Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate

Mishra, C.; Samantaray, Arun Kumar; Chakraborty, Goutam

doi:10.1016/j.ymssp.2015.10.019

Cited by 102 publications

(46 citation statements)

References 27 publications

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“…It uses a combination of short-time Fourier transform-based SK, kurtogram, adaptive SK, and protrugram [42]. However, despite such specific situations, the wavelet transform has been the most popular denoising technique for the extraction of the defect vibratory signature from the measured signal in which the random noise and other parameters of the bearing are immersed [43][44][45]. With respect to data processing, the stochastic process inherent to bearing wear can be classified as a single component which depends on the nature of the degradation state: discrete or continuous [46].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Pino

Ribas

Guimarães

2018

Shock and Vibration

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The contribution of a medium-sized hydro power plant to the power grid can be either at base load or at peak load. When the latter is the most common operation mode, it increases the start and stop frequency, intensifying the hydro turbine components' degradation, such as the guide bearings. This happens due to more frequent operation in transient states, which means being outside the service point of the machines' nominal condition, consisting of speed, flow, and gross head. Such transient state operation increases the runner bearings' mechanical vibration. The readings are acquired during the runner start-ups and filtered by a DC component mean value and a wavelet packet transform. The filtered series are used to estimate the relationship between the maximum orbit curve displacement and the accumulated operating hours. The estimated equation associated with the ISO 7919-5 vibration standards establishes the sojourn times of the degradation states, sufficient to obtain the transition probability distribution. Thereafter, a triangular probability function is used to determine the observation probability distribution in each state. Both matrices are inputs required by a hidden Markov model aiming to simulate the equipment deterioration process, given a sequence of maximum orbit curve displacements.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Pino

Ribas

Guimarães

2018

Shock and Vibration

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show abstract

“…Pulsating variable components are characterized by frequency and amplitude, while the mean amplitudes of pulsation are characterized by appropriate coefficients of pulsations [17]. For example, for the Reynolds-averaged Navier-Stokes (RANS) equation, the method of averaging is to replace the flow characteristics (velocity, pressure, density) with totals of the averaged and pulsating components that randomly change.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Formalization of design for physical model of the azimuth thruster with two degrees of freedom by computational fluid dynamics methods

Budashko

2017

EEJET

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“…are used to improve the signal signal-to-noise ratio (SNR) to some extent. 32,[34][35][36][37][38][39] Among all the denoising methods, wavelet transform (WT) denoising and singular value difference spectrum (SVDS) denoising has been widely applied to many signals. 35 Different from WT denoising, SVDS denoising is a nonparametric signal analysis tool which can be implemented without predefined base functions.…”

Section: Introductionmentioning

confidence: 99%

A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

Wang

2018

Journal of Low Frequency Noise, Vibration and Active Control

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Rolling bearing is one of the most crucial components in rotating machinery and due to their critical role, it is of great importance to monitor their operation conditions. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. Therefore, signal denoising preprocessing has become an essential part of condition monitoring and fault diagnosis. In the present study, a hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing is proposed. First, as a denoising preprocessing method, singular value difference spectrum denoising is applied to reduce the noise of the bearing vibration signal and improve the signal-to-noise ratio. Then, local mean decomposition method is used to decompose the denoised signals into several product functions. And product functions corresponding to the fault feature are selected according to the correlation coefficient criterion. Finally, Teager energy spectrum is analyzed by applying the Teager energy operator to the constructed amplitude modulation component. The proposed method is successfully applied to analyze the vibration signals collected from an experimental motive rolling bearing and rolling bearing of the self-made rotor experimental platform. The experimental results demonstrate that the proposed singular value difference spectrum denoising and local mean decomposition method can achieve fairly or slightly better performance than the normal local mean decomposition-Teager energy operator method, fast kurtogram, and the wavelet denoising and local mean decomposition method.

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Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate

Cited by 102 publications

References 27 publications

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Formalization of design for physical model of the azimuth thruster with two degrees of freedom by computational fluid dynamics methods

A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

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