Gear Crack Detection With the Wavelet Transform

Volume 1C: 16th Biennial Conference on Mechanical Vibration and Noise ◽

10.1115/detc97/vib-4009 ◽

1997 ◽

Author(s):

D. Boulahbal ◽

M. F. Golnaraghi ◽

F. Ismail

Keyword(s):

Wavelet Transform ◽

Crack Detection ◽

Fatigue Cracks ◽

Vibration Signal ◽

Frequency Component ◽

Time Frequency ◽

Tooth Width ◽

Ideal Tool ◽

Short Time ◽

Gear Crack

Abstract The wavelet transform has the ability to extract global information as well as localized small features from a given signal. This property makes it very well suited to the study of time-varying vibration signals generated by the operation of faulty gears. For a healthy and properly designed gear set, the vibration signal consists mainly of the gear meshing frequency component and its harmonics. Developing fatigue cracks introduce short-time transients that modulate both the amplitude and phase of the otherwise steady vibration signal. These transients are often difficult to detect with the traditional time-only or frequency-only techniques. Being a joint time-frequency distribution, the Wavelet transform allows one to look at the evolution in time of a signal’s frequency content. It thus appears to be the ideal tool to detect the localized transients. In this study, we use both the amplitude and phase maps of the wavelet transform to assess the condition of an instrumented gear test rig. With the proposed technique, simulated cracks as small as 20% of the tooth width at the root are easily detectable.

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Fatigue crack detection method using analysis of vibration signal

Technical Sciences ◽

10.31648/ts.2909 ◽

2017 ◽

Vol 1 (20) ◽

pp. 63-74 ◽

Cited By ~ 2

Author(s):

Arkadiusz Rychlik ◽

Krzysztof Ligier

Keyword(s):

Crack Detection ◽

Fatigue Cracks ◽

Fatigue Cracking ◽

Vibration Signal ◽

Technical Condition ◽

Visual Methods ◽

Signal Characteristics ◽

Fatigue Crack Detection ◽

Sample Structure ◽

Change Identification

This paper discusses the method used to identify the process involving fatigue cracking of samples on the basis of selected vibration signal characteristics. Acceleration of vibrations has been chosen as a diagnostic signal in the analysis of sample cross section. Signal characteristics in form of change in vibration amplitudes and corresponding changes in FFT spectrum have been indicated for the acceleration. The tests were performed on a designed setup, where destruction process was caused by the force of inertia of the sample. Based on the conducted tests, it was found that the demonstrated sample structure change identification method may be applied to identify the technical condition of the structure in the aspect of loss of its continuity and its properties (e.g.: mechanical and fatigue cracks). The vibration analysis results have been verified by penetration and visual methods, using a scanning electron microscope.

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A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery

Scientific Reports ◽

10.1038/s41598-021-96343-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kai Wei ◽

Xuwen Jing ◽

Bingqiang Li ◽

Chao Kang ◽

Zhenhuan Dou ◽

...

Keyword(s):

Vibration Signal ◽

Rotating Machinery ◽

Second Order ◽

Variable Speed ◽

Short Time Fourier Transform ◽

Stationary Characteristic ◽

Nonstationary Signals ◽

Time Frequency ◽

Effective Technology ◽

Short Time

AbstractIn recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.

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Monitoring a sudden crack of beam-like bridge during earthquake excitation

Vietnam Journal of Mechanics ◽

10.15625/0866-7136/35/3/2561 ◽

2013 ◽

Vol 35 (3) ◽

Author(s):

Nguyen Viet Khoa

Keyword(s):

Fourier Transform ◽

Crack Detection ◽

Crack Depth ◽

Sudden Change ◽

Wavelet Spectrum ◽

Cracked Beam ◽

Earthquake Excitation ◽

Time Frequency ◽

Short Time ◽

Time Information

This paper presents a wavelet spectrum technique for monitoring a sudden crack of a beam-like bridge structure during earthquake excitation. When there is a sudden crack caused by earthquake excitation the stiffness of the structure is changed leading to a sudden change in natural frequencies during vibration. It is difficult to monitor this sudden change in the frequency using conventional approaches such as Fourier transform because in Fourier transform the time information is lost so that it is impossible to analyse short time events. To overcome this disadvantage, wavelet spectrum, a time-frequency analysis, is used for monitoring a sudden change in frequency duringearthquake excitation for crack detection. In this study, a model of 3D crack is applied. The derivation of the stiffness matrix of a 3D cracked beam element with rectangular section adopted from fracture mechanics is presented. Numerical results showed that the sudden occurrence of the crack during earthquake excitation can be detected by the sudden change in frequency using wavelet power spectrum. When the crack depth increases, the instantaneous frequency (IF) of the structure is decreased.

