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Long-Term Structural State Trend Forecasting Based on an FFT–Informer Model

التفاصيل البيبلوغرافية
العنوان: Long-Term Structural State Trend Forecasting Based on an FFT–Informer Model
المؤلفون: Jihao Ma, Jingpei Dan
المصدر: Applied Sciences, Vol 13, Iss 4, p 2553 (2023)
بيانات النشر: MDPI AG, 2023.
سنة النشر: 2023
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: structural health monitoring, time series forecasting, FFT, Informer model, FFT–Informer, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Machine learning has been widely applied in structural health monitoring. While most existing methods, which are limited to forecasting structural state evolution of large infrastructures. forecast the structural state in a step-by-step manner, extracting feature of structural state trends and the negative effects of data collection under abnormal conditions are big challenges. To address these issues, a long-term structural state trend forecasting method based on long sequence time-series forecasting (LSTF) with an improved Informer model integrated with Fast Fourier transform (FFT) is proposed, named the FFT–Informer model. In this method, by using FFT, structural state trend features are represented by extracting amplitude and phase of a certain period of data sequence. Structural state trend, a long sequence, can be forecasted in a one-forward operation by the Informer model that can achieve high inference speed and accuracy of prediction based on the Transformer model. Furthermore, a Hampel filter that filters the abnormal deviation of the data sequence is integrated into the Multi-head ProbSparse self-attention in the Informer model to improve forecasting accuracy by reducing the effect of abnormal data points. Experimental results on two classical data sets show that the FFT–Informer model achieves high and stable accuracy and outperforms the comparative models in forecasting accuracy. It indicates that this model can effectively forecast the long-term state trend change of a structure and is proposed to be applied to structural state trend forecasting and early damage warning.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/13/4/2553; https://doaj.org/toc/2076-3417
DOI: 10.3390/app13042553
URL الوصول: https://doaj.org/article/297d2914b7d84d258cdd0061a1926fab
رقم الأكسشن: edsdoj.297d2914b7d84d258cdd0061a1926fab
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app13042553