Analysis of vibration signals produced by the cavitation in centrifugal pumps using the Power Spectrum and Continuous Wavelet Transform

Abstract Centrifugal pumps (CPs) fail due to anomalies in fluid flow patterns and/or due to failure of mechanical subsystems in them. In this work, a technique built on the multiclass support vector machine (MSVM) is developed to identify multiple faults in the CP. In addition, the complex problem of fault combinations and their classification is dealt with in this work. The combination of features from motor line current sensors and accelerometers is used to train the algorithm. To take into account the transient as well as harmonic components of fault signatures, continuous wavelet transform (CWT) analysis is used. Thereafter, the most important information from the CWT coefficients is selected using the two proposed novel methods CWT-based on energy (BE)-MSVM and CWT-principal component analysis (PCA)-MSVM, which are BE as well as PCA, respectively. It is experimentally observed that faults in the CPs have a very strong association with its operating speed. Thus, in order to make the CP versatile in operation, it is important that the fault diagnosis methodology is also efficient at large speed range of CP operation. This work attempts to develop a fault classification methodology, which is independent of the CP operating speed.

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Ball Bearing Fault Diagnosis Based on Vibration Signals of Two Stroke IC Engine Using Continuous Wavelet Transform

Lecture Notes in Mechanical Engineering - Advances in Rotor Dynamics, Control, and Structural Health Monitoring ◽

10.1007/978-981-15-5693-7_28 ◽

2020 ◽

pp. 381-391

Author(s):

K. N. Ravikumar ◽

C. K. Madhusudana ◽

Hemantha Kumar ◽

K. V. Gangadharan

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Continuous Wavelet Transform ◽

Ball Bearing ◽

Continuous Wavelet ◽

Ic Engine ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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Development of Spike Wavelet Analysis and Its Application to Damage Analysis on Gearbox

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.321-323.1233 ◽

2006 ◽

Vol 321-323 ◽

pp. 1233-1236

Author(s):

Sang Kwon Lee ◽

Jang Sun Sim

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

High Frequency ◽

Low Frequency ◽

Gear System ◽

Frequency Resolution ◽

Linear Scaling ◽

Continuous Wavelet ◽

Vibration Signals ◽

Gear Fault

Impulsive sound and vibration signals in gear system are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in condition monitoring of gear system. The traditional continuous wavelet transform has been used for detection of impulsive signals. However, it is often difficult for the continuous wavelet transform to identify spikes at high frequency and meshing frequencies at low frequency simultaneously since the continuous wavelet transform is to apply the linear scaling (a-dilation) to the mother wavelet. In this paper, the spike wavelet transform is developed to extract these impulsive sound and vibration signals. Since the spike wavelet transform is to apply the non-linear scaling, it has better time resolution at high frequency and frequency resolution at low frequency than that of the continuous wavelet transform respectively. The spike wavelet transform can be, therefore, used to detect fault position clearly without the loss of information for the damage of a gear system. The spike wavelet transform is successfully is applied to detection of the gear fault with tip breakage.

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Inaccessible rolling bearing diagnosis using a novel criterion for Morlet wavelet optimization

Journal of Vibration and Control ◽

10.1177/1077546321989503 ◽

2021 ◽

pp. 107754632198950

Author(s):

Mehdi Behzad ◽

Amirmasoud Kiakojouri ◽

Hesam Addin Arghand ◽

Ali Davoodabadi

Keyword(s):

Wavelet Transform ◽

Continuous Wavelet Transform ◽

Rolling Bearing ◽

The Other ◽

Vibration Measurement ◽

Continuous Wavelet ◽

Vibration Signals ◽

Characteristic Frequencies ◽

Ratio Criterion ◽

Bearing Characteristic

The objective of this research is to diagnose an inaccessible rolling bearing by indirect vibration measurement. In this study, a shaft supported with several bearings is considered. It is assumed that the vibration for at least one bearing is not recordable. The purpose is to diagnose inaccessible bearing by the recorded data from the sensors located on the other bearings. To achieve this goal, the continuous wavelet transform is used to detect weak signatures in the available vibration signals. A new criterion for adjusting the scale parameter of continuous wavelet transform is proposed based on the amplitude of the bearing characteristic frequencies. In this criterion, the optimal scale is selected to maximize the amplitude of bearing characteristic frequencies in comparison with the amplitude of the other frequencies. The results of the proposed method are compared with a popular method, energy-to-entropy ratio criterion, using two different sets of run-to-failure experimental data. Results indicate that the proposed method in this article is more effective and efficient for extracting the weak signatures and diagnosing inaccessible bearings from the recorded vibration signals.

