Review of Vibration-Based Structural Health Monitoring Using Deep Learning

Toh, Gyungmin; Park, Junhong

doi:10.3390/app10051680

Cited by 140 publications

(68 citation statements)

References 115 publications

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“…DL models have proven successful in diverse applications such as image recognition, language understanding, and deoxyribonucleic acid (DNA) biological processes prediction [ 34 ]. However, applications of recent DL algorithms in civil engineering, including convolutional neural networks (CNNs) and RNNs have been more common in structural health monitoring and crack detection owing to the large data sets available in these fields [ 35 , 36 ]. CNNs and RNNs are among the most popular DL algorithms.…”

Section: Machine Learning Basismentioning

confidence: 99%

Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model

2020

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Recycled aggregate concrete (RAC) contributes to mitigating the depletion of natural aggregates, alleviating the carbon footprint of concrete construction, and averting the landfilling of colossal amounts of construction and demolition waste. However, complexities in the mixture optimization of RAC due to the variability of recycled aggregates and lack of accuracy in estimating its compressive strength require novel and sophisticated techniques. This paper aims at developing state-of-the-art machine learning models to predict the RAC compressive strength and optimize its mixture design. Results show that the developed models including Gaussian processes, deep learning, and gradient boosting regression achieved robust predictive performance, with the gradient boosting regression trees yielding highest prediction accuracy. Furthermore, a particle swarm optimization coupled with gradient boosting regression trees model was developed to optimize the mixture design of RAC for various compressive strength classes. The hybrid model achieved cost-saving RAC mixture designs with lower environmental footprint for different target compressive strength classes. The model could be further harvested to achieve sustainable concrete with optimal recycled aggregate content, least cost, and least environmental footprint.

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Section: Machine Learning Basismentioning

confidence: 99%

Mixture Optimization of Recycled Aggregate Concrete Using Hybrid Machine Learning Model

2020

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“…ere have been different reviews and surveys conducted by the researchers relating to condition monitoring of motors and application of DL in this field [11,[36][37][38][39][40]. However, each review has been conducted in different contexts.…”

Section: Introductionmentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

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Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

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“…Although the use of vibration for structural integrity monitoring is complex, its potential to be used for a wide variety of engineering fields is continuously increasing. The studies on the application of machine learning to the classification of vibration-based monitoring are being proposed by many research groups as summarized in the review [1]. To anticipate unexpected failure and estimate remaining life time with actual implementation, continuous vibration monitoring together with statistical feature extractions are important.…”

Section: Introductionmentioning

confidence: 99%