Bearing fault diagnosis based on amplitude and phase map of Hermitian wavelet transform

Li, Hui; Fu, Lihui; Zheng, Huan

doi:10.1007/s12206-011-0717-0

Cited by 30 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simplest way to estimate this power density is to pass the data from a defined sample window. The defined window is called the periodogram and the advantage of this method is that it can detect hidden frequencies [7].…”

Section: Vibration Analysismentioning

confidence: 99%

Analysis, evaluation and monitoring of the characteristic frequencies of pneumatic drive unit and its bearing through their corresponding frequency spectra and spectral density

Castilla-Gutiérrez¹,

Fortes²,

Pulido‐Calvo³

2019

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

AnAlysis, evAluAtion And monitoring of the chArActeristic frequencies of pneumAtic drive unitAnd its beAring through their corresponding frequency spectrA And spectrAl density AnAlizA, ocenA i monitorowAnie częstotliwości chArAkterystycznych pneumAtycznego zespołu nApędowego i wchodzącego w jego skłAd łożyskA nA podstAwie widm częstotliwości orAz gęstości widmowej This article shows the results of the study of the characteristic frequencies of pneumatic drive equipment and its suspension bearing. The analysis approaches one of the most important requirements of the industrial sector, which seeks to be recognised by the efficiency and performance of its equipment when compared to its coming economic competitors. For data collection and we have followed the ISO 10816 standards, thus using the values of speed in RMS, aiming to reduce the masking of these signals that occurs depending on whether they are high or low frequencies. The study will respond to one of the most important requirements found in the predictive and preventive control of industrial sites. The problem of the predictive systems of maintenance of equipment with bearings lies in the number of monitoring and analysis points that generate a high cost in time and human resources. The aim will be to determine which of all the study frequencies is the most significant and in which position and measurement axis has the biggest impact. To do this, we will analyse the rotation frequency of the blowing machine, the resulting frequency of all the frequencies, the frequency of the impulsion blades and finally the frequency of the bearing. The study would be able to predict when our equipment is going to suffer a failure, reducing the control points and the cost.

show abstract

Section: Vibration Analysismentioning

confidence: 99%

Analysis, evaluation and monitoring of the characteristic frequencies of pneumatic drive unit and its bearing through their corresponding frequency spectra and spectral density

Castilla-Gutiérrez¹,

Fortes²,

Pulido‐Calvo³

2019

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

show abstract

“…All the abovementioned variations use the same basic principle to detect the fault, that is, if the shape of wavelet base function selected to take wavelet transform matches or correlates at a specific scale and time with the shape of the burst or impulse created when the fault occurs in rolling element bearing, wavelet transform generates large wavelet coefficient value otherwise gives low wavelet coefficient value. For this reason, researchers used various types of wavelet base functions such as Morlet (Lin and Qu, 2000; Nikolaou and Antoniadis, 2002; Yan and Gao, 2009), Hermitian (Li et al, 2011), impulse (Junsheng et al, 2007), Laplace (Al-Raheem et al, 2007; Feng et al, 2011), and empirical (Wang et al, 2018) for taking wavelet transform. The main drawback of a wavelet transform is that an analyzing mother wavelet cannot be changed once it is fix because of this reason single as well as MF detection in vibration signal is a very difficult task as wavelet base function selected for taking wavelet transform is not adaptive for different types of faults and machine operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Rolling element bearing multi-fault diagnosis using morphological joint time–frequency adaptive kernel–based semi-smart framework

Lonare

Fernandes²,

Abhyankar

2021

Journal of Vibration and Control

View full text Add to dashboard Cite

The wavelet transform is a state of the art time–frequency analysis method for rolling element bearing localized fault detection, using vibration signals. When these localized faults are present at more than one location of bearing, it is called “multi-fault.” Using wavelet transform fault detection with high severity is possible, but this method fails to detect the presence of fault as well as the location of a fault in multi-fault case and when the fault severity is low. The identification of the fault location, in rolling element bearing when more than one location of bearing contains a localized fault, is very useful for further root cause analysis; therefore, multi-fault detection is a challenge today. In the present work, a new morphological joint time–frequency adaptive kernel–based semi-smart framework is developed to address this challenge. In morphological joint time–frequency adaptive kernel, the kernel will adapt itself by analyzing the basic morphology of the bearing under observation and by considering the location of a fault. The simulation and experimental results show that morphological joint time–frequency adaptive kernel–based framework is able to detect low severity single fault as well as the location of the localized fault on rolling element bearing in the multi-fault case. Experimental results also show that the morphological joint time–frequency adaptive kernel framework is independent of bearing dimensions as well as machine operating conditions.

show abstract

“…Generally, low energy is associated with the frequency of defective bearing, and hence, it can be easily covered by other low-frequency effects and noise. [7][8][9][10][11][12] The time and frequency domain techniques have been developed by the researchers to overcome these problems. They have developed some techniques which generally use some parameters that are sensitive toward impulse oscillations, such as crest factor, kurtosis, peak level, root mean square (RMS) value, and shock pulse.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, low energy is associated with the frequency of defective bearing, and hence, it can be easily covered by other low-frequency effects and noise. 7–12…”

Section: Introductionmentioning

confidence: 99%

Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine

Malla

Rai

Kaul

et al. 2019

Noise & Vibration Worldwide

View full text Add to dashboard Cite

Condition monitoring and fault diagnosis of rolling element bearings are very important to ensure proper working of different types of machinery. Condition monitoring of rotating machines is mainly based on the analysis of machine vibration. The vibration signals from the mechanical fault generally comprise periodic impulses with specified characteristic frequency corresponds to a particular defect. But due to heavy noise in the industry, the vibration signals have a very low signal-to-noise ratio. Hence, it requires an appropriate technique to extract the impulses from the noisy signal. This article emphasized on the fault diagnosis of rolling element bearings having some specific size of defects on various bearing elements using the complex Morlet wavelet analysis. The phase and amplitude map of the complex Morlet wavelet are utilized for identification and diagnosis of the fault in the rolling element bearing. The amplitude and phase map corresponding to the complex Morlet wavelet are found to show unique informative signatures in the presence of bearing faults. The classification technique based on artificial neural network and support vector machine for rolling element bearing fault detection is presented in this article. The classification results of bearing faults clearly indicate that support vector machine has a more precise bearing fault classification ability than artificial neural network.

show abstract

Bearing fault diagnosis based on amplitude and phase map of Hermitian wavelet transform

Cited by 30 publications

References 21 publications

Analysis, evaluation and monitoring of the characteristic frequencies of pneumatic drive unit and its bearing through their corresponding frequency spectra and spectral density

Analysis, evaluation and monitoring of the characteristic frequencies of pneumatic drive unit and its bearing through their corresponding frequency spectra and spectral density

Rolling element bearing multi-fault diagnosis using morphological joint time–frequency adaptive kernel–based semi-smart framework

Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine

Contact Info

Product

Resources

About