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Continuous wavelet transform based processing for estimating the power spectrum content of heart rate variability during hemodiafiltration

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2020.102031 ◽

2020 ◽

Vol 62 ◽

pp. 102031 ◽

Cited By ~ 1

Author(s):

Raúl Cartas-Rosado ◽

Brayans Becerra-Luna ◽

Raúl Martínez-Memije ◽

Óscar Infante-Vázquez ◽

Claudia Lerma ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Wavelet Transform ◽

Power Spectrum ◽

Continuous Wavelet Transform ◽

Continuous Wavelet

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Identification of BDS Satellite Clock Periodic Signals Based on Lomb-Scargle Power Spectrum and Continuous Wavelet Transform

Energies ◽

10.3390/en14217155 ◽

2021 ◽

Vol 14 (21) ◽

pp. 7155

Author(s):

Jacek Kudrys ◽

Dominik Prochniewicz ◽

Feng Zhang ◽

Mateusz Jakubiak ◽

Kamil Maciuk

Keyword(s):

Wavelet Transform ◽

Power Spectrum ◽

Continuous Wavelet Transform ◽

Complex Analysis ◽

Earth Orbit ◽

Navigation Satellite ◽

Continuous Wavelet ◽

Satellite Clock ◽

Complex Behaviour ◽

Time Frequency

Onboard satellite clocks are the basis of Global Navigation Satellite Systems (GNSS) operation, and their revolution periods are at the level of 2 per day (about 12 h) in the case of the Medium Earth Orbit (MEO) satellites. In this work, the authors analysed the entire BeiDou Navigation Satellite System (BDS) space segment (BDS-2 and BDS-3) in terms of the occurrence of periodic, repetitive signals in the clock products, and checked if they coincide with the orbital periods or their multiples. The Lomb-Scargle (L-S) power spectrum was used as a tool to determine the periods present in the BDS clock products, allowing for analyses based on incomplete input data; in this case, the incomplete data were the phase data with jumps and outliers removed. In addition, continuous wavelet transform (CWT) was used to produce a time−frequency representation showing the more complex behaviour of the satellite clock products. As shown in the case of geostationary and geosynchronous inclined orbit satellites, the main period was 23.935 h, while for the Medium Earth Orbit it was 12.887 h, with the BDS satellite orbital period being 12 h 53 m (12.883 h). Some effects connected with reference clock swapping are also visible in the power spectrum. The conducted analyses showed that the BDS-2 satellite clocks have much higher noise than the BDS-3 satellite clocks, meaning that the number of designated periods is greater, but their reliability is significantly lower. BDS-3 satellites have only been in operation for a very short time, thus this is the first analysis to include this type of data. Moreover, such a wide and complex analysis has not been carried out to date.

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Instantaneous Frequencies of Continuous Blood Pressure a Comparison of the Power Spectrum, the Continuous Wavelet Transform and the Hilbert–Huang Transform

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x17500097 ◽

2017 ◽

Vol 09 (04) ◽

pp. 1750009

Author(s):

Kathrine Knai ◽

Geir Kulia ◽

Marta Molinas ◽

Nils Kristian Skjaervold

Keyword(s):

Blood Pressure ◽

Wavelet Transform ◽

Power Spectrum ◽

Continuous Wavelet Transform ◽

Continuous Wavelet ◽

Frequency Shifts ◽

Hilbert Huang Transform ◽

Frequency Components ◽

Instantaneous Frequencies ◽

Continuous Blood Pressure

Continuous biological signals, like blood pressure recordings, exhibit nonlinear and nonstationary properties which must be considered during their analysis. Heart rate variability analyses have identified several frequency components and their autonomic origin. There is need for more knowledge on the time-changing properties of these frequencies. The power spectrum, continuous wavelet transform and Hilbert–Huang transform are applied on a continuous blood pressure signal to investigate how the different methods compare to each other. The Hilbert–Huang transform shows high ability to analyze such data, and can, by identifying instantaneous frequency shifts, provide new insights into the nature of these kinds of data.

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