Magnetic Bearing Rotor Vibration Analysis Based on the Harmonic Wavelet Package Algorithm

2012 ◽  
Vol 588-589 ◽  
pp. 152-155
Author(s):  
De Guang Li ◽  
Shu Qin Liu
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Magnetic Bearing ◽  
Rotor Vibration ◽  
Time Frequency ◽  
Optimizing Design ◽  
Harmonic Wavelet ◽  
Frequency Map

Analysis of the magnetic bearing rotor vibration is the base of the optimizing design, supervise and diagnosis of the magnetic bearing. Harmonic wavelet package was used for the analysis of the vibration signal, and the 3 dimension time-frequency domain energy map was constructed, then the analysis of the rotor vibration became convenient. Via analysis of the time-frequency map, the vibration in each time and each frequency was obtained, and the supervise and the diagnosis of the rotor can be realized.

Mine Ventilator Fault Diagnosis Based on Harmonic Wavelet Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 613-617
Author(s):  
Shuang Xi Jing ◽  
Yong Chang ◽  
Jun Fa Leng
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Frequency Region ◽  
Analysis Method ◽  
Vibration Signals ◽  
Harmonic Wavelet ◽  
Frequency Components

Harmonic wavelet function, with the strict box-shaped characteristic of spectrum, has strong ability of identifying signal in frequency domain, and can extract weak components form vibration signals in frequency domain. Using harmonic wavelet analysis method, the selected frequency region and other frequency components of vibration signal of mine ventilator were decomposed into independent frequency bands without any over-lapping or leaking. Simulation and diagnosis example show that this method has good fault diagnosis effect, and the ventilator fault is diagnosed successfully.

A Portable Detecting and Analytic Instrument of Vibration Signal

2012 ◽  
Vol 442 ◽  
pp. 305-308
Author(s):  
Jian Wei Li ◽  
Ling Wang ◽  
Hong Mei Zhang
Keyword(s):  
Frequency Domain ◽  
Virtual Instrument ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Time Frequency ◽  
Notebook Computer ◽  
Usb Bus ◽  
Data Acquisition Card

It is often needed in engineering that detecting and analyzing vibration signal of some equipment. To meet the requirement, a portable detecting and analytic instrument was designed using virtual instrument concept. In the instrument, notebook computer was used as the platform of hardware. Vibration signal was obtained by integrated piezoelectric acceleration sensor (DTS0104T), and was transferred to a notebook computer through data acquisition card (NI USB-6210) based on USB bus. The software, running on the notebook computer, was developed under LabVIEW. Vibration signal could be displayed on screen, recorded in disk or printed by printer, retrieved, and analyzed. The analysis functions of the instrument include: time-domain analysis, frequency-domain analysis, time-frequency domain analysis, and correlation analysis. The instrument is compact, portable, powerful, and with friendly interfaces, has broad application prospects.

Gear fault diagnosis based on time–frequency domain de-noising using the generalized S transform

2017 ◽  
Vol 24 (15) ◽  
pp. 3338-3347 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Time Frequency ◽  
Transmission Modes ◽  
S Transform ◽  
Mining Machinery ◽  
Gear Fault ◽  
Generalized S Transform ◽  
Gear Fault Diagnosis

Gears are the most important transmission modes used in mining machinery, and gear faults can cause serious damage and even accidents. In the work process, vibration signals are influenced not only by friction, nonlinear stiffness, and nonstationary loads, but also by strong noise. It is difficult to separate the useful information from the noise, which brings some trouble to the fault diagnosis of mining machinery gears. The generalized S transform has the advantages of the short time Fourier transform and wavelet transform and is reversible. The time–frequency energy distribution of the gear vibration signal can be accurately presented by the generalized S transform, and a time–frequency filter factor can be constructed to filter the vibration signal in the time–frequency domain. These characteristics play an important role when the generalized S transform is used to remove the noise in the time–frequency domain. In this paper, a new gear fault diagnosis based on the time–frequency domain de-noising is proposed that uses the generalized S transform. The application principle, method steps, and evaluation index of the method are presented, and a wavelet soft-threshold filtering method is implemented for comparison with the proposed approach. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a gear with a tooth crack. Our analyses also indicate that the proposed method can be used for fault diagnosis of mining machinery gears.

