Morlet Wavelet UDWT Denoising and EMD based Bearing Fault Diagnosis

Raj, Ananya; Muthialu, Nagarajan

doi:10.7251/els1317001r

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…It was reported that soft thresholding might result in the loss of useful information; therefore, hard thresholding was preferred for de-noising. The bearing fault signal, which was de-noised by the undecimated discrete wavelet transform (UDWT), was decomposed by empirical mode decomposition (EMD) into a set of intrinsic mode functions (IMFs) [10], [11]. The fast Fourier transform (FFT) of the specific IMF could clearly reflect the characteristic frequency of the fault.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Machine learning algorithms have often been used as classifiers to distinguish faulty conditions from the normal condition. The relative spectral entropy and gravity frequency of the envelope spectrum were used as the two-dimensional vector features for the K-nearest neighbor recognition [11]. The envelope analysis based on an energy operator was reportedly better than the envelope demodulation of Hilbert transform in terms of speed and accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

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Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

Liu

Weng

2019

Measurement Science Review

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Motor-driven machines, such as water pumps, air compressors, and fans, are prone to fatigue failures after long operating hours, resulting in catastrophic breakdown. The failures are preceded by faults under which the machines continue to function, but with low efficiency. Most failures that occur frequently in the motor-driven machines are caused by rolling bearing faults, which could be detected by the noise and vibrations during operation. The incipient faults, however, are difficult to identify because of their low signal-to-noise ratio, vulnerability to external disturbances, and non-stationarity. The conventional Fourier spectrum is insufficient for analyzing the transient and non-stationary signals generated by these faults, and hence a novel approach based on wavelet packet decomposition and support vector machine is proposed to distinguish between various types of bearing faults. By using wavelet and statistical methods to extract the features of bearing faults based on time-frequency analysis, the proposed fault diagnosis procedure could identify ball bearing faults successfully.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

Liu

Weng

2019

Measurement Science Review

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“…In other words, even EMD combination techniques are unreliable in noisy environments. Therefore, to effectively apply enhanced signal analysis techniques to non-stationary vibration signals, a proper pre-processing method to reduce noise is required, such as narrowband demodulation [36] or discrete wavelet transform (DWT) [37,38]. Applying those de-noising methods effectively reduces the measurement noise, but the original informative signal is also distorted by the attenuation of a narrow bandpass filter in narrowband demodulation or the threshold in DWT-based de-noising.…”

Section: Introductionmentioning

confidence: 99%

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Nguyen

Prosvirin

Kim

2020

Sensors

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The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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“…Sawalhi [27] combines MED with SK to enhance the rolling bearing failure. Raj [25] combines EMD with wavelet de-noising for fault diagnosis of bearings. Although much work has been done, there are still many shortcomings in bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis based on Stochastic Resonance with Cuckoo Search

Chi¹

2018

IJPE

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Rolling bearings are the main components of modern machinery, and harsh operating environments often make them prone to failure. Therefore, detecting the incipient fault as soon as possible is useful for bearing prognostics and health management. However, the useful feature information relevant to the bearing fault contained in the vibration signals is weak under the influence of the noise and transmission path. The useful feature information is even submerged in the noise. Thus, it becomes difficult to identify the fault symptom of rolling bearings in time from the vibration signals. Stochastic resonance (SR) is a reliable method to detect the weak signal in intense noise. However, the effect of SR depends on the adjustment of two parameters. Cuckoo Search (CS) is a heuristic novel optimization algorithm that can search the global solution quickly and efficiently. Thus, CS is utilized to optimize the two parameters in this paper. Local signalto-noise ratio (LSNR) is used to evaluate resonance effect. Two bearing fault datasets were used to confirm the effectiveness of SR optimized by CS. SR methods optimized by particle swarm optimization (PSO), genetic algorithms (GA), firefly algorithm (FA), and ant colony optimization (ACO) are also used to detect the bearing fault signal in the two datasets. The analysis results state SR optimized by CS can find better LSNR than SR optimized by other algorithms no matter if it is in the same iterations or in the same computation time, thereby making the fault feature more obvious.

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Morlet Wavelet UDWT Denoising and EMD based Bearing Fault Diagnosis

Abstract: Abstract-Bearing

Cited by 12 publications

References 9 publications

Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

Fault Diagnosis of Ball Bearing Elements: A Generic Procedure based on Time-Frequency Analysis

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Bearing Fault Diagnosis based on Stochastic Resonance with Cuckoo Search

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