کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4924410 | 1430845 | 2017 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
ترجمه فارسی عنوان
تشخیص آسیب ساختاری مبتنی بر ارتعاش در زمان واقعی با استفاده از شبکه عصبی کانولوشن یک بعدی
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کلمات کلیدی
لرزش، نظارت بر سلامت سازمانی، تشخیص آلودگی سازه، شبکه های عصبی، شبکه های عصبی انعقادی،
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی عمران و سازه
چکیده انگلیسی
Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Sound and Vibration - Volume 388, 3 February 2017, Pages 154-170
Journal: Journal of Sound and Vibration - Volume 388, 3 February 2017, Pages 154-170
نویسندگان
Osama Abdeljaber, Onur Avci, Serkan Kiranyaz, Moncef Gabbouj, Daniel J. Inman,