Performance assessment of a morphological index in fault prediction and trending of defective rolling element bearings

Patargias, T. I.; Yiakopoulos, Christos; Antoniadis, Ioannis

doi:10.1080/10589750600673568

Cited by 18 publications

(12 citation statements)

References 11 publications

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“…The first is to calculate the power spectrum of morphological envelopes to extract the impulse repetition frequency (IRF) [5]. The second is to isolate local peaks of morphological envelopes [7]. The latter method seems to be more effective than the former, because when a signal is heavily clouded by noise, its morphological envelope would mainly consist of the profile of noise.…”

Section: Discussion On Morphological Signal Processingmentioning

confidence: 99%

“…In this paper, the flat SE is used because it is the simplest one among SEs and appears to be quite appropriate for detecting impulses [7]. A flat SE is defined as a zero series with a length of M, i.e.…”

Section: Discussion On Morphological Signal Processingmentioning

confidence: 99%

“…Zhang et al [8] also used the closing operator with a flat SE, which is 0.6~0.8 times the IRP in length, to extract impulsive features from gear vibration signals. Patargias et al [7] proposed a morphological index (MI) that is the root mean square (RMS) value of the impulses isolated by the closing operator for rolling bearings monitoring.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a new method named morphological signal processing (MSP) has been introduced into the fault diagnosis of rotating machinery [5][6][7][8][9][10]. MSP is a nonlinear time-domain processing algorithm with four basic operators: erosion, dilation, opening and closing [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction

2010

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Periodical impulses are vital indicators of rotating machinery faults. Therefore, the extraction of weak periodical impulses from vibration signals is of great importance for incipient fault detection. However, measured signals are often severely tainted by various noises, which makes the detection of impulses rather difficult. As such, a proper signal processing technique is necessary. In this paper, a hybrid method comprised of wavelet filter and morphological signal processing (MSP) is proposed for this task. The wavelet filter is used to eliminate the noise and enhance the impulsive features. Then, the filtered signal is processed by the morphological closing operator and a local maximum algorithm to isolate periodical impulses. To select the proper parameters of the joint approach, i.e., the center frequency, the bandwidth of wavelet filter, and the length of flat structuring elements (SE), a novel optimization algorithm based on differential evolution (DE) is developed. The results of simulated experiments and bearing vibration signal analysis verify the effectiveness of the proposed method.

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Section: Discussion On Morphological Signal Processingmentioning

confidence: 99%

Section: Discussion On Morphological Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction

2010

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“…The repetitive impacts produced by a localized bearing defect can be described as a train of Dirac delta functions ( ) with the period , expressed as [28] …”

Section: Simulation Of the Bearing Faultmentioning

confidence: 99%

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

Lin

Shangfei

et al. 2017

Shock and Vibration

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The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH) is proposed to extract the impulsive signal and enhance the impulsiveness of the bearing fault signal, and the envelope analysis is applied to reveal the fault-related signatures. The impulsive extraction performance of the proposed CTH is compared with that of finite impulse response filter (FIR) by analyzing the simulated bearing fault signals, and the result indicates that the CTH is more effective in extracting impulsive signals. The method is evaluated using real fault signals from defective bearings with early rolling element fault and early fault located on the outer race. The results show that the proposed method is able to enhance the impulsiveness of early bearing fault signals.

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Rolling element bearing fault detection in industrial environments based on a K-means clustering approach

Yiakopoulos

Gryllias

Antoniadis

2011

Expert Systems with Applications

203

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Performance assessment of a morphological index in fault prediction and trending of defective rolling element bearings

Cited by 18 publications

References 11 publications

A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction

A joint adaptive wavelet filter and morphological signal processing method for weak mechanical impulse extraction

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

Rolling element bearing fault detection in industrial environments based on a K-means clustering approach

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