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The purpose of this paper is to obtain a more accurate fatigue life of structures by introducing the surface roughness into fatigue life prediction model.

Based on the fatigue life prediction model with surface roughness correction, the shock absorber cylinder is taken as an example to verify the feasibility of the improved method. Based on the load of the shock absorber cylinder during driving, fatigue experiments are performed under longitudinal and lateral forces, respectively. Then, the fatigue life predicted by the modified model is compared with that predicted by the traditional model.

By comparing with the test results, considering the influence of mean stress, the Manson method is more accurate in life prediction. Then, the modified Manson-Coffin and Manson method with surface roughness is more accurate in life prediction under longitudinal force and lateral forces, respectively. This verifies the feasibility of the improved method with the surface roughness.

The research on the influence of surface roughness on fatigue life can lay the technical foundation for the life prediction of products and have great significance to the quality evaluation of products.

The purpose of this paper is to introduce error ellipse into the bootstrap method to improve the reliability of small samples and the credibility of the S-N curve.

Based on the bootstrap method and the reliability of the original samples, two error ellipse models are proposed. The error ellipse model reasonably predicts that the discrete law of expanded virtual samples obeys two-dimensional normal distribution.

By comparing parameters obtained by the bootstrap method, improved bootstrap method (normal distribution) and error ellipse methods, it is found that the error ellipse method achieves the expansion of sampling range and shortens the confidence interval, which improves the accuracy of the estimation of parameters with small samples. Through case analysis, it is proved that the tangent error ellipse method is feasible, and the series of S-N curves is reasonable by the tangent error ellipse method.

The error ellipse methods can lay a technical foundation for life prediction of products and have a progressive significance for the quality evaluation of products.

To improve the accuracy of parameter prediction for small-sample data, considering the existence of error in samples, the error circle is introduced to analyze original samples.

The influence of surface roughness on fatigue life is discussed. The error circle can treat the original samples and extend the single sample, which reduces the influence of the sample error.

The S-N curve obtained by the error circle method is more reliable; the S-N curve of the Bootstrap method is more reliable than that of the Maximum Likelihood Estimation (MLE) method.

The parameter distribution and characteristics are statistically obtained based on the surface roughness, surface roughness factor and intercept constant. The original sample is studied by an error circle and discussed using the Bootstrap and MLE methods to obtain corresponding S-N curves. It provides a more trustworthy basis for predicting the useful life of products.

This study aims to reconstruct the frictional vibration signal from noise and characterize the running-in process by frictional vibration.

There is a strong correlation between tangential frictional vibration and normal frictional vibration. On this basis, a new frictional vibration reconstruction method combining cross-correlation analysis with ensemble empirical mode decomposition (EEMD) was proposed. Moreover, the concept of information entropy of friction vibration is introduced to characterize the running-in process.

Compared with the wavelet packet method, the tangential friction vibration and the normal friction vibration reconstructed by the method presented in this paper have a stronger correlation. More importantly, during the running-in process, the information entropy of friction vibration gradually decreases until the equilibrium point is reached, which is the same as the changing trend of friction coefficient, indicating that the information entropy of friction vibration can be used to characterize the running-in process.

The study reveals that the application EEMD method is an appropriate approach to reconstruct frictional vibration and the information entropy of friction vibration represents the running-in process. Based on these results, a condition monitoring system can be established to automatically evaluate the running-in state of mechanical parts.

The EEMD method was applied to reconstruct the frictional vibration. Furthermore, the information entropy of friction vibration was used to analysis the running-in process.

The purpose of this paper is to study the effect of second generation involvement on corporate innovation in Chinese family firms.

Based on the manually collected sample of listed Chinese family firms from 2003 to 2014, the study empirically examines the impact of second generation involvement on corporate innovation. The authors apply a difference-in-differences research design and a Heckman two-stage least squares regression to relieve the endogeneity concerns.

The research finds a positive relationship between second generation involvement and corporate innovation. This effect is more pronounced among the firms appointing second generation family members with overseas training experience and firms with weak external monitoring. Further analysis shows that the curtailment of related party transactions and the improvement of accounting information quality are important channels.

The findings provide several practical implications for Chinese family firms to survive the succession process and maintain competitive advantages across generations.

First, this study is helpful to understand the strategies adopted by family firms to maintain their long-term competitiveness and pursue continuing growth across generations. Second, the findings are also consistent with the transfer cost hypothesis of Fan et al. (2012) and Bennedsen et al. (2015). Finally, the findings imply that second generation involvement has a substitutive effect for external monitoring mechanisms.