2021): 8548. http://dx.doi.org/10.3390/su13158548.

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Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under data-driven to extend equipment life, promoting sustainable development. The stochastic degradation model was established based on the nonlinear Wiener process. A combination of real-time update and offline estimation estimated the degradation model’s parameters and deduced the equipment’s RUL distribution. Based on the RUL prediction results, we established a maintenance decision model with the lowest long-term cost rate as the goal. Case analysis shows that the model proposed in this paper can improve the accuracy of RUL prediction and realize equipment sustainability.

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Xie, Jiawei, Jinsong Huang, Cheng Zeng, Shui-Hua Jiang, and Nathan Podlich. "Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering." Geosciences 10, no. 11 (October 26, 2020): 425. http://dx.doi.org/10.3390/geosciences10110425.

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Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling. The railway industry is now collecting a large amount of data with the fast-paced development of sensor technologies. These data sets carry information about the conditions of various components in railway track. Since just before the beginning of the 21st century, data-driven models have been used in the predictive maintenance of railway track. This study presents a systematic literature review of data-driven models applied in the predictive maintenance of railway track. A taxonomy to classify the existing literature based on types of models and types of applications is provided. It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track. Rail geometry irregularity, rail head defect, and missing rail components detection were the top three most commonly considered issues within the application of data-driven models. Prediction of rail breaks has received increasing attention in the last four years. Among these data-driven model applications, the collected data types are the most critical factors which affect selecting suitable models. Finally, this study discusses upcoming challenges in the predictive maintenance of railway track.

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Basciftci, Beste, Shabbir Ahmed, and Nagi Gebraeel. "Data-driven maintenance and operations scheduling in power systems under decision-dependent uncertainty." IISE Transactions 52, no. 6 (October 1, 2019): 589–602. http://dx.doi.org/10.1080/24725854.2019.1660831.

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Chien, Chen-Fu, and Chia-Cheng Chen. "Data-Driven Framework for Tool Health Monitoring and Maintenance Strategy for Smart Manufacturing." IEEE Transactions on Semiconductor Manufacturing 33, no. 4 (November 2020): 644–52. http://dx.doi.org/10.1109/tsm.2020.3024284.

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40

Zhong, Jun, Wenyuan Li, Caisheng Wang, and Juan Yu. "A RankBoost-Based Data-Driven Method to Determine Maintenance Priority of Circuit Breakers." IEEE Transactions on Power Delivery 33, no. 3 (June 2018): 1044–53. http://dx.doi.org/10.1109/tpwrd.2017.2748146.

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Savolainen, P., J. Magnusson, M. Gopalakrishnan, E. Turanoglu Bekar, and A. Skoogh. "Organisational Constraints in Data-driven Maintenance: a case study in the automotive industry." IFAC-PapersOnLine 53, no. 3 (2020): 95–100. http://dx.doi.org/10.1016/j.ifacol.2020.11.015.

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Baptista, Marcia, Shankar Sankararaman, Ivo P. de Medeiros, Cairo Nascimento, Helmut Prendinger, and Elsa M. P. Henriques. "Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling." Computers & Industrial Engineering 115 (January 2018): 41–53. http://dx.doi.org/10.1016/j.cie.2017.10.033.

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Jain, Prerna, Efstratios N. Pistikopoulos, and M. Sam Mannan. "Process resilience analysis based data-driven maintenance optimization: Application to cooling tower operations." Computers & Chemical Engineering 121 (February 2019): 27–45. http://dx.doi.org/10.1016/j.compchemeng.2018.10.019.

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Li, Yipeng, SeyedReza RazaviAlavi, and Simaan AbouRizk. "Data-Driven Simulation Approach for Short-Term Planning of Winter Highway Maintenance Operations." Journal of Computing in Civil Engineering 35, no. 5 (September 2021): 04021013. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000980.

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Xia, Ye, Xiaoming Lei, Peng Wang, and Limin Sun. "Artificial Intelligence Based Structural Assessment for Regional Short- and Medium-Span Concrete Beam Bridges with Inspection Information." Remote Sensing 13, no. 18 (September 15, 2021): 3687. http://dx.doi.org/10.3390/rs13183687.

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The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based methodology to solve the condition assessment of regional bridges and optimize their maintenance schemes. It includes data integration, condition assessment, and maintenance optimization. Data from bridge inspection reports is the main source of this data-driven approach, which could provide a substantial amount og condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified. Given the need to manage limited resources and ensure safety and functionality, adequate maintenance schemes for regional bridges are optimized with genetic algorithms. The proposed data-driven methodology is applied to real regional highway bridges. The regional inspection information is obtained with the help of emerging technologies. The established structural deterioration models achieve up to 85% prediction accuracy. The obtained optimal maintenance schemes could be chosen according to actual structural conditions, maintenance requirements, and total budget. Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges.

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Ton, Bram, Rob Basten, John Bolte, Jan Braaksma, Alessandro Di Bucchianico, Philippe van de Calseyde, Frank Grooteman, et al. "PrimaVera: Synergising Predictive Maintenance." Applied Sciences 10, no. 23 (November 24, 2020): 8348. http://dx.doi.org/10.3390/app10238348.

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The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions.

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Möhring, Michael, Rainer Schmidt, Barbara Keller, Kurt Sandkuhl, and Alfred Zimmermann. "Predictive Maintenance Information Systems." International Journal of Enterprise Information Systems 16, no. 2 (April 2020): 22–37. http://dx.doi.org/10.4018/ijeis.2020040102.

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Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.

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Nabati, Elaheh Gholamzadeh, and Klaus-Dieter Thoben. "Data Driven Decision Making in Planning the Maintenance Activities of Off-shore Wind Energy." Procedia CIRP 59 (2017): 160–65. http://dx.doi.org/10.1016/j.procir.2016.09.026.

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Morant, Amparo, Per-Olof Larsson-Kråik, and Uday Kumar. "Data-driven model for maintenance decision support: A case study of railway signalling systems." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 230, no. 1 (May 14, 2014): 220–34. http://dx.doi.org/10.1177/0954409714533680.

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Letot, C., P. Dersin, M. Pugnaloni, P. Dehombreux, G. Fleurquin, C. Douziech, and P. La-Cascia. "A data driven degradation-based model for the maintenance of turnouts: a case study." IFAC-PapersOnLine 48, no. 21 (2015): 958–63. http://dx.doi.org/10.1016/j.ifacol.2015.09.650.

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