Application of rank-order morphological filter in vibration signal de-noising

Zhang, Wenbin; Wang, Hongjun; Teng, Rui; Xu, Shaoping

doi:10.1109/cisp.2010.5648096

Cited by 10 publications

(8 citation statements)

References 2 publications

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“…Step 2: Using rank-order morphological filter to denoise the white noise and other interferences in the original signal. The detailed algorithm and calculating method can be seen in author's previous research achievement [1][2].…”

Section: Gear Fault Recognition Methodsmentioning

confidence: 99%

Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence

Zhang

Zhou

et al. 2013

AMR

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In this paper, a novel method to recognize gear fault pattern was approached based on harmonic wavelet package (HWP), sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Secondly, different gear fault signals were decomposed into eight frequency bands by harmonic wavelet package in three levels; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

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Section: Gear Fault Recognition Methodsmentioning

confidence: 99%

Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence

Zhang

Zhou

et al. 2013

AMR

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“…(ii) Using rank-order morphological filter to denoise the white noise and other interferences in the original signal. The detailed algorithm and calculating method can be seen in author's previous research achievement [1][2].…”

Section: Algorithm Of Fault Recognition For Rotormentioning

confidence: 99%

A New Intelligent Fault Recognition Method for Rotating Machinery

Zhang

Min

et al. 2013

AMR

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In this paper, a novel method to recognize rotor fault pattern was proposed based on rank-order morphological filter, harmonic window decomposition, sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Then, the six feature frequency bands which contain the typical fault information were extracted by harmonic window decomposition that needs not decomposition; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different rotor vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in fault diagnosis of rotating machinery effectively.

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“…Many signal processing methods using advanced techniques have been presented by many researchers: frequency analysis focusing on Fourier transform [9], Wigner distribution [10], rank-order morphological filter [11], cyclostationary signals for mechanical applications [12], and the envelope analysis [13], which is the most well-known for rotational-machine fault diagnosis applications such as bearing-fault diagnosis. It detects the repeating shock amplitudes that appear as faulty teeth traverse each rotation cycle.…”

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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Application of rank-order morphological filter in vibration signal de-noising

Cited by 10 publications

References 2 publications

Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence

Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence

A New Intelligent Fault Recognition Method for Rotating Machinery

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

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