Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

The fault vibration signal of a bearing has nonstationary and nonlinear characteristics and can be regarded as the combination of multiple amplitude- and frequency-modulation components. The envelope of a single component contains the fault characteristics of a bearing. Local characteristic-scale decomposition (LCD) can decompose the vibration signal into a series of multiple intrinsic scale components. Some components can clearly reflect the running state of a bearing, and fault diagnosis is conducted according to the envelope spectrum. However, the conventional LCD takes a single-channel signal as the research object, which cannot fully reflect the characteristic information of the rotor, and the analysis results based on different channel signals of the same section will be inconsistent. To solve this problem, based on full vector spectrum technology, the homologous dual-channel information is fused. A vector LCD method based on cross-correlation coefficient component selection is given, and a simulation analysis is completed. The effectiveness of the proposed method is verified by simulated signals and experimental signals of a bearing, which provides a method for bearing feature extraction and fault diagnosis.

Coordination and Compensation Model of E-Commerce Dual-Channel Supply Chain Based on Optimized Genetic Algorithm

Due to the conflict between traditional channels and electronic channels in the e-commerce dual-channel supply chain, retailers are threatened and need to be compensated in some way. Based on this, an e-commerce dual-channel supply chain coordination compensation model based on optimized genetic algorithm is designed. Based on the problem description and basic assumptions, analyze the manufacturer’s profit and the retailer’s maximum profit in the case of centralized decision-making and decentralized decision-making. The genetic algorithm is optimized by introducing a collaborative genetic operator, and the optimized genetic algorithm is used to obtain dual e-commerce channels. The maximum profit of the supply chain, so far, the model design is completed. Through comparative experiments, the optimized genetic algorithm used in the model is compared with two traditional algorithms. Experimental results show that the proposed algorithm takes shorter iteration time to solve the problem, its convergence is better, and it can effectively obtain a global optimal solution instead of a local optimal solution.