PLPose: An efficient framework for detecting power lines via key points-based pose estimation

Jaffari, Rabeea; Hashmani, Manzoor Ahmed; Reyes-Aldasoro, Constantino Carlos; Junejo, Aisha Zahid; Taib, Hasmi; Abdullah, M. Nasir B.

doi:10.1016/j.jksuci.2023.101615

Cited by 1 publication

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References 67 publications

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“…The reason for this controversy is the difficulty in sorting the AE signals caused by disbondment because other cathodic reaction products are also produced. ML is widely utilized by researchers and practitioners in various research areas, such as computer vision [ 15 , 16 ], speech recognition [ 17 , 18 ], mechanical engineering [ 19 , 20 ], civil engineering [ 21 , 22 ], and so on, with positive results. Similarly, a wide variety of research works have also utilized ML algorithms to classify the AE features generated from various monitoring events [ 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Failure Severity Prediction for Protective-Coating Disbondment via the Classification of Acoustic Emission Signals

Rahman

May

Jaffari

et al. 2023

Sensors

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Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals.

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