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Nowadays, star athletes are global brand personalities. The increased popularity of the professional sport has contributed to elevating exceptional athletes to international star status. This empirical study aims to assess the impact of athlete performance and brand social value on product involvement with the mediation effect of celebrity athlete endorsement.

A quantitative survey was conducted with 399 Chinese participants. PLS-SEM is adopted to examine the associated paths and the mediating effect of celebrity endorsement.

The results demonstrate the significant impact of athlete performance and brand social value on product involvement. Celebrity endorsement partially mediates the effects of athlete performance and brand social value on product involvement.

This study extends understanding of celebrity athlete endorsement and provides insight into the strategic implications for Chinese social media-based marketing initiatives in the context of the recent Olympic Game in Tokyo 2021.

Bearings play a critical role in the reliable operation of induction machines, and their failure can lead to significant operational challenges and downtime. Detecting and diagnosing these defects is imperative to ensure the longevity of induction machines and preventing costly downtime. The purpose of this paper is to develop a novel approach for diagnosis of bearing faults in induction machine.

To identify the different fault states of the bearing with accurately and efficiently in this paper, the original bearing vibration signal is first decomposed into several intrinsic mode functions (IMFs) using variational mode decomposition (VMD). The IMFs that contain more noise information are selected using the Pearson correlation coefficient. Subsequently, discrete wavelet transform (DWT) is used to filter the noisy IMFs. Second, the composite multiscale weighted permutation entropy (CMWPE) of each component is calculated to form the features vector. Finally, the features vector is reduced using the locality-sensitive discriminant analysis algorithm, to be fed into the support vector machine model for training and classification.

The obtained results showed the ability of the VMD_DWT algorithm to reduce the noise of raw vibration signals. It also demonstrated that the proposed method can effectively extract different fault features from vibration signals.

This study suggested a new VMD_DWT method to reduce the noise of the bearing vibration signal. The proposed approach for bearing fault diagnosis of induction machine based on VMD-DWT and CMWPE is highly effective. Its effectiveness has been verified using experimental data.