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Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform and Its Application in Wind Turbine Gearbox Fault Diagnosis

Measurement Science and Technology ◽

10.1088/1361-6501/ac38ee ◽

2021 ◽

Author(s):

Zhaohong Yu ◽

Cancan Yi ◽

Xiangjun Chen ◽

Tao Huang

Keyword(s):

Wavelet Transform ◽

Wind Turbine ◽

Time Window ◽

Vibration Signal ◽

Second Order ◽

Center Frequency ◽

Wind Turbine Gearbox ◽

Adaptive Wavelet ◽

Time Frequency ◽

Window Width

Abstract Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing Time-Frequency (TF) Analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated Time-Frequency Representation (TFR), this paper proposes a new TFA method, namely Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform (AMWSST2). Firstly, a short-time window is innovatively introduced on the foundation of classical Continuous Wavelet Transform (CWT), and the window width is adaptively optimized by using the center frequency and scale factor. After that, a smoothing process is carried out between different segments to eliminate the discontinuity and thus Adaptive Wavelet Transform (AWT) is generated. Then, on the basis of the theoretical framework of Synchrosqueezing Transform (SST) and accurate Instantaneous Frequency (IF) estimation by the utilization of second-order local demodulation operator, Adaptive Second-order Synchrosqueezing Wavelet Transform (AWSST2) is formed. Considering that the quality of actual time-frequency analysis is greatly disturbed by noise components, through performing multiple Synchrosqueezing operations, the congregation of TFR energy is further improved, and finally, the AMWSST2 algorithm studied in this paper is proposed. Since Synchrosqueezing operations are performed only in the frequency direction, this method AMWSST2 allows the signal to be perfectly reconstructed. For the verification of its effectiveness, this paper applies it to the processing of the vibration signal of the gearbox of a 750 kW wind turbine.

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Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.439-440.1037 ◽

2010 ◽

Vol 439-440 ◽

pp. 1037-1041 ◽

Cited By ~ 1

Author(s):

Yan Jue Gong ◽

Zhao Fu ◽

Hui Yu Xiang ◽

Li Zhang ◽

Chun Ling Meng

Keyword(s):

Wavelet Transform ◽

Frequency Characteristic ◽

Vibration Signal ◽

Wavelet Denoising ◽

Signal Denoising ◽

Denoising Method ◽

Vibration Signals ◽

Air Compressor ◽

Time Frequency ◽

Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

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Application of Choi—Williams Reduced Interference Time Frequency Distribution to Machinery Diagnostics

Shock and Vibration ◽

10.1155/1995/721969 ◽

1995 ◽

Vol 2 (6) ◽

pp. 437-444 ◽

Cited By ~ 2

Author(s):

Howard A. Gaberson

Keyword(s):

Fourier Transform ◽

Vibration Signal ◽

Test Cases ◽

Short Time Fourier Transform ◽

Vibration Signals ◽

Time Frequency ◽

Machinery Diagnostics ◽

Short Time ◽

Amplitude And Frequency Modulation ◽

Time Frequency Distribution

This article discusses time frequency analysis of machinery diagnostic vibration signals. The short time Fourier transform, the Wigner, and the Choi–Williams distributions are explained and illustrated with test cases. Examples of Choi—Williams analyses of machinery vibration signals are presented. The analyses detect discontinuities in the signals and their timing, amplitude and frequency modulation, and the presence of different components in a vibration signal.

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Robust estimation of instantaneous phase using a time-frequency adaptive filter

Geophysics ◽

10.1190/geo2011-0435.1 ◽

2013 ◽

Vol 78 (1) ◽

pp. O1-O7 ◽

Cited By ~ 5

Author(s):

Wen-kai Lu ◽

Chang-Kai Zhang

Keyword(s):

Adaptive Filter ◽

Signal To Noise Ratio ◽

Amplitude Spectrum ◽

Estimation Method ◽

Frequency Component ◽

Data Sets ◽

Time Frequency ◽

Instantaneous Phase ◽

The Hilbert Transform ◽

Short Time

The instantaneous phase estimated by the Hilbert transform (HT) is susceptible to noise; we propose a robust approach for the estimation of instantaneous phase in noisy situations. The main procedure of the proposed method is applying an adaptive filter in time-frequency domain and calculating the analytic signal. By supposing that one frequency component with higher amplitude has higher signal-to-noise ratio, a zero-phase adaptive filter, which is constructed by using the time-frequency amplitude spectrum, enhances the frequency components with higher amplitudes and suppresses those with lower amplitudes. The estimation of instantaneous frequency, which is defined as the derivative of instantaneous phase, is also improved by the proposed robust instantaneous phase estimation method. Synthetic and field data sets are used to demonstrate the performance of the proposed method for the estimation of instantaneous phase and frequency, compared by the HT and short-time-Fourier-transform methods.

