The hole-structural bearing crossbeam plays an important role in high-speed trains. In the service process, however, the long-term fatigue load may lead to structural damage such as cracks, resulting in performance degradation and failure. Ultrasonic guided wave (GW) technology is one of the most effective damage localization methods in structural health monitoring (SHM), with a high damage sensitivity and wide monitoring range. To address the damage localization in bearing crossbeams, a modified reconstruction algorithm for probabilistic inspection of damage (RAPID) based on the corrected probability distribution function is proposed. First, the valid sensor paths affected by damage are obtained using damage index (DI) based on correlation analysis. Then, the positional relationships between valid paths and damage are classified based on the time of flight (TOF). Finally, the damage diagnostic image and localization are obtained by fusion imaging using the corresponding probability distribution functions and shape factors, depending on different types of the path. The effectiveness was verified by numerical simulation and experiment. By taking the crossbeam of the high-speed train as the research object, through the static simulation of the crossbeam stress distribution under load, the damaged hot-spots area is obtained, and the sensor network is designed. And then, the SHM experimental system is constructed to perform damage localization experiments. The localization absolute error was less than 8 mm. Experimental results show that the proposed method can effectively locate the damage position in the crossbeam and has better accuracy and reliability than the traditional RAPID algorithm.

The in-situ observation is of great significance to the study of the growth mechanism and controllability of two-dimensional transition metal dichalcogenides (TMDCs). Here, the differential reflectance spectroscopy (DRS) was performed to monitor the growth of molybdenum disulfide (MoS₂) on a SiO₂/Si substrate prepared by chemical vapor deposition (CVD). A home-built in-situ DRS setup was applied to monitor the growth of MoS₂ in-situ. The formation and evolution of monolayer MoS₂ are revealed by differential reflectance (DR) spectra. The morphology, vibration mode, absorption characteristics and thickness of monolayer MoS₂ have been confirmed by optical microscopy, Raman spectroscopy, ex-situ DR spectra, and atomic force microscopy (AFM) respectively. The results demonstrated that DRS was a powerful tool for in-situ observations and has great potential for growth mechanism and controllability of TMDCs prepared by CVD. To the best of the authors’ knowledge, it was the first report in which the CVD growth of two-dimensional TMDCs has been investigated in-situ by reflectance spectroscopy.

Pipeline structures are widely used in construction, oil transportation and other industrial fields, but with increasing service life, pipeline structures can develop health problems such as perforations and cracks, which can cause pipeline fractures and bring serious safety accidents and economic losses if they aren’t detected at an early stage. Ultrasonic guided wave detection technology has been widely applied in precise damage detection of pipeline structures. To facilitate the grasp of structural health status, a pipeline structural damage imaging method combined with time-of-flight (ToF) compensation and probabilistic reconstruction is proposed. This method analyzes the response signals collected under different states, uses the correlation of the signals as the damage index, constructs an elliptical probability distribution, and determines the axial position of the damage. Afterwards, using ToF as the characteristic parameter, a circular probability distribution is constructed to determine the circumferential location of the damage, and finally the precise location of the damage is obtained. 45 test points were selected for the experiment. The average positioning error is 7 mm. The experimental results show that the method has certain recognition ability for single or multiple damages, and has higher accuracy and stability compared to traditional probability reconstruction algorithms.

Ultrasonic guided wave detection technology is the one of the most effective damage identification method in structural health monitoring, but external factors will cause errors during identifying damage. Based on ring probability distribution and path optimization, a Bayesian fusion damage localization method is proposed. Firstly, the effective band of healthy signal and damaged signal collected by piezoelectric sensor array is selected, and solve correlation. The threshold of the obtained correlation coefficient is screened, so obtain the valid path greatly affected by damage. Then, the time-of-flight (ToF) and damage index (DI) of the valid path are calculated and input into the Bayesian framework as damage characteristics. Finally, the posterior distribution is sampled by Markov chain Monte Carlo (MCMC) method. The frequency distribution of sample points is Gaussian fitted. The peak value of the fitted curve is identified as damage position. Finite element simulation model is established to preliminarily verify the feasibility and effectiveness. Some localization experiments were carried out on aluminum plate and carbon fiber composite plate. The results show that the average localizing error is 6.58 mm and the standard deviation of the error is 3.05 mm, which are less than the localizing error using all paths. By this method, the valid path is about half of all paths, which can not only reduce the impact of invalid paths to improve the stability and robustness of localizing results, but also reduce the amount of data to be processed to realize the purpose of data lightweight.

State monitoring and fault diagnosis of an internal combustion engine are critical for complex machinery safety. In the present study, a high‐frequency vibration system was proposed based on Fiber Bragg Grating (FBG) cantilever sensor and intelligent algorithm. Structural vibration signal containing fault information of engine valves and oil nozzle was identified by FBG sensors and preprocessed using wavelet decomposition and reconstruction. Moreover, vibration energy was taken as fault characteristics. Subsequently, a fault identification model was built based on multiclass υ‐support vector classification (υ‐SVC). Experimental tests on the valve fault and fuel injection advance angle fault were performed and presented to verify the efficacy of the proposed approach. The results here reveal that the proposed method exhibits excellent fault detection performance for ICE fault identification. Furthermore, the proposed method can achieve higher performance than other methods in the fault identification accuracy.