Benchmark of Damage Localisation Algorithms Using Mode Shape Data

2005 ◽  
Vol 293-294 ◽  
pp. 305-312 ◽  
Author(s):  
Joseph Morlier ◽  
F. Bos ◽  
P. Castera
Keyword(s):  
Signal Processing ◽  
Fractal Dimension ◽  
Wavelet Transform ◽  
Mode Shape ◽  
Crack Detection ◽  
Singularity Detection ◽  
Local Complexity ◽  
Noise Measurements ◽  
Signal Processing Methods ◽  
Shape Data

This paper presents a comparative study of three enhanced signal processing methods to locate damage on mode shape data. The first method called curvature mode shape is used as a reference. The second tool uses wavelet transform and singularity detection theory to locate damage. Finally we introduce the windowed fractal dimension of a signal as a tool to easily measure the local complexity of a signal. Our benchmark aims at comparing the crack detection using optimal spatial sampling under different severity, beam boundary conditions (BCs) and added noise measurements.

Dynamic Analysis of Faulty Rotors through Signal Processing

2016 ◽  
Vol 852 ◽  
pp. 602-606
Author(s):  
Cherukuri Bhargav Sai ◽  
D. Mallikarjuna Reddy
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Dynamic Analysis ◽  
Mode Shape ◽  
Wavelet Coefficients ◽  
Nodal Displacement ◽  
Rotating Shafts ◽  
The Difference ◽  
Shape Data

In this study, an effective method based on wavelet transform, for identification of damage on rotating shafts is proposed. The nodal displacement data of damaged rotor is processed to obtain wavelet coefficients to detect, localise and quantify damage severity. Because the wavelet coefficients are calculated with various scaled indices, local disturbances in the mode shape data can be found out in the finer scales that are positioned at local disturbances. In the present work the displacement data are extracted from the MATLAB model at a particular speed. Damage is represented as reduction in diameter of the shaft. The difference vectors between damaged and undamaged shafts are used as input vectors for wavelet analysis. The measure of damage severity is estimated using a parameter formulated from the distribution of wavelet coefficients with respect to the scales. Diagnosis results for different damage cases such as single and multiple damages are presented.

scholarly journals Identification of Crack Location in Beam Structures Using Wavelet Transform and Fractal Dimension

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yong-Ying Jiang ◽  
Bing Li ◽  
Zhou-Suo Zhang ◽  
Xue-Feng Chen
Keyword(s):  
Fractal Dimension ◽  
Wavelet Transform ◽  
Mode Shape ◽  
Practical Importance ◽  
Estimation Method ◽  
Experimental Investigations ◽  
Complex Structures ◽  
Cracked Beam ◽  
Beam Structures ◽  
Crack Location

Identification of structural crack location has become an intensely investigated subject due to its practical importance. In this paper, a hybrid method is presented to detect crack locations using wavelet transform and fractal dimension (FD) for beam structures. Wavelet transform is employed to decompose the mode shape of the cracked beam. In many cases, small crack location cannot be identified from approximation signal and detailed signals. And FD estimation method is applied to calculate FD parameters of detailed signals. The crack locations will be detected accurately by FD singularity of the detailed signals. The effectiveness of the proposed method is validated by numerical simulations and experimental investigations for a cantilever beam. The results indicate that the proposed method is feasible and can been extended to more complex structures.

Phase-based spectrum analysis method for identifying weak harmonics

2018 ◽  
Vol 24 (23) ◽  
pp. 5585-5596 ◽  
Author(s):  
Jingsong Xie ◽  
Wei Cheng ◽  
Yanyang Zi ◽  
Mingquan Zhang
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Characteristic Frequency ◽  
Discrete Wavelet ◽  
Accurate Identification ◽  
Processing Methods ◽  
Characteristic Frequencies ◽  
Frequency Detection ◽  
Signal Processing Methods

