Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode’s power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO) algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.

Previously, we screened a proteoglycan for anti-hyperglycemic, named FYGL, from Ganoderma Lucidum. For further research of the antidiabetic mechanisms of FYGL in vivo, the glucose homeostasis, activities of insulin-sensitive enzymes, glucose transporter expression and pancreatic function were analyzed using db/db mice as diabetic models in the present work. FYGL not only lead to a reduction in glycated hemoglobin level, but also an increase in insulin and C-peptide level, whereas a decrease in glucagons level and showed a potential for the remediation of pancreatic islets. FYGL also increased the glucokinase activities, and simultaneously lowered the phosphoenol pyruvate carboxykinase activities, accompanied by a reduction in the expression of hepatic glucose transporter protein 2, while the expression of adipose and skeletal glucose transporter protein 4 was increased. Moreover, the antioxidant enzyme activities were also increased by FYGL treatment. Thus, FYGL was an effective antidiabetic agent by enhancing insulin secretion and decreasing hepatic glucose output along with increase of adipose and skeletal muscle glucose disposal in the late stage of diabetes. Furthermore, FYGL is beneficial against oxidative stress, thereby being helpful in preventing the diabetic complications.

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To solve the intractable problems of optimal rank truncation threshold and dominant modes selection strategy of the standard dynamic mode decomposition (DMD), an improved DMD algorithm is introduced in this paper. Distinct from the conventional methods, a convex optimization framework is introduced by applying a parameterized non-convex penalty function to obtain the optimal rank truncation number. This method is inspirited by the performance that it is more perfectible than other rank truncation methods in inhibiting noise disturbance. A hierarchical and multiresolution application similar to the process of wavelet packet decomposition in modes selection is presented so as to improve the algorithm’s performance. With the modes selection strategy, the frequency spectrum of the reconstruction signal is more readable and interference-free. The improved DMD algorithm successfully extracts the fault characteristics of rolling bearing fault signals when it is utilized for mechanical signal feature extraction. Results demonstrated that the proposed method has good application prospects in denoising and fault feature extraction for mechanical signals.