Bolt connections are subjected to severe service conditions, such as cyclic loading and mechanical shock, leading to loosening failure. Commonly, the degradation of the bolt pretightening state is a multistage process, consisting of the tight contact stage (TCS) and significant loosening stage. Therefore, utilizing a single model to monitor the pretightening state in the full degradation stage is difficult. Here, a method based on nonlinear Lamb waves to identify the TCS of bolts and quantitatively monitor the pretightening state to bolt loosening is proposed. In the proposed method, phase reversal technology is first adopted to enhance the sensitivity and reduce the calculation errors of nonlinear damage indexes for bolt loosening in the TCS, and then the phase reversal relative nonlinear coefficient (PRC) is constructed. This indicator overcomes the disadvantage that linear indicators are insensitive to early loosening and realizes the identification of critical points between the TCS and the significant loosening stage, which provides a prerequisite for constructing a staged loosening monitoring model. After the TCS is determined, a quantitative monitoring model for loosening, which fuses seven nonlinear damage indexes, is established based on canonical correlation forests to evaluate the pretightening state. To verify the effectiveness of the method, an experimental study of bolts is carried out, the lamb signals under different loosening states are measured, and the monitoring effects of different indicators are compared and analyzed. The comparison results show that the proposed method has higher accuracy than conventional approaches.

During the operation of high-speed trains, components are prone to fatigue crack damage under the action of cyclic loading and extreme temperature environments. Temperature change has become an important factor for monitoring the health of high-speed train structures. A new fatigue crack size quantification method under a variable temperature environment based on a Gaussian mixture model (GMM) is presented in this paper. A series of damage indexes are proposed to characterize the interaction mechanism between the signal and crack under temperature change. Moreover, multidimensional damage indexes extracted by the fusion of dimensionality reduction technology are used to establish a complete working condition baseline GMM database. The service temperature of the structure is determined according to the maximum similarity criterion between the baseline GMMs and detection GMMs. Furthermore, the quantitative crack length detection model is established at each temperature. To validate the effectiveness of the method, fatigue crack experiments in a variable temperature environment are carried out. The verification results show that this method can detect fatigue crack growth at different temperatures.

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