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Vibration signature analysis for monitoring rotor broken bar in double squirrel cage induction motors based on wavelet analysis

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-09-2013-0304 ◽

2014 ◽

Vol 33 (5) ◽

pp. 1625-1641 ◽

Cited By ~ 10

Author(s):

Rosario Miceli ◽

Yasser Gritli ◽

Antonino Di Tommaso ◽

Fiorenzo Filippetti ◽

Claudio Rossi

Keyword(s):

Wavelet Transform ◽

Induction Motor ◽

Vibration Signal ◽

Operating Conditions ◽

Single Frequency ◽

Content Type ◽

Time Frequency ◽

Wavelet Transform Analysis ◽

Cage Induction Motor ◽

Diagnosis Technique

Purpose – The purpose of this paper is to present a diagnosis technique, for rotor broken bar in double cage induction motor, based on advanced use of wavelet transform analysis. The proposed technique is experimentally validated. Design/methodology/approach – The proposed approach is based on a combined use of frequency sliding and wavelet transform analysis, to isolate the contribution of the rotor fault components issued from vibration signals in a single frequency band. Findings – The proposed technique is reliable for tracking the rotor fault components over time-frequency domain. The quantitative analysis results based on this technique are the proof of its robustness. Research limitations/implications – The validity of the proposed diagnosis approach is not limited to the analysis under steady-state operating conditions, but also for time-varying conditions where rotor fault components are spread in a wide frequency range. Practical implications – The developed approach is best suited for automotive or high power traction systems, in which safe-operating and availability are mandatory. Originality/value – The paper presents a diagnosis technique for rotor broken bar in double cage induction motor base on advanced use of wavelet transform which allows the extraction of the most relevant rotor fault component issued from axial vibration signal and clamping it in a single frequency bandwidth, avoiding confusions with other components and false interpretations.

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Wavelet Transform in Vibration Analysis for Mechanical Fault Diagnosis

Shock and Vibration ◽

10.1155/1996/375635 ◽

1996 ◽

Vol 3 (1) ◽

pp. 17-26 ◽

Cited By ~ 10

Author(s):

W.J. Wang

Keyword(s):

Wavelet Transform ◽

Time Scale ◽

Vibration Analysis ◽

Three Dimensional ◽

Two Dimensional ◽

Vibration Signals ◽

Time Frequency ◽

Special Cases ◽

Mechanical Faults ◽

Short Time

The wavelet transform is introduced to indicate short-time fault effects in associated vibration signals. The time-frequency and time-scale representations are unified in a general form of a three-dimensional wavelet transform, from which two-dimensional transforms with different advantages are treated as special cases derived by fixing either the scale or frequency variable. The Gaussian enveloped oscillating wavelet is recommended to extract different sizes of features from the signal. It is shown that the time-frequency and time-scale distributions generated by the wavelet transform are effective in identifying mechanical faults.

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Application of Short Time Fourier Transform and Wavelet Transform for Sound Source Localization Using Single Moving Microphone in Machine Condition Monitoring

KnE Engineering ◽

10.18502/keg.v0i0.488 ◽

2016 ◽

Vol 1 (1) ◽

Author(s):

Meifal Rusli

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Sound Source ◽

Low Frequency ◽

Peak Position ◽

Source Position ◽

Short Time Fourier Transform ◽

Sound Sources ◽

Time Frequency ◽

Short Time

The paper discusses means to predict sound source position emitted by fault machine components based on a single microphone moving in a linear track with constant speed. The position of sound source that consists of some frequency spectrum is detected by time-frequency distribution of the sound signal through Short Time Fourier Transform (STFT) and Continues Wavelet Transform (CWT). As the amplitude of sound pressure increases when the microphone moves closer, the source position and frequency are predicted from the peaks of time-frequency contour map. Firstly, numerical simulation is conducted using two sound sources that generate four different frequencies of sound. The second case is experimental analysis using rotating machine being monitored with unbalanced, misalignment and bearing defect. The result shows that application of both STFT and CWT are able to detect multiple sound sources position with multiple frequency peaks caused by machine fault. The STFT can indicate the frequency very clearly, but not for the peak position. On the other hand, the CWT is able to predict the position of sound at low frequency very clearly. However, it is failed to detect the exact frequency because of overlapping.

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