Fault characteristic frequency extraction is an important means for the fault diagnosis of rotating machineries. Traditional signal processing methods commonly use the amplitude information of signals to detect damages. However, when the amplitudes of characteristic frequencies are weak, the recognition effects of traditional methods may be unsatisfactory. Therefore, this paper proposes the phase-based enhanced phase waterfall plot (EPWP) method and frequency equal ratio line (FERL) method for identifying weak harmonics. Taking a cracked rotor as an example, the characteristic frequency detection performances of the EPWP and FERL methods are compared with that of the traditional signal processing methods namely fast Fourier transform, short-time Fourier transform, discrete wavelet transform, continuous wavelet transform, ensemble empirical mode decomposition, and Hilbert–Huang transform. Research results demonstrate that the effects of EPWP and FERL for the recognitions of weak harmonics which are contained in steady signals and transient signals are better than that of the traditional signal processing methods. The accurate identification of weak characteristic frequencies in the vibration signals can provide an important reference for damage detections and improve the diagnostic accuracy.

Algorithm Study for Metal Magnetic Memory Signal Processing

2012 ◽  
Author(s):  
Zhanjun Feng ◽  
Weibin Wang ◽  
Hongjun Dong ◽  
Song Lin ◽  
Dianxue Wang ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Data ◽  
Experimental Result ◽  
Work Piece ◽  
Magnetic Memory ◽  
Metal Magnetic Memory ◽  
Destructive Testing ◽  
Singularity Detection ◽  
Signal Processing Methods ◽  
Non Destructive

Currently, Metal Magnetic Memory (MMM) non-destructive testing has been applied on trenchless inspection for buried pipeline. However, the problem of signal pattern recognition still exists. This paper introduces two signal processing methods to enhance the inspection accuracy of MMM: the signal segmented fluctuation and the signal segmented dissimilarity. Both of the two methods are designed for Signal singularity detection (SSD) of MMM signal. It is well known that the MMM signal of the stress concentrated area (SCA) is more fluctuant and dissimilar in contrast to those of non SCA, which is the basis of deriving the two algorithms. The two methods have the advantage in the detection of the metallic work piece which is non defective in looks but fatigued as well as may be helpful to detect the singularity of the signals such as negative pressure wave, ultrasonic wave etc. Experimental result with real data demonstrates the effectiveness of the proposed algorithms. Moreover, the MMM SSD software implementation is considered.

scholarly journals Time-Frequency Analysis of Compressor Rotating Stall by Means of Wavelet Transform

1996 ◽  
Author(s):  
Shengfang Liao ◽  
Jingyi Chen
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Analysis ◽  
Rotating Stall ◽  
Processing Methods ◽  
Stall Inception ◽  
Time Frequency ◽  
Signal Processing Methods ◽  
Conventional Signal

In this paper, an application of Wavelet Transform, which is a newly developed time-frequency technique of signal processing, is demonstrated in analyzing compressor rotating stall signals. In contrast to conventional signal processing methods, e.g. Fourier Transform, Wavelet Transform is very suitable for analyzing transient processes as rotating stall inception in compressors. In this study, some typical rotating stall signals are processed via Morlet’s wavelet. It is concluded that Wavelet Transform has a great advantage in detecting rotating stall inceptions, which are usually very weak and embedded in relatively stronger noises. In the diagrams resulted from the transform, every emergence of precursor as well as full stall signals of a certain frequency is illustrated versus time.

scholarly journals A Novel Method to Estimate the Damage Severity Using Spatial Wavelets and Local Regularity Algorithm

2020 ◽  
Vol 14 (54) ◽  
pp. 36-55
Author(s):  
Mallikarjuna Reddy ◽  
Arun Kumar K
Keyword(s):  
Wavelet Transform ◽  
Mode Shape ◽  
Continuous Wavelet Transform ◽  
Structural Damage ◽  
Damage Identification ◽  
Local Regularity ◽  
Beam Model ◽  
Continuous Wavelet ◽  
Novel Method ◽  
Shape Data

 In the process of structural damage detection using continuous wavelet transform (CWT), the perturbation or damage is located by identifying the defects locally in the input signal data.  In this work the damage identification procedure using continuous wavelet transform is developed. This method is studied numerically using a simple beam model. The influence of reduced spatial sampling using fundamental mode shape is investigated in detail. The method is also investigated to ascertain the smallest level of damage identified using strain energy mode shape data.

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