scholarly journals Signal Transformations for Analysis of Supraharmonic EMI Caused by Switched-Mode Power Supplies

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2088
Author(s):  
Leonardo Sandrolini ◽  
Andrea Mariscotti
Keyword(s):  
Wavelet Packet ◽  
Low Frequency ◽  
Power Supplies ◽  
Frequency Resolution ◽  
Frequency Interval ◽  
Sampled Data ◽  
Mode Decomposition ◽  
Switched Mode Power Supplies ◽  
Filtering Effect ◽  
Conducted Emissions

Switched-Mode Power Supplies (SMPSs) are a relevant source of conducted emissions, in particular in the frequency interval of supraharmonics, between 2 kHz and 150 kHz. When using sampled data for assessment of compliance, methods other than Fourier analysis should be considered for better frequency resolution, compact signal energy decomposition and a shorter time support. This work investigates the application of the Wavelet Packet Transform (WPT) and the Empirical Mode Decomposition (EMD) to measured recordings of SMPS conducted emissions, featuring steep impulses and damped oscillations. The comparison shows a general accuracy of the amplitude estimate within the variability of data themselves, with very good performance of WPT in tracking on stationary components in the low frequency range at some kHz. WPT performance however may vary appreciably depending on the selected mother wavelet for which some examples are given. EMD and its Ensemble EMD implementation show a fairly good accuracy in representing the original signal with a very limited number of base functions with the capability of exploiting a filtering effect on the low-frequency components of the signal.

MECHANICAL WATCH SIGNATURE ANALYSIS BASED ON WAVELET DECOMPOSITION

2009 ◽  
Vol 07 (04) ◽  
pp. 491-512 ◽  
Author(s):  
QINGBO HE ◽  
RUXU DU
Keyword(s):  
Acoustic Signal ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Wide Frequency Range ◽  
Frequency Resolution ◽  
Signature Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Wavelet Techniques

The acoustic signal of mechanical watch is a distinct multi-component signal. It contains many frequency components corresponding to specific escapement motion sources with a very wide frequency range. Therefore, it is challenging for signature analysis of mechanical watch by the acoustic signal. This paper studies the time-frequency signatures of the mechanical watch based on wavelet decomposition. Two methods are proposed to improve the frequency resolution of traditional wavelet techniques by combining other beneficial techniques in the sense of decomposing specific mono- or independent components. The empirical mode decomposition (EMD) is presented to advance the wavelet packet decomposition (WPD) to decompose the mono-component signals. And the blind source separation (BSS) makes the redundancy of continuous wavelet transform (CWT) further gain good frequency resolution in the independent meaning. The decomposed signals by the two methods reveal the insight of mechanical watch movement and can contribute much simpler and clearer time-frequency signatures. Experimental results indicated the effectiveness of the two methods and the value of the time-frequency signatures in analyzing and diagnosing mechanical watch movements.

The Application of Fault Diagnosis Based on EMD and Real Modulation Zoom Method

2013 ◽  
Vol 274 ◽  
pp. 37-40
Author(s):  
Jing Zhong Xiang ◽  
Fu Peng Ge ◽  
Han Sun ◽  
Xian Jiang Shi
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Hilbert Transformation ◽  
Frequency Resolution ◽  
Mode Decomposition ◽  
Shift Frequency ◽  
Envelope Spectrum ◽  
High Resonant Frequency

It is first to make Empirical Mode Decomposition (EMD) and achieve Hilbert-Hung transform (HHT) of envelope spectrum after Hilbert transformation, which can resolve the cross fault existing in Hilbert transformation is utilized merely. However, it can’t improve the resolution of envelope spectrum, which doesn’t adapt the characteristic of high resonant frequency of rolling bearing and low fault frequency. The paper utilizing the principle of real modulation shift frequency moves the EMD decomposition components of high frequency section to low frequency section and makes analysis of envelope spectrum after Hilbert transformation, which resolves the problem of low frequency resolution of pure HHT.

scholarly journals Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Lu Ke
Keyword(s):  
High Frequency ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Monitoring Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Structure Monitoring ◽  
Energy Leakage ◽  
Frequency Components ◽  
Different Characteristics

High-frequency components are often discarded for data denoising when applying pure wavelet multiscale or empirical mode decomposition (EMD) based approaches. Instead, they may raise the problem of energy leakage in vibration signals. Hybrid EMD and wavelet packet (EMD-WP) is proposed to denoise Global Positioning System- (GPS-) based structure monitoring data. First, field observables are decomposed into a collection of intrinsic mode functions (IMFs) with different characteristics. Second, high-frequency IMFs are denoised using the wavelet packet; then the monitoring data are reconstructed using the denoised IMFs together with the remaining low-frequency IMFs. Our algorithm is demonstrated on a synthetic displacement response of a 3-story frame excited by El Centro earthquake along with a set of Gaussian random white noises on different levels added. We find that the hybrid method can effectively weaken the multipath effect with low frequency and can potentially extract vibration feature. However, false modals may still exist by the rest of the noise contained in the high-frequency IMFs and when the frequency of the noise is located in the same band as that of effective vibration. Finally, real GPS observables are implemented to evaluate the efficiency of EMD-WP method in mitigating low-frequency multipath.

