scholarly journals Damped CAPES 2D Spectral Estimation for Real-Valued Vibration Signals

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Danhui Dan ◽  
Jiongxin Gong ◽  
Yiming Zhao
Keyword(s):  
Spectral Estimation ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
Angular Frequency ◽  
Frequency Resolution ◽  
Spectrum Estimation ◽  
Vibration Signals ◽  
Vibration Data ◽  
Decay Factor ◽  
Sine Term

We propose a 2D representation in the frequency-decay factor plane of an arbitrary real-world vibration signal. The signal is expressed as the sum of a decayed-attenuation sine term modulated by an amplitude function and a noise residue. We extend the combined approach of Capon estimation and amplitude and phase estimation (CAPES) to damped real vibration signals (DR-CAPES). In the proposed DR-CAPES method, the high-resolution amplitude and phase are estimated simultaneously for both angular frequency and decay factor grids. The performance of the proposed approach is tested numerically with noisy vibration data. Results show that the DR-CAPES method has an excellent frequency resolution, which helps to overcome difficulties in spectrum estimation when vibration modes are very close, and a small bias, which makes it suitable for obtaining accurate amplitude spectrums. The results also indicate that the proposed method can accurately estimate the amplitude spectrum with the use of averaging and denoising processes.

Detection of Transients in Vibration Signals using Reverse Dispersion Entropy

2021 ◽  
pp. 1-33
Author(s):  
Jaafar Alsalaet
Keyword(s):  
Shannon Entropy ◽  
Vibration Signal ◽  
Fir Filter ◽  
Frequency Resolution ◽  
Frequency Bandwidth ◽  
Vibration Signals ◽  
Spectral Kurtosis ◽  
Ideal Filter ◽  
Envelope Signal ◽  
The Ideal

Abstract In this work, the reverse dispersion entropy (RDE) is used to process the squared envelope signal in order to detect nonstationarites. Based on the idea of spectral kurtosis (SK) and kurtogram, the squared envelope signal is first extracted by applying STFT to vibration signal. Then, as an alternative to negative Shannon entropy, the RDE is used to process the squared envelope to detect the range of frequencies at which the transients occur. The RDEgram color-coded map is used to represent the RDE values as a function of frequency and frequency resolution from which the ideal filter parameters can be inferred. Once, the best frequency and frequency bandwidth pair are found, an optimum FIR filter can be designed to filter the original vibration signal. The proposed method is tested against simulated and actual vibration signals and proved to be superior to existing methods.

Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform

2018 ◽  
Vol 17 (02) ◽  
pp. 1850012 ◽  
Author(s):  
F. Sabbaghian-Bidgoli ◽  
J. Poshtan
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
External Noise ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Hilbert Huang Transform

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

Digital Analysis of the Vibration Signals Amplitude Spectrum Based on Fourier Processing of the Binary-Sign Analog-Stochastic Quantization Result

2019 ◽  
Vol 20 (12) ◽  
pp. 723-731
Author(s):  
V. N. Yakimov ◽  
V. I. Batyschev ◽  
A. V. Mashkov
Keyword(s):  
Harmonic Analysis ◽  
Computational Efficiency ◽  
Amplitude Spectrum ◽  
Experimental Studies ◽  
Analytical Calculation ◽  
Frequency Resolution ◽  
Stochastic Quantization ◽  
Complex Signals ◽  
Workload Analysis ◽  
Vibration Signals

The article is devoted to the problem of developing a digital algorithm for operational harmonic analysis of complex vibration signals. The basis for solving this problem was the generalized equation of statistical measurements, which defines the measurement procedure as the sequential execution of interrelated measurement and computational transformations. During the development of the algorithm, special attention is paid to analog-to-digital conversion because it directly affects the computational efficiency of digital procedures for obtaining the final result. As such a conversion, the use of binarysign analog-stochastic quantization is justified, which allows performing two-level quantization without systematic error regardless of the statistical properties of the analyzed signals. The discrete-event model of the binary-sign analog-stochastic quantization result allowed for the analytical calculation of integration operations in the transition to estimating the amplitude spectrum in digital form. As a result, the developed algorithm of harmonic analysis does not require performing digital multiplication operations typical for classical algorithms, which are based on the calculation of the direct discrete Fourier transform. The execution of the algorithm is reduced to the implementation of the addition and subtraction arithmetic operations of the cosine-function values in the time moments determined by the result of the binary-sign analogue-stochastic quantization. The exclusion of digital multiplication operations provided an increase in the computational efficiency of amplitude spectrum estimation. Laboratory studies of the developed algorithm were carried out using simulation modeling. The simulation results showed that the algorithm allows calculating estimates of the amplitude spectrum of complex signals with high accuracy and frequency resolution in the presence of additive noise. In real conditions, the testing of the developed algorithm was carried out during bench studies of the operational status of the MAZ-206067 bus, designed for the transportation of passengers on urban and suburban routes of average workload. Analysis of the results of experimental studies confirmed the possibility of using the algorithm as part of the diagnosability provision for operational monitoring of vibration signals in a complex noise environment.

