scholarly journals A Piezoelectric Sensor Signal Analysis Method for Identifying Persons Groups

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 733
Author(s):  
Hitoshi Ueno
Keyword(s):  
Frequency Spectrum ◽  
Signal Analysis ◽  
Cardiac Cycle ◽  
Vibration Signal ◽  
Piezoelectric Sensor ◽  
Analysis Method ◽  
Single Person ◽  
Discrimination Criterion ◽  
Privacy Issues ◽  
Signal Analysis Method

The is an increasing number of elderly single-person households causing lonely deaths and it is a social problem. We study a watching system for elderly families by laying the piezoelectric sensors inside the house. There are few privacy issues of this system because piezoelectric sensor detects only a person’s vibration signal. Furthermore, it has a benefit of sensing the ability for a bio-signal including the respiration cycle and cardiac cycle. We propose a method of identifying the person who is on the sensor by analyzing the frequency spectrum of the bio-signal. Multiple peaks of harmonics originating from the heartbeat appear in the graph of the frequency spectrum. We propose a method to identify people by using the peak shape as a discrimination criterion.

The Damped Eigenfrequency Analysis for an Elastic Disk Clamped by a Water-Filled Cylinder

2012 ◽  
Vol 490-495 ◽  
pp. 3742-3747
Author(s):  
Zhi Xi Yang
Keyword(s):  
Signal Analysis ◽  
Effective Means ◽  
Three Dimensional ◽  
Vibration Signal ◽  
Analysis Method ◽  
Obvious Effect ◽  
Coupled Numerical Model ◽  
Elastic Disk ◽  
Vibration Signal Analysis ◽  
Signal Analysis Method

A vibroacoustic testing model in laboratory for the damped eigenfrequencies and eigenmodes is introduced in this paper. The unsymmetric (u, p) variational formulas are implemented for three dimensional structures based on the elastodynamic displacement field u and the fluid acoustic pressure field p. The damping coefficients of materials seem to have no obvious effect on the coupled numerical model. Then the damped eigenfrequencies can alternately be obtained by vibration signal analysis method. The Fast Fourier Transform for the spectrum domain analysis illustrates an effective means to evaluate the damped eigenfrequencies.

Research on a vibration signal analysis method for motor bearing

Optik ◽  
2016 ◽  
Vol 127 (20) ◽  
pp. 10014-10023 ◽  
Author(s):  
Huimin Zhao ◽  
Wu Deng ◽  
Xinhua Yang ◽  
Xiumei Li
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Analysis Method ◽  
Vibration Signal Analysis ◽  
Signal Analysis Method ◽  
Motor Bearing

A Bearing Fault Diagnosis Using Wavelet Envelope Spectrum Based on Full Vector Spectrum Technology

2012 ◽  
Vol 190-191 ◽  
pp. 873-879 ◽  
Author(s):  
Xiao Yun Gong ◽  
Jie Han ◽  
Hong Chen ◽  
Wen Ping Lei
Keyword(s):  
Signal Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Analysis Method ◽  
Bearing Fault Diagnosis ◽  
Bearing Vibration ◽  
Vibration Signal Analysis ◽  
Envelope Spectrum ◽  
Signal Analysis Method ◽  
Full Vector

Wavelet envelope demodulation method can distinguish the fault information from complex bearing vibration signal. However, traditional signal analysis method, which is solely based on a single source data, is imperfect. In this paper, an approach to wavelet packet and envelope analysis based on full vector spectrum technology was proposed. Firstly, two different data from the same source were respectively decomposed and recomposed by wavelet packet transform. Then, in order to improve the accuracy of detecting fault, the recomposed signals were merged by using the full vector spectrum method. Compared to the traditional signal analysis method, the advantage of the new method is presented by showing their application to bearings. Finally, results from the bearing vibration signal analysis show that the new approach is more effective because of its inheritance and all-sided feature.

Research on Applied-Information Technology in Digital Speech Based on LMD Algorithm

2014 ◽  
Vol 1014 ◽  
pp. 447-451
Author(s):  
Dong Kang He ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Production Function ◽  
Signal Analysis ◽  
Training Model ◽  
Recognition System ◽  
Analysis Method ◽  
End Effects ◽  
Local Mean ◽  
Non Stationary Signal ◽  
Signal Analysis Method

As a new nonlinear and non-stationary signal analysis method,local mean decomposition (LMD) has a good adaptability. We decompose the original non-stationary acceleration vibration signals into several stationary production function (PF).But performing LMD will produce end effects which make results distorted. A hidden Markov model (HMM)-based speech recognition system for Chinese spell.After analyzing reasons for end effects of LMD in detail,a new method based on weighted matching similar waveform was proposed.Experiments in speech recognition to the production function as the training model, the more traditional identification method to identify higher rates. LMD is an effective method. It is feasible to extract the feature from speech signals with LMD.

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