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

Abstract

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.

A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults

“…The first scenario belongs to group (0-6), where the speed acceleration of the rotors was increased rapidly to reach about 3500 rpm for HP rotor and 4200 rpm for LP rotor. In this case, the second scenario started from group (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) slightly reduces the speed acceleration compared to the previous one, while the maximum obtained speed was 3000 rpm for the LP rotor and 5000 rpm for the HP rotor. In both scenarios, the speed ratio, which refers to the ratio between LP rotor speed and the HP rotor speed, remained constant.…”

“…However, obtained results could suffer from several drawbacks related to model generalizability which resulted due to not taking into account resiliency against data drift problem. In [7], an approach combining successive variational mode decomposition and blind source separation based on slap swarm optimization for bearing fault diagnosis is proposed. The study basically targets data complexity from the power spectrum analysis perspective of improving the noise-to-ratio of vibration signals.…”

Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach

“…Most of the time, feature extraction techniques were performed automatically by involving deep learning (i.e., [10,13,14]); on the contrary, it is of great importance to explore the spectral nature of the recorded signal before obtaining better representations (e.g., [9,11,12,15]); 5.…”