Wavelets base Automatic Extraction Technology for Vibration Signal Characteristics of Hybrid Electric Vehicle Engine

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Dongfang Song ◽  
Guanfei Yin
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Domain Analysis ◽  
Extraction Technology ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Signal Characteristics

Traditional automatic characteristic extraction technology of engine vibration signals for hybrid electric vehicles (HEV) only focuses on the analysis of engine vibration signals in time domain and frequency domain. Single time domain analysis or single frequency domain analysis cannot accurately analyse the vibration signals, while both time domain analysis and frequency domain analysis have cross-analysis. As a result, the analysis results are repetitive and conflicting, which makes it difficult to extract the characteristics of engine vibration signals. The final extraction accuracy is not high and the extraction efficiency is low. For this reason, an automatic characteristic extraction technology of HEV engine vibration signal based on wavelet packet energy analysis is proposed. Firstly, the mechanical vibration of engine is converted into corresponding voltage and current signals by various sensors and then converted into digital signals by A/D (analog/digital) conditioner. The data of vibration signals are often mixed with various noises, which have a great impact on the final analysis of vibration signals. Data interception and pre-filtering are adopted. Wave, zero-mean, elimination of trend term and elimination of staggered points are used to pre-treat the vibration signals with mixed noise. Short-Time Fourier Transform (STFT) algorithm is introduced to analyse the pre-processed engine vibration signals and the fundamental properties of the non-stationary vibration signals in actual operation of the engine are obtained. The energy distribution of the analysed engine vibration signal is calculated by the wavelet packet energy analysis method. The calculated parameters of the energy distribution of the wavelet packet are taken as the characteristic parameters of the vibration signal. The vibration signal characteristics of the engine are automatically extracted. The experiment is carried out in the form of comparison with the traditional method. The experimental results show that the time-frequency joint analysis applied in the proposed technology can accurately analyse the essential characteristics of the engine vibration signal of HEV. The wavelet packet energy analysis method can ensure the extraction accuracy of the engine vibration signal characteristics.

Time-Frequency Characteristics of Vibration Signals Analysis for Large Instantaneous Impact Mill Rolling Based on Wavelet Packet

2013 ◽  
Vol 834-836 ◽  
pp. 1061-1064
Author(s):  
Qi Jun Xiao ◽  
Zhong Hui Luo
Keyword(s):  
Time Domain ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Signal Wave ◽  
Reconstruction Technique ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Impact Vibration ◽  
Signal Time

The wavelet packet decomposition and reconstruction technique is applied to time-frequency analysis of bite steel impact vibration signal by big rolling machine, it is obtained the bite steel impact signal wave packet. According to the size of the wavelet packet energy, it is reconstructed the signal of No.1 and No.2 wavelet packet. According to reconstruction of the signal time domain waveform and FFT spectrum chart, some meaningful conclusions are obtained.

scholarly journals Analysis of Three-Phase Wye-Delta Connected LLC

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3606
Author(s):  
Jing-Yuan Lin ◽  
Chuan-Ting Chen ◽  
Kuan-Hung Chen ◽  
Yi-Feng Lin
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Operation Time ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Complete Analysis ◽  
Plane Analysis ◽  
Analysis Methods ◽  
Three Phase

Three-phase wye–delta LLC topology is suitable for voltage step down and high output current, and has been used in the industry for some time, e.g., for server power and EV charger. However, no comprehensive circuit analysis has been performed for three-phase wye–delta LLC. This paper provides complete analysis methods for three-phase wye–delta LLC. The analysis methods include circuit operation, time domain analysis, frequency domain analysis, and state–plane analysis. Circuit operation helps determine the circuit composition and operation sequence. Time domain analysis helps understand the detail operation, equivalent circuit model, and circuit equation. Frequency domain analysis helps obtain the curve of the transfer function and assists in circuit design. State–plane analysis is used for optimal trajectory control (OTC). These analyses not only can calculate the voltage/current stress, but can also help design three-phase wye-delta connected LLC and provide the OTC control reference. In addition, this paper uses PSIM simulation to verify the correctness of analysis. At the end, a 5-kW three-phase wye–delta LLC prototype is realized. The specification of the prototype is a DC input voltage of 380 V and output voltage/current of 48 V/105 A. The peak efficiency is 96.57%.

IDENTIFICATION OF INTERHARMONICS BASED ON WAVELET PACKET TRANSFORM

2021 ◽  
Vol 3 (1) ◽  
pp. 031-036
Author(s):  
S. A. GOROVOY ◽  
◽  
V. I. SKOROKHODOV ◽  
D. I. PLOTNIKOV ◽  
◽  
...  
Keyword(s):  
Fourier Transform ◽  
Simulation Model ◽  
Frequency Domain ◽  
Time Domain ◽  
Wavelet Packet ◽  
High Accuracy ◽  
Wavelet Packet Transform ◽  
Mathematical Apparatus ◽  
Nonlinear Load ◽  
The Time Domain

This paper deals with the analysis of interharmonics, which are due to the presence of a nonlinear load. The tool for the analysis was a mathematical apparatus - wavelet packet transform. Which has a number of advantages over the traditional Fourier transform. A simulation model was developed in Simulink to simulate a non-stationary non-sinusoidal mode. The use of the wavelet packet transform will allow to determine the mode parameters with high accuracy from the obtained wavelet coefficients. It also makes it possible to obtain information, both in the frequency domain of the signal and in the time domain.