A Harmonic Wavelets Approach for Extracting Transient Patterns From Measured Rotor Vibration Data

2001 ◽  
Author(s):  
Valeta Carol Chancey ◽  
George T. Flowers ◽  
Candice L. Howard
Keyword(s):  
Vibration Analysis ◽  
Transient Effects ◽  
Rotor Vibration ◽  
Harmonic Wavelets ◽  
Vibration Data ◽  
Harmonic Wavelet ◽  
Vibration Signature ◽  
The Fourier Transform ◽  
Traditional Approaches

Vibration analysis is a powerful diagnostic tool for rotating machinery problems. Traditional approaches to vibration signature analysis have focused on the Fourier transform, which tends to average out transient effects. Recent work in the area of wavelets has allowed for the characterization of signals in frequency and in time, which, if properly interpreted, can provide substantial insight, particularly with regard to transient behaviors. There are many different wavelets, but the harmonic wavelet was developed specifically for vibration analysis. It uses an algorithm based upon the FFT, which makes it particularly attractive to many in the vibration analysis community. This paper considers the harmonic wavelet as a tool for extracting transient patterns from measured vibration data. A method for characterizing transient behaviors using the harmonic wavelet is described and illustrated using simulation and experimental results.

scholarly journals Condition assessment of steel truss bridge using time frequency domain analysis

2016 ◽  
Vol 14 (2) ◽  
pp. 141-166
Author(s):  
Suresh Walia ◽  
Raj Patel ◽  
Hemant Vinayak ◽  
Raman Parti
Keyword(s):  
Frequency Domain ◽  
3D Visualization ◽  
Vibration Signal ◽  
Domain Analysis ◽  
Condition Assessment ◽  
Frequency Domain Analysis ◽  
Mode Shapes ◽  
Steel Bridge ◽  
Damage State ◽  
Time Frequency

In the present article the work is carried out on scaled modeled bridge for condition assessment due to seeded damage. The objective is to find the location of damage in the steel bridge using vibration signal. For the differentiation between damage and intact condition, time, frequency domain analysis has been used. Power spectral density has been applied to the vibration signal to extract the mode shapes and compare between healthy and damage state of the modeled. Further, Short Time Fourier Transform gives the 3D visualization of amplification in different mode of vibration which helps to identify the damage location. Using nodal energy approach, Wavelet Packet Transform has been used to determine the location of damage, which is superior than the frequency and time domain analysis parameters.

Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ronny Francis Ribeiro Junior ◽  
Isac Antônio dos Santos Areias ◽  
Guilherme Ferreira Gomes
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Electric Motors ◽  
Content Type ◽  
Operation Conditions ◽  
Vibration Pattern ◽  
Vibration Signal Analysis ◽  
Detection And Diagnosis

Purpose Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives. Design/methodology/approach This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed. Findings Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition. Originality/value The proposed methodology is verified through a real experimental setup.

Study on the Defects Size of Ball Bearings Elements Using Vibration Analysis

2014 ◽  
Vol 658 ◽  
pp. 289-294 ◽  
Author(s):  
Carmen Bujoreanu ◽  
Razvan Monoranu ◽  
N. Dumitru Olaru
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Ball Bearing ◽  
Fault Detection And Diagnosis ◽  
Response Measurement ◽  
Time Frequency ◽  
Bearing Life ◽  
Rolling Element ◽  
Detection And Diagnosis ◽  
The Time Domain

The vibration analysis aims to extract features from the measurements in order to be used for fault detection and diagnosis. Vibration response measurement is an important and effective technique for the detection of the defects in rolling element bearings. The corresponding analysis methods operate in the time domain, in the frequency domain and recently in the time-frequency domain. A quantitative determination of the defect severity and its development are useful to be determined in order to estimate the remaining useful ball bearing life. Experimental data from a bearing with a defect are collected by an accelerometer then processed to identify the passing time of a ball over a defect. The paper presents a computation model corroborated to an experimental investigation to establish the defect length of a ball bearing inner race.

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