Aeroengines Remaining Useful Life Prediction Based on Improved C-Loss ELM

Zhang, Bowen; Li, Yanjun; Bai, Yu; Cao, Yue

doi:10.1109/access.2020.2979747

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…A Battery Management System (BMS) is a system that manages a rechargeable battery's charging and discharging, specifically in electric vehicles to maintain safety, prolong battery life and maintain optimum performance (Yang et al, 2020). One useful way of handling battery arrays is the 3-Series system or '3S'.…”

Section: Battery Management Systemmentioning

confidence: 99%

Intersection of domains: Designing a remote-controlled dish antenna

Oluwole,

Olaluyi,

Johnson

2024

dujopas

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The advent of technology has instigated rapid advances in the field of communications, a prime example being the remote-controlled dish antenna, which embodies a parabolic design intended to intercept and concentrate electromagnetic waves onto a common focal point. The modification towards remote control mandates a core understanding of diverse technological realms as it involves an amalgamation of com-ponents and knowledge. This discourse probes into the nuanced design and implementation of a remote-controlled dish antenna, conceived around the core touchstones of a microcontroller and an array of complementary components. The research work signifies a compounding of several technological do-mains, including microcontroller programming, power electronics, wireless communication, and motor control, necessitating extensive proficiency in these areas and consequently, offering an invaluable foundation to the broader control system applications. The overarching aim of this research is to enable users to effectively control a dish antenna's positioning remotely, leveraging a Bluetooth HC-05 module for optimal convenience and accessibility. This revolutionary project relies on a PIC18F2550 microcon-troller, a high-performance RISC CPU with integrated EEPROM and SRAM, which allows for flexible data storage and retrieval. Considering its compatibility with a variety of peripherals, the PIC18F2550 emerges as a logical choice, significantly enhancing the system's performance efficacy.

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Section: Battery Management Systemmentioning

confidence: 99%

Intersection of domains: Designing a remote-controlled dish antenna

Oluwole,

Olaluyi,

Johnson

2024

dujopas

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“…The article [13] uses sparse Bayesian learning (SBL) [14] to establish health indicators. In addition, there are methods of constructing an HI index based on classical ML clustering, proposed, for example, in [15], where a new index is constructed using the sample distances to each cluster.…”

Section: Auxiliary Health Characteristicmentioning

confidence: 99%

Interaction models for remaining useful life estimation

Zhevnenko¹,

Kazantsev²,

Makarov³

2023

Preprint

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The paper deals with the problem of controlling the state of industrial devices according to the readings of their sensors. The current methods rely on one approach to feature extraction in which the prediction occurs. We proposed a technique to build a scalable model that combines multiple different feature extractor blocks. A new model based on sequential sensor space analysis achieves state-of-the-art results on the C-MAPSS benchmark for equipment remaining useful life estimation. The resulting model performance was validated including the prediction changes with scaling.

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“…A forgetting mechanism considering a forgetting factor was used to adapt the hidden layer generalization with data changes. In [82], a new type of loss function was given to the ELM to provide better regression robustness when predicting the RUL of aircraft engines. Accordingly, and similarly to MLPs, ELM also has been used for direct RUL predictions.…”

Section: Elmmentioning

confidence: 99%

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

Berghout

Benbouzid

2022

Electronics

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Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system’s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects.

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Aeroengines Remaining Useful Life Prediction Based on Improved C-Loss ELM

Cited by 10 publications

References 28 publications

Intersection of domains: Designing a remote-controlled dish antenna

Intersection of domains: Designing a remote-controlled dish antenna

Interaction models for remaining useful life estimation

A Systematic Guide for Predicting Remaining Useful Life with Machine Learning

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