UM2000 Spectrum Estimation Using Multiple Signal Classification Method

2013 ◽  
Vol 734-737 ◽  
pp. 2622-2629
Author(s):  
Shen Liu ◽  
Fu Ping Wang ◽  
Xiu Cheng Liu
Keyword(s):  
Minimum Description Length ◽  
Spectral Estimation ◽  
Frequency Discrimination ◽  
Frequency Resolution ◽  
Estimation Methods ◽  
Signal Spectrum ◽  
Spectrum Estimation ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Signal Simulation

This paper focused on UM2000 signal spectrum estimation using MUSIC algorithm. Because of the limitation of data window length, traditional frequency discrimination methods fail to meet the requirement of high frequency resolution. In this paper, the influence of SNR on MUSIC spectrum estimation is analyzed and MDL (minimum description length) principle is used to determine the dimension of the signal. Simulation results based on several other modern spectral estimation methods are also presented and compared with that of MUSIC method, from which the superiority of MUSIC method is verified.

Research on Identification Method of Multi-Mixed Vibration Signal of Aeroengine

2009 ◽  
Vol 419-420 ◽  
pp. 801-804
Author(s):  
Xiang Yang Jin ◽  
Shi Sheng Zhong
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signal ◽  
Signal Separation ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Vibration Data ◽  
Monitoring And Diagnosis ◽  
Rotating Shafts

The effectiveness of separation and identification of mechanical signals vibrations is crucial to successful fault diagnosis in the condition monitoring and diagnosis of complex machines.Aeroengine vibration signals always include many complicated components, blind source separation (BSS) provides a efficient way to separate the independent component.In order to get the most effective algorithm of vibration signal separation,experiment has been done to acquire plenty of multi-mixed rotor vibration signals,three sets of vibration data generated from aeroengine rotating shafts were separated from the synthetic vibration signal. The results prove that blind source separation is effective and can be applied for vibration signal processing and fault diagnosis of aeroengine.

scholarly journals Study on the Method for Collecting Vibration Signals from Mill Shell Based on Measuring the Fill Level of Ball Mill

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Peng Huang ◽  
Minping Jia ◽  
Binglin Zhong
Keyword(s):  
High Sensitivity ◽  
Vibration Signal ◽  
Experimental Result ◽  
Vibration Signals ◽  
Vibration Data ◽  
Vibration Sensors ◽  
Coal Powder ◽  
Fill Level ◽  
Steel Balls ◽  
The Impact

At present the method for measuring the fill level which used the vibration signal of mill shell shows its advantage compared with other methods. However, this method is developed late, and the technique for collecting the vibration signal from mill shell is immature. In this paper, a novel method for collecting the vibration data from mill shell is proposed. Firstly, the layout scheme of vibration sensors on mill shell is given by analyzing the axial and circumferential movement of coal powder in roller. And a special data acquisition system is developed, which can acquire vibration data from different axial and circumferential positions on mill shell. Then the sampling frequency is obtained based on impact model and hierarchical model of steel balls. At the same time, the impact region on mill shell caused by steel balls is considered as the collecting region of vibration signals. Experimental result shows that vibration signals collected by the method proposed in this paper present a high sensitivity to the changes on fill level compared with vibration data of mill bearing, which provides a reliable basis for accurate measurement of the fill level.

Gearbox Fault Diagnosis Using Vibration Signal with Wavelet De-Noising

2011 ◽  
Vol 86 ◽  
pp. 735-738
Author(s):  
Zhi Feng Dong ◽  
Hui Cheng ◽  
Hui Jia Yang ◽  
Wei Fu ◽  
Ji Wei Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Frequency Domain Analysis ◽  
Sensor Data ◽  
Spectrum Estimation ◽  
Vibration Signals ◽  
Fault Features ◽  
Vibration Signal Analysis ◽  
Simulation Signal ◽  
The Time Domain

This paper dealt with the gearbox fault diagnosis with vibration signal analysis. The vibration signals from experiment contained a lot of noises which result from motor, gears, bears and box, and were collected through accelerate sensor, data collector and computer. The wavelet de-noising stratification was used to de-noise the vibration signals before the frequency-domain analysis was done. The effects of the simulation signal de-noising was contrasted, and the noise cancellation the power spectrum estimation was carried out. The experimental and analytical results show that the different features are indicated with vibration signal of the normal gearbox and the signal with bolts loosened of the gearbox. The gearbox fault with bolts loosened can be diagnosed by extracting the time-domain fault features of vibration signals.

