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AI and Statistical Technologies for Manufacturing and Maintenance Strategies Improvement: Health Monitoring for Electromechanical Actuators

AI and Statistical Technologies for Manufacturing and Maintenance Strategies Improvement: Health Monitoring for Electromechanical Actuators

Susana Ferrerio Del Río, Santiago Fernández, Iñaki Bravo-Imaz, Egoitz Konde, Aitor Arnaiz Irigaray
Copyright: © 2020 |Pages: 20
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch029
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MLA

Del Río, Susana Ferrerio, et al. "AI and Statistical Technologies for Manufacturing and Maintenance Strategies Improvement: Health Monitoring for Electromechanical Actuators." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 569-588. https://doi.org/10.4018/978-1-7998-2460-2.ch029

APA

Del Río, S. F., Fernández, S., Bravo-Imaz, I., Konde, E., & Irigaray, A. A. (2020). AI and Statistical Technologies for Manufacturing and Maintenance Strategies Improvement: Health Monitoring for Electromechanical Actuators. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 569-588). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch029

Chicago

Del Río, Susana Ferrerio, et al. "AI and Statistical Technologies for Manufacturing and Maintenance Strategies Improvement: Health Monitoring for Electromechanical Actuators." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 569-588. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch029

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Abstract

The development and the implementation of advanced actuation systems has increased in recent years, as many factors are driving the migration from hydraulic actuators to electromechanical actuators (EMAs) in aeronautics. But not only do we have to consider the right design to customize the system from the requirements oriented to the final application, also additional functions that can provide the system with additional value, to make it more competitive in this market. This is the case of the Health Monitoring (HM) systems. The development, implementation and integration of predictive algorithms into the environment of the EMA provide the system with an additional functionality, from which it is possible to detect failures at an early stage in order to avoid catastrophic accidents and improve maintenance activities. This work shows how to develop HM algorithms based on AI and Statistical technologies to detect and predict early stages of failure in a gearbox, which can directly affect to the transmission of power in EMAs.

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