Combining an optimisation-based frequency band identification method with historical data for novelty detection under time-varying operating conditions

Schmidt, Stephan; Gryllias, Konstantinos

doi:10.1016/j.measurement.2020.108517

Cited by 8 publications

(3 citation statements)

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“…The GWO algorithm was used to optimize the parameters of MOMEDA, and it can be seen from Figure 11a that the maximum SII value of the deconvolution signal is obtained when the population evolves to the 11th generation, while the optimal parameter combination [T * s , L * ] searched is [247. 27,1602]. The signal reconstructed by using TVFEMD was denoised using optimal-parameter MOMEDA, and the envelope spectrum of the denoised signal was calculated.…”

Section: Analysis Of the Bearing Outer Ring Fault Signalmentioning

confidence: 99%

“…After the filter length L in the optimal parameter combination [147. 27,1902] was changed to 600, the MOMEDA method was used to filter the TVFEMD-reconstructed signal, and the results are shown in Figure 13a As a comparison, the MCKD method and the FSK method were used to analyze the fault signals of the bearing outer race. Figure 13 shows the processing results of the MCKD method.…”

Section: Analysis Of the Bearing Outer Ring Fault Signalmentioning

confidence: 99%

“…To select the optimal parameter combination [L, T s ], a new index of multi-objective optimization is constructed to adaptively determine the filter length and the initial value of the fault cycle search. In the time domain, the root mean square of the maximum of autocorrelation function (MOAF) is used to measure the periodic fault impact components contained in the deconvolution signals [26,27]. The core of this index is the autocorrelation function.…”

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confidence: 99%

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Early Fault Detection of Rolling Bearings Based on Time-Varying Filtering Empirical Mode Decomposition and Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted

Song,

Wang

2023

Entropy

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Due to the early formation of rolling bearing fault characteristics in an environment with strong background noise, the single use of the time-varying filtering empirical mode decomposition (TVFEMD) method is not effective for the extraction of fault characteristics. To solve this problem, a new method for early fault detection of rolling bearings is proposed, which combines multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) with parameter optimization and TVFEMD. Firstly, a new weighted envelope spectrum kurtosis index is constructed using the correlation coefficient and envelope spectrum kurtosis, which is used to identify the effective component and noise component of the bearing fault signal decomposed by TVFEMD, and the intrinsic mode function (IMF) containing rich fault information is selected for reconstruction. Then, a new synthetic impact index (SII) is constructed by combining the maximum value of the autocorrelation function and the kurtosis of the envelope spectrum. The SII index is used as the fitness function of the gray wolf optimization algorithm to optimize the fault period, T, and the filter length, L, of MOMDEA. The signal reconstructed by TVF-EMD undergoes adaptive filtering using the MOMEDA method after parameter optimization. Finally, an envelope spectrum analysis is performed on the signal filtered by the adaptive MOMEDA method to extract fault feature information. The experimental results of the simulated and measured signals indicate that this method can effectively extract early fault features of rolling bearings and has good reliability. Compared to the classical FSK, MCKD, and TVFEMD-MOMEDA methods, the first-order correlated kurtosis (FCK) and fault feature coefficient (FFC) of the filtered signal obtained using the proposed method are the largest, while the sample entropy (SE) and envelope spectrum entropy (ESE) are the smallest.

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Section: Analysis Of the Bearing Outer Ring Fault Signalmentioning

confidence: 99%

Section: Analysis Of the Bearing Outer Ring Fault Signalmentioning

confidence: 99%

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confidence: 99%

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