Use of Neural Networks in Road Recognition by Vibration Data

2010 ◽  
Author(s):  
Giacomo Reggiani ◽  
Marco Cocconcelli ◽  
Riccardo Rubini ◽  
Francesco Lolli
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Sensitivity Analysis ◽  
Vibration Signal ◽  
Accelerometer Sensor ◽  
Vibration Signals ◽  
Vibration Data ◽  
The Neural Network ◽  
Different Types ◽  
Control Set

This paper deals with road recognition by the use of neural networks on vibration signals. In particular, an accelerometer sensor is used and it acquires the vibrations transmitted in the contact between the tyres and the ground. Twelve different types of road have been tested at different speed of the car. Based on these data a neural network has been proposed to correlate a set of suitable characteristics of the vibration signal with the type of road. The effectiveness of the resulting neural network has been proved on a control set of data. Moreover the paper reports a sensitivity analysis of the neural network in order to minimize the number of inputs needed and to make it rugged.

scholarly journals Prediction and analysis of bearing vibration signal with a novel gray combination model

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091924
Author(s):  
Qiang Yuan ◽  
Yu Sun ◽  
Rui-ping Zhou ◽  
Xiao-fei Wen ◽  
Liang-xiong Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Model ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Data Fitting ◽  
Layer By Layer ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Vibration Data ◽  
Bearing Vibration

Bearings are the core components of ship propulsion shafting, and effective prediction of their working condition is crucial for reliable operation of the shaft system. Shafting vibration signals can accurately represent the running condition of bearings. Therefore, in this article, we propose a new model that can reliably predict the vibration signal of bearings. The proposed method is a combination of a fuzzy-modified Markov model with gray error based on particle swarm optimization (PGFM (1,1)). First, particle swarm optimization was used to optimize and analyze the three related parameters in the gray model (GM (1,1)) that affect the data fitting accuracy, to improve the data fitting ability of GM (1,1) and form a GM (1,1) based on particle swarm optimization, which is called PGM (1,1). Second, considering that the influence of historical relative errors generated by data fitting on subsequent data prediction cannot be expressed quantitatively, the fuzzy mathematical theory was introduced to make fuzzy corrections to the historical errors. Finally, a Markov model is combined to predict the next development state of bearing vibration signals and form the PGFM (1,1). In this study, the traditional predictions of GM (1,1), PGM (1,1), and newly proposed PGFM (1,1) are carried out on the same set of bearing vibration data, to make up for the defects of the original model layer by layer and form a set of perfect forecast system models. The results show that the predictions of PGM (1,1) and PGFM (1,1) are more accurate and reliable than the original GM (1,1). Hence, they can be helpful in the design of practical engineering equipment.

Damping Estimation by 2D dr_APES and its Application to a Real Cable-Stayed Bridge

2016 ◽  
Vol 16 (05) ◽  
pp. 1550002 ◽  
Author(s):  
Dan-Hui Dan ◽  
Jiong-Xin Gong ◽  
Li-Min Sun ◽  
Wei Cheng
Keyword(s):  
Amplitude Spectrum ◽  
Flexible Structures ◽  
Three Dimensional ◽  
Dynamic Test ◽  
Vibration Signal ◽  
Damping Factor ◽  
Engineering Structures ◽  
Vibration Signals ◽  
Cable Stayed Bridge ◽  
Identification Approach

Introduced herein is a new structural damping identification approach based on a two-dimensional (2D) amplitude and phase estimation (APES) method. The original APES method is suitable for application to an undamped and complex harmonic vibration signal. Hence, it has to be modified for application to real damped vibration signals such as the vibration test signals in engineering structures. This modified approach will be named as dr_APES. It can transform one-dimensional (1D) signals in time domain into their corresponding signals in the 2D domain of frequency and damping factor. By applying this dr_APES approach, the three-dimensional (3D) amplitude spectrum, with peaks corresponding to the vibration modes, can be obtained for any given vibration signals. By accurately locating the coordinates of the peaks, modal frequencies and damping factors can be identified. Owing to the high-resolution of the location of 2D ordinates of the spectral line and the value of spectrum, the accurate location of peaks can be estimated, and therefore, the modal frequencies and damping factor can be accurately determined. This is demonstrated by a numerical case study. Moreover, by applying the proposed approach to a real onsite dynamic test on two cables in a cable-stayed bridge, the inherent damping of the two cables was identified accurately, thereby verifying the ability of proposed damping identification approach in meeting the requirement of weak damping characteristics identification in flexible structures such as naked cables.

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