Emd via Memd: Multivariate Noise-Aided Computation of Standard Emd

Rehman, Naveed ur; Park, Cheolsoo; Huang, Norden E.; Mandic, Danilo P.

doi:10.1142/s1793536913500076

Cited by 161 publications

(145 citation statements)

References 22 publications

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“…[13]. By introducing extra channels of multivariate noise, the effects of mode mixing and aliasing could be reduced.…”

Section: Ceemd Methodmentioning

confidence: 99%

“…Bearing defects will generate a series of impact vibrations which occur at bearing characteristic frequencies every time a running roller passes over the surfaces of defects [13]. Therefore the type of defects can be determined by identifying the characteristic frequency.…”

Section: Pass Frequency Of Rolling Bearingmentioning

confidence: 99%

“…For instance, EMD was proved to be a dyadic filter bank and the properties of the white noise under EMD were also studied [12]. Rehman et al [13] demonstrated that NA-MEMD is useful in comparison with EEMD by illustrative examples and performance evaluations. And the real world electroencephalography signals had been analyzed to prove the advantage of NA-MEMD.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

Wang

Tang

et al. 2014

Advances in Mechanical Engineering

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In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

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“…[13]. By introducing extra channels of multivariate noise, the effects of mode mixing and aliasing could be reduced.…”

Section: Ceemd Methodmentioning

confidence: 99%

Section: Pass Frequency Of Rolling Bearingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

Wang

Tang

et al. 2014

Advances in Mechanical Engineering

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“…Figure 7 indicates the time-domain wave form of outer-race and inner-race fault vibration signals when the rotation speed is 60 rpm and Figure 8 shows the time-domain waveform of outer-race and inner-race vibration signals when the rotation speed is 70 rpm. Bearing defects will generate a series of impact vibrations that emit at bearing characteristic frequencies every time a running roller passes over a defect [32]. Therefore the fault type can be determined by identifying the characteristic frequency [33].…”

Section: Bearing Data Set Descriptionmentioning

confidence: 99%

A New Method for Weak Fault Feature Extraction Based on Improved MED

Jiang

Fan

et al. 2018

Shock and Vibration

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Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. Moreover, the traditional minimum entropy deconvolution (MED) method has been proved to be used to detect such fault signals. The MED uses objective function method to design the filter coefficient, and the appropriate threshold value should be set in the calculation process to achieve the optimal iteration effect. It should be pointed out that the improper setting of the threshold will cause the target function to be recalculated, and the resulting error will eventually affect the distortion of the target function in the background of strong noise. This paper presents an improved MED based method of fault feature extraction from rolling bearing vibration signals that originate in high noise environments. The method uses the shuffled frog leaping algorithm (SFLA), finds the set of optimal filter coefficients, and eventually avoids the artificial error influence of selecting threshold parameter. Therefore, the fault bearing under the two rotating speeds of 60 rpm and 70 rpm is selected for verification with typical low-speed fault bearing as the research object; the results show that SFLA-MED extracts more obvious bearings and has a higher signal-to-noise ratio than the prior MED method.

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“…In order to address these issues, a noise-assisted approach (Rehman et al 2013) is used in this work to provide a robust and improved oscillation detection method. The proposed method is shown to be robust against multiple oscillations, coloured 12 Background noise and varying noise strengths.…”

Section: Oscillation Detection In Individual Loopsmentioning

confidence: 99%

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

Aftab¹,

Hovd²,

Sivalingam

2017

IFAC-PapersOnLine

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SummaryThe profitability of any industrial process is closely related to its ability to maintain near optimal operating conditions; therefore robust methods for root cause detection of any control performance degradation is crucial not only to maintain the desired operation of any industrial process but can result in marked improvement in productivity and over all economics.In a large and complex industrial control system, disturbances/oscillations originating at one point tend to propagate both up-and downstream due to underlying interactions, process flows and recycle streams, thus leading to plant-wide effects. Effective controller performance monitoring therefore requires detection and diagnosis of such plant-wide disturbances so that targeted maintenance can be achieved within the shortest possible time.Oscillations are considered to be the most important indicator of control performance degradation. Oscillations not only cause product variability and effect quality but also lead to adverse economical consequences owing to loss of precious resources like raw material and energy. Therefore, automated detection and diagnosis of oscillations needs to be reliable and effective as the large scale of the control system in industrial process plants makes manual observation and diagnosis practically impossible.Detection and diagnosis of oscillatory control loops is not trivial due to the causes such as presence of multiple oscillations, unknown process dynamics, non stationary effects and noise corruption, to name a few. The work presented in this thesis is aimed at addressing these challenges and at the development of improved tools for both detection and diagnosis of oscillations and plant-wide disturbances. Moreover, data driven methods are preferred over model based diagnostics as they are more general and are independent of a specific plant model. Dynamic proiii iv Summary cess models can be costly to develop and maintain, and for many process plants dynamic models are not available.This thesis has focussed on using the multivariate empirical mode decomposition (MEMD) and associated characteristics for the detection and diagnosis of oscillations in control loops. The methods proposed in thesis are aimed at addressing the shortcomings of the existing approaches and are fully data driven that require no a priori knowledge about the data or process itself. An improved approach for the oscillation detection has been presented that caters for non-stationary effects and reduces the mode mixing problems associated with the univariate empirical mode decomposition. Moreover, MEMD along with the proposed grouping algorithm provides a robust method to detect the plant-wide oscillations where different control variables, having common signatures of oscillations, are oscillating due to the same root cause. This helps in searching for the root cause only in the affected variables.Another important contribution of the thesis is an automated detection of harmonics in an oscillating signal. Non-linearity induced oscillations g...

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Emd via Memd: Multivariate Noise-Aided Computation of Standard Emd

Cited by 161 publications

References 22 publications

An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

A New Method for Weak Fault Feature Extraction Based on Improved MED

A Delay Vector Variance based Approach for Detecting and Isolating the Non-linearity Induced Oscillations in Control Loops

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