scholarly journals Output-Only Damage Detection of Steel Beam Using Moving Average Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hadi Kordestani ◽  
Yi-Qiang Xiang ◽  
Xiao-Wei Ye

This paper provides a simple and direct output-only baseline-free method to detect damage from the noisy acceleration data by using Moving Average Filter (MAF). MAF is a convolution approach based on a simple filter kernel (rectangular shape) that works as an averaging method to smooth signal and remove incorporated noise. In this paper, a method is proposed to employ MAF to smooth acceleration signals obtained from a series of accelerometers and determine the damage location along a steel beam. To verify the proposed method, a simply supported beam was modelled through a 3D numerical simulation and an experimental model under a moving vehicle load. The response acceleration data was then recorded at a sampling frequency of 500 Hz. Finally, damage location was identified by applying the proposed method. The results showed that the proposed method can accurately estimate the damage location from the acceleration signal without applying any frequency filtering or baseline correction.

2014 ◽  
Vol 116 ◽  
pp. 276-283 ◽  
Author(s):  
Naji Rajai Nasri Ama ◽  
Wilson Komatsu ◽  
Lourenco Matakas Junior

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3997 ◽  
Author(s):  
Tam Nguyen ◽  
Xiaoli Qin ◽  
Anh Dinh ◽  
Francis Bui

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.


2020 ◽  
Vol 206 ◽  
pp. 104354 ◽  
Author(s):  
Amir Ali Safaei Pirooz ◽  
Richard G.J. Flay ◽  
Lorenzo Minola ◽  
Cesar Azorin-Molina ◽  
Deliang Chen

Sign in / Sign up

Export Citation Format

Share Document