Optimal wavelet analysis and enhanced independent component analysis for isolated and combined mechanical faults diagnosis

Thelaidjia, Tawfik; Chenikher, Salah; Moussaoui, Abdelkrim

doi:10.1504/ijamechs.2020.111309

Cited by 5 publications

(9 citation statements)

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“…For this reason, another stage using an unsupervised classifier based on fuzzy C-mean algorithm is introduced to select the reliable independent sources. In this stage the same algorithm developed in [5] is adopted with the use of fuzzy C-mean algorithm instead of K-mean algorithm to select the reliable independent components. Here, tri-axial accelerometer that can simultaneously measure vibration signals through three perpendicular directions is used for data acquisition.…”

Section: Step 4 Construction Of the Input Matrixmentioning

confidence: 99%

“…Although it has several advantages, vibrations signals are always contaminated with noise that should be reduced as possible as we can. Several signal processing methods have been developed to overcome this shortcoming such as: wavelet analysis, empirical mode decomposition and variational mode decomposition (VMD) [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet analysis needs a good choice of the mother wavelet and the maximum level of decomposition [5]. The main disadvantage of empirical mode decomposition is mode mixing [9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Fig 5. Envelope spectra of the two independent sources obtained after the application of the suggested methodology using SVMD and SSA based on fuzzy C-mean.…”

mentioning

confidence: 99%

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Successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis

Thelaidjia

Chetih

Moussaoui

et al. 2023

Int J Adv Manuf Technol

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In this paper we are interested in developing a new approach that combines successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis. Firstly, vibration signals are pre-processed using successive variational mode decomposition to increase the signal-to-noise ratio. Then, the dynamic time warping algorithm is adopted to select the most effective modes which will be considered as mixture signals. In the second step we apply salp swarm algorithm (SSA) for estimating the de-mixing matrix to extract independent components from mixture signals. However, SSA suffers from the problem of population diversity. Consequently, it offers somewhat different independent sources at every execution of the program. To overcome this shortcoming, the SSA based source estimation will be executed several times with different ranges of initial positions. Then, a fuzzy C-mean algorithm is introduced to select the reliable independent components. The suggested method is tested based on two experiments and compared with other blind source algorithms based on Bat and particle swarm optimization (PSO) algorithms. The obtained results demonstrate the effectiveness of the suggested method in recovering reliable independent components and extracting fault frequency of bearings.

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Section: Step 4 Construction Of the Input Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Wavelet analysis needs a good choice of the mother wavelet and the maximum level of decomposition [5]. The main disadvantage of empirical mode decomposition is mode mixing [9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Fig 5. Envelope spectra of the two independent sources obtained after the application of the suggested methodology using SVMD and SSA based on fuzzy C-mean.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis

Thelaidjia

Chetih

Moussaoui

et al. 2023

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

show abstract

“…Wavelet analysis needs a good choice of the mother wavelet and the maximum level of decomposition [5]. The main disadvantage of empirical mode decomposition is mode mixing [9][10].…”

Section: Introductionmentioning

confidence: 99%