Studies of filtering effect on fault diagnosis of spindle device in hoist

2021 ◽  
Vol 63 (6) ◽  
pp. 348-356
Author(s):  
Jun Gu ◽  
Yuxing Peng ◽  
Bobo Cao
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Correct Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Practical Significance ◽  
Support Vector ◽  
Mode Decomposition ◽  
Core Components ◽  
Filtering Effect

Spindle devices, which are among the core components of mine hoists, are typical rotor-bearing systems. Vibration-based fault diagnosis techniques are often used to help prevent mechanical failures of such systems. The fault vibration signals generally include pulse information reflecting fault type, independent vibration components caused by other non-faulty mechanical components, noise in the surrounding environment and so on. The reduction of noise in the vibration signal collected by the sensor is of practical significance for the correct diagnosis of subsequent rotating machinery faults. To solve this problem, a fault diagnosis method based on a smooth (SM) filtering algorithm combined with variational mode decomposition (VMD) and a support vector machine (SVM) is proposed. Wavelet transform (WT) and wavelet packet transform (WPT) methods are used to compare the noise reduction. The reliability and effectiveness of the method are verified by experiments on a hoist mechanical fault simulator. Experimental results show that the proposed method has high prediction accuracy and can provide a good practical reference for fault diagnosis of rotating machinery.

Transform Operator Pair Assisted Hilbert–Huang Transform for Signals With Instantaneous Frequency Intersections

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Yuxin Sun ◽  
Chungang Zhuang ◽  
Zhenhua Xiong
Keyword(s):  
A Priori ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Resolution ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Start Up ◽  
Instantaneous Frequencies

Due to low frequency resolution for closely spaced spectral components, i.e., the instantaneous frequencies (IFs) lie within an octave or even have intersections, the Hilbert–Huang transform (HHT) fails to separate such signals and consequently generates inaccurate time–frequency distribution (TFD). In this paper, a transform operator pair assisted HHT is proposed to improve the capability of the HHT to separate signals, especially those with IF intersections. The two operators of a pair are constructed to remove the chosen component that is clearly observed in the TFD of the signal, and then recover it from intrinsic mode functions (IMFs). With this approach, the components can be clearly separated and the intersections can also be identified in the TFD. Since a priori knowledge of the transform operator is usually not available in real applications, an iterative algorithm is presented to obtain a global transform operator. The effectiveness of the proposed algorithm is demonstrated by analysis of numerical signals and a real signal collected from a cracked rotor–bearing system during the start-up process. Moreover, the proposed approach is shown to be superior to the normalized Hilbert transform (NHT) as well as the ensemble empirical mode decomposition (EEMD).

A Novel Fault-location Method for HVDC Transmission Lines Based on Concentric Relaxation Principle and Wavelet Packet

2020 ◽  
Vol 13 (5) ◽  
pp. 705-716
Author(s):  
Congshan Li ◽  
Ping He ◽  
Feng Wang ◽  
Cunxiang Yang ◽  
Yukun Tao ◽  
...  
Keyword(s):  
Transmission Lines ◽  
Traveling Wave ◽  
Fault Location ◽  
Wave Attenuation ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Evaluation Process ◽  
Location Method ◽  
Hvdc Transmission ◽  
Instantaneous Energy

Background: A novel fault location method of HVDC transmission line based on a concentric relaxation principle is proposed in this paper. Methods: Due to the different position of fault, the instantaneous energy measured from rectifier and inverter are different, and the ratio k between them is the relationship to the fault location d. Through the analysis of amplitude-frequency characteristics, we found that the wave attenuation characteristic of low frequency in the traveling wave is stable, and the amplitude of energy is larger, so we get the instantaneous energy ratio by using the low-frequency data. By using the method of wavelet packet decomposition, the voltage traveling wave signal was decomposed. Results: Finally, calculate the value k. By using the data fitting, the relative function of k and d can be got, that is the fault location function. Conclusion: After an exhaustive evaluation process considering different fault locations, fault resistances, and noise on the unipolar DC transmission system, four-machine two-area AC/DC parallel system, and an actual complex grid, the method presented here showed a very accurate and robust behavior.

Sign in / Sign up

Export Citation Format

Share Document