Real-time signal analysis on the TCV PHA diagnostic

2003 ◽  
Vol 74 (3) ◽  
pp. 2004-2006 ◽  
Author(s):  
T. I. Madeira ◽  
A. P. Rodrigues ◽  
C. A. F. Varandas ◽  
P. Amorim ◽  
B. P. Duval
Keyword(s):  
Real Time ◽  
Signal Analysis ◽  
Time Signal

scholarly journals Peak Detection Implementation for Real-Time Signal Analysis Based on FPGA

2018 ◽  
Vol 09 (10) ◽  
pp. 148-167 ◽  
Author(s):  
Alperen Mustafa Colak ◽  
Taito Manabe ◽  
Yuichiro Shibata ◽  
Fujio Kurokawa
Keyword(s):  
Real Time ◽  
Signal Analysis ◽  
Peak Detection ◽  
Time Signal

Online EMD-Wavelet Method for Flutter Test Excited by Atmospheric Turbulence

2019 ◽  
Vol 33 (07) ◽  
pp. 1950010
Author(s):  
Hua Zheng ◽  
Junhao Liu ◽  
Shiqiang Duan
Keyword(s):  
Real Time ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Wavelet Spectrum ◽  
Modal Identification ◽  
Time Signal ◽  
Measured Signal ◽  
Wavelet Method ◽  
Vibration Signal Analysis ◽  
Emd Method

Empirical Mode Decomposition (EMD) and Wavelet method have been widely used in the vibration signal analysis, mainly because of the powerful ability of blind modal separation. In the flutter test, modal identification and modal trajectories tracking online can help to ensure the test safety. However, traditional EMD method is only suitable for offline data batching, which limits its applications to the real-time or near real-time signal analysis. For this reason, according to the online processing requirements of flutter test under the turbulent excitation conditions, combining with wavelet spectrum analysis, an online EMD method based on a sliding window for structural modal identification and flutter boundary prediction (FBP) is proposed in this paper. The corresponding mathematical principles and implementation algorithms are given as well, which decompose the measured signal into the Intrinsic Mode Function (IMF) firstly, and then estimate the modal parameters by using the wavelet power spectrum method. The FBP result is typically obtained online during the test process by using curve fitting and extrapolation of the modal damping. The numerical simulations and test data from a low-speed wind tunnel flutter test are used to verify the effectiveness and engineering feasibility of the proposed method.

scholarly journals Anomaly Detection and Classification of Physiological Signals in IoT- Based Healthcare Framework

2021 ◽  
Author(s):  
Menaa Nawaz ◽  
Jameel Ahmed
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Real Time ◽  
Signal Analysis ◽  
Physiological Signals ◽  
Series Data ◽  
Time Signal ◽  
Biomedical Sensors

Abstract Physiological signals retrieve information from sensors implanted or attached to the human body. These signals are vital data sources that can assist in predicting the disease well before time; thus, proper treatment can be made possible. With the addition of the Internet of Things in healthcare, real-time data collection and preprocessing for signal analysis has reduced the burden of in-person appointments and decision making on healthcare. Recently, deep learning-based algorithms have been implemented by researchers for the recognition, realization and prediction of diseases by extracting and analyzing important features. In this research, real-time 1-D time series data of on-body noninvasive biomedical sensors were acquired, preprocessed and analysed for anomaly detection. Feature engineered parameters of large and diverse datasets have been used to train the data to make the anomaly detection system more reliable. For comprehensive real-time monitoring, the implemented system uses wavelet time scattering features for classification and a deep learning-based autoencoder for anomaly detection of time series signals to assist the clinical diagnosis of cardiovascular and muscular activity. In this research, an implementation of an IoT-based AI-edge healthcare framework using biomedical sensors was presented. This paper also aims to analyse cloud data acquired through biomedical sensors using signal analysis techniques for anomaly detection, and time series classification has been performed for disease prognosis in real time by implementing 24 AI-based techniques to find the most accurate technique for real-time raw signals. The deep learning-based LSTM method based on wavelet time scattering feature extraction has shown a classification test accuracy of 100%. Using wavelet time scattering feature extraction achieved 95% signal reduction to increase the real-time processing speed. In real-time signal anomaly detection, 98% accuracy is achieved using LSTM autoencoders. The average mean absolute error loss of 0.0072 for normal signals and 0.078 is achieved for anomalous signals.

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