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The effect of early fault vibration signals from rotating machinery is weak and easily drowned out by intensive noise. Thus, it is still a great challenge to perform early fault diagnosis. An intelligent early fault diagnosis method for rotating machines is proposed based on Variational Mode Decomposition (VMD) parameter optimization and Deep Multi-Kernel Extreme Learning Machine (DMKELM). First, the iterative chaotic mapping, nonlinear convergence factor and inertia weight are introduced into the whale optimization algorithm (WOA). Then, an improved WOA (IWOA) is presented and utilized to optimize VMD parameters. The optimized VMD (OVMD) combined with the sampling entropy is used to denoise and reconstruct the signals. Finally, the mish function is served as the activation function. Meanwhile, the radial basis kernel function (RBF) and polynomial kernel (PK) are introduced to construct the mixed kernel function. The mixed kernel assignment is applied to replace the random assignment, and the DMKELM is presented to enhance the classification performance and generalization capability of the model, and DMKELM is used for early intelligent fault diagnosis. Two experiments on bearings and gears demonstrated that the fault diagnosis accuracy is 99% and 98.5%, respectively, and the fault diagnosis accuracy is high. It shows that this method has great superiority in early fault diagnosis of rotating machinery.

With the gradual expansion of the scale of urban rail transit, its safety and cost issues have aroused widespread concern. Aiming at the common phenomena of "over-maintenance" or "under-maintenance" in urban rail trains, the imperfect preventive maintenance model with a two-level inspection strategy based on a three-stage failure process for train bogie system is proposed. Firstly, three parameters of Weibull distribution of components are obtained by analyzing historical fault data. According to the reliability curve and failure rate obtained by Weibull three parameters, the single components are divided into three stages: normal driving stage, initial deterioration stage and severe deterioration stage, and the three stages are identified by a two-level inspection. Among them, the normal driving stage and the severe deterioration stage are identified by the low-level inspection, and at the same time, it is possible to identify the initial deterioration stage, while all stages are identified accurately by the high-level inspection. Secondly, maintenance economy and train availability are taken as the optimization condition of the imperfect maintenance strategy, and the obtained optimal imperfect maintenance interval is taken as the high-level inspection time in the two-level inspection. Then an inspection threshold is proposed to determine the functional relationship between the high-level inspection and the low-level inspection. With the minimum expected cost as the goal, the expected cost of single components is calculated through different low-level inspection time, inspection threshold and preventive repair threshold. Finally, taking certain city urban rail train bogie system as an example, it is found that the minimum expected cost of axle box subassembly is CNY 43.28 a day, the maintenance period is 428 days. The minimum expected cost of gear box subassembly is CNY 27.26 a day, and the maintenance period is 651 days. Compared with the similar models, the maintenance period is prolonged and the expected cost is reduced, which verifies the effectiveness and superiority of the model and provides a theoretical basis for scientific maintenance.

Background: PM exposure can lead to myocardial hypertrophy, with a potential contribution via DNA methylation. Myocardial lipotoxicity is closely related to myocardial hypertrophy. But, myocardial lipotoxicity caused by PM has not been reported. PDGFRβ, a platelet-derived growth factor receptor, is also essential for normal cardiovascular development. However, It is unclear the role of PM-induced PDGFRβ methylation in myocardial hypertrophy and myocardial lipotoxicity. We investigated the effect of PDGFRβ methylation induced by PM on myocardial hypertrophy and myocardial lipotoxicity. Results: PDGFRβ methylation caused by PM decreased PDGFRβ mRNA and protein expression in C57BL/6J mouse hearts. Thus inhibiting gene expression in its downstream pathway, ultimately leading to cardiac hypertrophy. Disturbances of myocardial lipid metabolism caused by PM in AC16 cells and C57BL/6J mouse hearts were also observed. High expression of PDGFRβ in neonatal rat primary cardiomyocytes was found to activate its downstream pathway and ameliorate the effects of PM-induced cardiac hypertrophic activity. At the same time, adenovirus was used to induce high expression of PDGFRβ in C57BL/6J mice. It was found that PDGFRβ not only improved PM-induced cardiac hypertrophy, but also alleviated PM-induced myocardial lipotoxicity. Conclusions: PDGFRβ gene methylation may be one of the potential biomarkers of myocardial hypertrophy induced by PM exposure. And high expression of PDGFRβ may be a potential way to prevent myocardial hypertrophy and cardiac lipid metabolism disorder caused by PM exposure in mice.