Breathing Crack Detection Using Dynamic Equations and Measurement Data Regression and Filtering Techniques

2021 ◽  
Vol 5 (4) ◽  
pp. 359-372
Author(s):  
Z.C. Feng ◽  
Yi Shang
Keyword(s):  
Crack Detection ◽  
Measurement Data ◽  
Dynamic Equations ◽  
Breathing Crack ◽  
Data Regression ◽  
Filtering Techniques

Reference-Free Breathing Crack Identification of Beam-Like Structures Using an Enhanced Spatial Fourier Power Spectrum with Exponential Weighting Functions

2019 ◽  
Vol 19 (02) ◽  
pp. 1950017 ◽  
Author(s):  
J. Prawin ◽  
A. Rama Mohan Rao
Keyword(s):  
Fatigue Crack ◽  
Power Spectrum ◽  
Crack Detection ◽  
Crack Problem ◽  
Experimental Studies ◽  
Crack Identification ◽  
Successful Implementation ◽  
Breathing Crack ◽  
Fourier Power Spectrum ◽  
Exponential Weighting

Detection of incipient damage of structures at the earliest possible stage is desirable for successful implementation of any health monitoring system. In this paper, we focus on breathing crack problem and present a new reference-free algorithm for fatigue crack detection, localization, and characterization for beam-like structures. We use the spatial curvature of the Fourier power spectrum as a damage sensitive feature for fatigue crack identification. An exponential weighting function that takes into account nonlinear dynamic signatures, such as sub- and superharmonics, is proposed in the Fourier power spectrum in order to enrich the damage-sensitive features of the structure. Both numerical and experimental studies have been carried out to test and verify the proposed algorithm.

Noise filtering techniques for Lamb waves in structural health monitoring

2018 ◽  
Vol 14 (4) ◽  
pp. 676-694
Author(s):  
Ambuj Sharma ◽  
Sandeep Kumar ◽  
Amit Tyagi
Keyword(s):  
Crack Detection ◽  
Lamb Waves ◽  
Random Noise ◽  
Matched Filtering ◽  
Noise Filtering ◽  
Content Type ◽  
Time Frequency ◽  
Structural Health ◽  
Filtering Technique ◽  
Filtering Techniques

Purpose The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities. Design/methodology/approach Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal. Findings Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application. Practical implications The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage. Originality/value Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.

scholarly journals Effects of Crack on Vibration Characteristics of Mistuned Rotated Blades

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Hailong Xu ◽  
Zhongsheng Chen ◽  
Yongmin Yang ◽  
Limin Tao ◽  
Xuefeng Chen
Keyword(s):  
Natural Frequency ◽  
Vibration Amplitude ◽  
Crack Detection ◽  
Vibration Characteristics ◽  
Lumped Parameter Model ◽  
Crack Model ◽  
Breathing Crack ◽  
Vibration Localization ◽  
Lumped Parameter ◽  
Amplitude Increase

Rotated blades are key mechanical components in turbine and high cycle fatigues often induce blade cracks. Meanwhile, mistuning is inevitable in rotated blades, which often makes it much difficult to detect cracks. In order to solve this problem, it is important and necessary to study effects of crack on vibration characteristics of mistuned rotated blades (MRBs). Firstly, a lumped-parameter model is established based on coupled multiple blades, where mistuned stiffness with normal distribution is introduced. Next, a breathing crack model is adopted and eigenvalue analysis is used in coupled lumped-parameter model. Then, numerical analysis is done and effects of depths and positions of a crack on natural frequency, vibration amplitude, and vibration localization parameters are studied. The results show that a crack causes natural frequency decease and vibration amplitude increase of cracked blade. Bifurcations will occur due to a breathing crack. Furthermore, based on natural frequencies and vibration amplitudes, variational factors are defined to detect a crack in MRBs, which are validated by numerical simulations. Thus, the proposed method provides theoretical guidance for crack detection in MRBs.

Smart Rotating Machines for Condition Monitoring

2009 ◽  
Vol 413-414 ◽  
pp. 423-430
Author(s):  
Michael I. Friswell ◽  
Yong Yong He
Keyword(s):  
Crack Detection ◽  
Dynamic Behaviour ◽  
Normal Operation ◽  
Active Magnetic Bearings ◽  
Static Deflection ◽  
Breathing Crack ◽  
The Past ◽  
Rotating Machine ◽  
Run Up

The concept that changes in the dynamic behaviour of a rotor could be used for general fault detection and monitoring is well established. Current methods rely on the response of the machine to unbalance excitation during run-up, run-down or normal operation, and are mainly based on pattern recognition approaches. Of all machine faults, probably cracks in the rotor pose the greatest danger and research in crack detection has been ongoing for the past 30 years. For large unbalance forces the crack will remain permanently open and the rotor is then asymmetric, which can lead to stability problems. If the static deflection of the rotor due to gravity is large then the crack opens and closes due to the rotation of the shaft (a breathing crack), producing a parametrically excited dynamical system. Although monitoring the unbalance response of rotors is able to detect the presence of a crack, often the method is relatively insensitive, and the crack must be large before it can be robustly detected. Recently methods to enhance the quality of the information obtained from a machine have been attempted, by using additional excitation, for example from active magnetic bearings. This research is directed towards the concept of a smart rotating machine, where the machine is able to detect and diagnose faults and take action automatically, without any human intervention. This paper will consider progress to date in this area, with examples, and consider the prospects for future development.

Frequency Comparison Function Method for Real-Time Identification of Breathing Crack at Welding Joint

2020 ◽  
Vol 20 (13) ◽  
pp. 2041001
Author(s):  
Xin Wang ◽  
Nan Wu ◽  
Quan Wang
Keyword(s):  
Real Time ◽  
Crack Detection ◽  
Experimental Studies ◽  
High Sensitivity ◽  
Crack Identification ◽  
Comparison Function ◽  
Amplitude Curve ◽  
Breathing Crack ◽  
Welding Joint ◽  
Frequency Comparison

In this research, the frequency comparison function (FCF) method is proposed and studied to realize high-sensitive real-time crack identification at the welding joint area for a beam-type structure. This method is derived from the frequency response function (FRF). During FCF, we use the response signal collected from the designated point of the structure instead of the excitation. The standard deviation of the FCF amplitude curve is calculated to detect and evaluate the possible crack and its induced vibration perturbations afterward. Vibration responses are simulated in ANSYS by the use of the finite element analysis of a welded beam structure, and these signals are then analyzed with the FCF algorithm. It is concluded that FCF is an efficient method for breathing crack identification and can be easily applied under different excitation conditions, including harmonic and random ones. Meanwhile, FCF can be applied without any pre-processing algorithms such as filtering and smoothing. So, it can be used for real-time crack identification. By combining the FCF with the smart coating sensor composed of piezoelectric layers, the crack identification with high sensitivity is realized. The crack is detectable at its very early stage (starting from 3% of the beam thickness). Experimental studies under harmonic and random excitations are processed, and the results prove high sensitivity and feasibility of the proposed crack detection method.

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