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Incomplete diagnostic information, inadequate multisource sensor information, weak diagnosis models, and subjective experience result in difficulty in predicting rotating machinery faults. To overcome these limitations, we proposed a multiple domain and heterogeneous information entropy fusion model based on an optimisation of bearing fault diagnosis. The spatiotemporal approach uses a multiscene domain fusion strategy based on heterogeneous sensors (HSMSF) to extract feature fusion strategies and analyses the characteristics of the bearing fault features by multichannel processes with convolutional neural networks to vibration signals. After the mapping of multiple quality characteristics, the high-quality features are combined with each other, and the adaptive entropy weighted fusion method is used to analyse and make decisions on sensor information from different detection points. Nineteen key model parameters that were required for HSMSF construction were selected by adaptive optimisation using the chaos elitist modified sparrow search algorithm (CEI-SSA), and a self-learning diagnostic model that is suitable for multiple detection points was constructed. The validity and feasibility of the proposed fault diagnosis method were verified experimentally on two common reference-bearing datasets, CWRU and IMS, and compared with other fault diagnosis methods.

Linearity is one of the most important static indexes of hemispherical resonator gyro (HRG). Linearity directly affects the measurement error of the HRG in the full range, thus determining the effective range of the HRG. Through finite element analysis of the linearity of the HRG under force-rebalance mode, we can provide a theoretical basis for compensating the linearity by circuit or software, improving the linearity of the gyro combination and increasing the maximum effective range of the gyro combination.