Drilling Riser VIV Analysis Using Time Domain Code SimVIV

2013 ◽  
Author(s):  
Djoni E. Sidarta
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Ocean Currents

Drilling risers are often subjected to VIV from ocean currents, which may vary in directions over depth. VIV of drilling riser has commonly been analyzed using frequency domain code. This paper presents an alternative tool of analyzing VIV of drilling riser using time domain code SimVIV. With this tool it is possible to apply currents in varying directions over depth. Measured currents and VIV responses of a drilling riser available in the literature are used in this study. The results of time domain analysis using SimVIV are compared against measured responses. The effect of current directionality over depth on drilling riser VIV response is also analyzed.

scholarly journals Optimal implementation of frequency domain impedances in time domain simulations of building structures on embedded foundations

2020 ◽  
Author(s):  
Danilo S. Kusanovic ◽  
Elnaz E. Seylabi ◽  
Domniki Asimaki
Keyword(s):  
Finite Element ◽  
Frequency Domain ◽  
Time Domain ◽  
Response Spectrum ◽  
Free Field ◽  
Domain Analysis ◽  
Shallow Foundation ◽  
Rigid Base ◽  
Dimensionless Frequency ◽  
Building Structures

Soil-Structure Interaction (SSI) have been studied the last decades, and proper analysis for the linear elastic case in frequency domain has been established successfully. However, SSI is rarely considered in the seismic design of building structures. Regardless of its importance as a significant source of flexibility and energy dissipation, buildings are analyzed using a rigid base assumption, and the design is based on a response spectrum analysis, for which not only the soil, but also time are totally ignored. In a first attempt to improve and to incentivize time domain analyzes compatible with standard finite element packages for the engineering community, the state-of-practice introduces two major simplifications to transform the frequency domain analysis into a time domain analysis: (a) it assumes the frequency at which the impedance value should be read is the flexible-base frequency, and (b) it also assumes that the foundation input motion preserves the phase of the free field motion. Upon these simplifications, the following questions may arise: How does NIST recommendations perform in overall against a full finite element model? Are the embedment effects for shallow foundation not important so that the phase angle can be neglected? What is the best dimensionless frequency to estimate the soil impedance? Is it possible to make a better estimation of the dimensionless frequency to increase the NIST accuracy? In this study, we attempt to address these questions by using an inverse problem formulation.

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

scholarly journals Comparison of tibialis anterior and gastrocnemius muscles activation on balance training devices and hoverboard

2017 ◽  
Vol 13 (4-2) ◽  
pp. 495-500 ◽  
Author(s):  
Khairiyah Abdul Rahman ◽  
Aizreena Azaman ◽  
Hadafi Fitri Mohd Latip ◽  
Mohd Azuwan Mat Dzahir ◽  
Malarvili Balakrishnan
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Tibialis Anterior ◽  
Gastrocnemius Muscle ◽  
Balance Training ◽  
Domain Analysis ◽  
Median Frequency ◽  
Training Device ◽  
Emg Signal ◽  
Significant Difference

Balance training devices such as wobble board, basu ball and balance cushion are the tool use in balance training exercise programme in order to improve muscle strength and restore posture balance due degeneration of body function or injury. Recently, self-balancing scooter such as Segway and hover board showed a positive effects on rehabilitation. However, it is less known how these devices affect muscle physiological properties. This study aims to to measure ankle muscles activation on  difference balance training devices and hover board. Besides, a comparison between these device will be done in order to identify if hover board has a promising feature to be an alternative balance training device. In this research, surface EMG (sEMG) was used to record tibialis anterior and gastrocnemius muscle activities. Seventeen healthy subjects were required to stand on three different types of balance training device such as wobble board, balance cushion, bosu ball and a hover board. They were asked to maintain their standing position on each devices for two minutes. Both time domain and frequency domain analysis were used to identify the features of the EMG signal. Time domain analysis measurement involved average rectified value (ARV) and root mean square (RMS), meanwhile for frequency domain, median frequency (MDF) of the signal were measured. The results shows that, the RMS is differed significantly between the balance training devices (p<0.05) for tibialis anterior muscle but not gastrocnemius muscle. Meanwhile, no significant difference between the devices in the ARV and the MDF value (p>0.05). Besides, less stable devices increased muscle activity were observed. There is not much difference between hover board and the other devices in term of physiological effects of both tibialis anterior and gastrochemious muscle. It is also suggested that hover board offers a promising feature to be an alternative device for balance training device.

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