Real-time predictive maintenance for wind turbines using Big Data frameworks

Canizo, Mikel; Onieva, Enrique; Conde, Ángel; Charramendieta, Santiago; Trujillo, Salvador

doi:10.1109/icphm.2017.7998308

Cited by 140 publications

(91 citation statements)

References 23 publications

(41 reference statements)

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“…Some studies refer both on CM data and event data. For example, Canizo et al [28] presented a Big Data analytics approach for the renewable energy field. In particular, they developed a PdM application for wind turbines by using a Big Data processing framework to generate data-driven predictive models that are based upon historical operational data (e.g., power, wind speed, rotor speed, and generator speed recorded) and system status data, previously stored in the cloud.…”

Section: Related Workmentioning

confidence: 99%

SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0

et al. 2020

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Predictive Maintenance (PdM) is a prominent strategy comprising all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the main challenges of PdM is to design and develop an embedded smart system to monitor and predict the health status of the machine. In this work, we use a data-driven approach based on machine learning applied to woodworking industrial machines for a major woodworking Italian corporation. Predicted failures probabilities are calculated through tree-based classification models (Gradient Boosting, Random Forest and Extreme Gradient Boosting) and calculated as the temporal evolution of event data. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime (RUL) of woodworking machines. The effectiveness of the proposed method is showed by testing an independent sample of additional woodworking machines without presenting machine down. The Gradient Boosting model achieved accuracy, recall, and precision of 98.9%, 99.6%, and 99.1%. Our predictive maintenance approach deployed on a Big Data framework allows screening simultaneously multiple connected machines by learning from terabytes of log data. The target prediction provides salient information which can be adopted within the maintenance management practice.

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Section: Related Workmentioning

confidence: 99%

SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0

et al. 2020

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“…Furthermore, a Data Mining block is implemented (as one of the upper blocks) to (i) identify key features on data and (ii) learn the relation between those features (input variables) and the target application. Finally, the other upper block, which interacts with the application user, is the Data visualisation block that shows important information about the application and the obtained predictions [9].…”

Section: Related Workmentioning

confidence: 99%

Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance

et al. 2020

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Heating appliances consume approximately 48 % of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.

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“…As for compatibility, it initially supports LXC, as well as Docker. This orchestration system is often used to organize the solution of issues in the field of Big Data processing [15,52,77,86].…”

Section: Review Of Container Orchestration Solutionsmentioning

confidence: 99%

Comparative Analysis of Virtualization Methods in Big Data Processing

Радченко

Alaasam

Tchernykh

2019

JSFI

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Cloud computing systems have become widely used for Big Data processing, providing access to a wide variety of computing resources and a greater distribution between multi-clouds. This trend has been strengthened by the rapid development of the Internet of Things (IoT) concept. Virtualization via virtual machines and containers is a traditional way of organization of cloud computing infrastructure. Containerization technology provides a lightweight virtual runtime environment. In addition to the advantages of traditional virtual machines in terms of size and flexibility, containers are particularly important for integration tasks for PaaS solutions, such as application packaging and service orchestration. In this paper, we overview the current state-ofthe-art of virtualization and containerization approaches and technologies in the context of Big Data tasks solution. We present the results of studies which compare the efficiency of containerization and virtualization technologies to solve Big Data problems. We also analyze containerized and virtualized services collaboration solutions to support automation of the deployment and execution of Big Data applications in the cloud infrastructure.

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Real-time predictive maintenance for wind turbines using Big Data frameworks

Cited by 140 publications

References 23 publications

SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0

SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0

Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance

Comparative Analysis of Virtualization Methods in Big Data Processing

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