Real-time damage detection of bridges using adaptive time-frequency analysis and ANN
Authors
Abstract:
Although traditional signal-based structural health monitoring algorithms have been successfully employed for small structures, their application for large and complex bridges has been challenging due to non-stationary signal characteristics with a high level of noise. In this paper, a promising damage detection algorithm is proposed by incorporation of adaptive signal processing and Artificial Neural Network (ANN). First, three adaptive signal processing techniques including Empirical Mode Decomposition (EMD), Local Mean Decomposition (LMD) and Hilbert Vibration Decomposition (HVD) are compared. The efficacy of these methods is examined for several numerically simulated signals to find a reliable signal processing tool. Then, three signal features are compared to find the most sensitive feature to damage. In the next step, an ANN ensemble is utilized as a classifier. Traditional statistical features and energy indices are used as the network input and output to make real-time detection of damage possible. The strength of this approach lies with training the network only based on healthy state of the structure. Having a trained ANN, online processing can be made to find a possible damage. Results show that the proposed algorithm has a good capacity as an online output-only damage detection method.
similar resources
Real Time Knock Detection with Dft-based Time-frequency Analysis
New generations of spark ignition engines require a correct ignition adjustment to guarantee optimal performance. In this paper, we address the challenge of knock detection improvements with time-frequency analysis. Two real time knock detection methodologies based on the sliding Discrete Fourier Transform and the pseudo Wigner-Ville distribution are exposed. Their relative performances and rob...
full textDetecting and counting vehicles using adaptive background subtraction and morphological operators in real time systems
vehicle detection and classification of vehicles play an important role in decision making for the purpose of traffic control and management.this paper presents novel approach of automating detecting and counting vehicles for traffic monitoring through the usage of background subtraction and morphological operators. We present adaptive background subtraction that is compatible with weather and ...
full textPulmonary crackle detection using time-frequency and time-scale analysis
a r t i c l e i n f o a b s t r a c t Keywords: Lung sounds Crackle detection Time–frequency and time–scale analysis Dual-tree complex wavelet transform Denoising Ensemble methods Support vector machines Pulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders in auscultation. Crackles are very common adventitious transient sounds. From the characteristics of...
full textDetection of Fuel Adulteration in Real Time Using Optical Fiber Sensor and Peripheral Interface Controller
Detection of fuel adulteration in real time is the most important need of the world as it generates the hazardous effects on the environment, living and nonliving things. Various automotive users and fuel providers may mix kerosene in petrol and diesel. Adulterated fuel increases air pollution, as well as reduces the performance of vehicle engines. Such a malpractice in fuel is prevailing becau...
full textReal-Time Interference Detection in Tracking Loop of GPS Receiver
Global Positioning System (GPS) spoofing could pose a major threat for GPS navigation ‎systems, so the GPS users have to gain a better understanding of the broader implications of ‎GPS.‎ In this paper, a plenary anti-spoofing approach based on correlation is proposed to distinguish spoofing effects. The suggested ‎method can be easily implemented in tracking loop of GPS receiver...
full textMy Resources
Journal title
volume 4 issue 1
pages 49- 61
publication date 2019-09
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023