Rolling bearing fault diagnosis is crucial to improve industrial safety and reliability. In recent years, intelligent fault diagnosis method represented by deep learning (DL) has been receiving increasing attention. In order to ameliorate the full training of the deep network, improve the model accuracy, and perfect the analysis of mechanical vibration signals with huge amount of information, a multi-scale attention mechanism residual network (MSA-ResNet) fault diagnosis method is proposed in this paper. First, an attention mechanism block is introduced to construct a new type of residual block combination. Second, a multi-scale structure is constructed by choosing an appropriate convolution kernel size. Finally, the overall framework of MSA-ResNet is constructed for efficient training and failure pattern recognition. The MSA-ResNet algorithm introduces an attention mechanism in each residual module of the residual network (ResNet), which improves the sensitivity to features. The features of different scales are obtained through the multi-scale convolution kernel, and the multi-scale feature extraction of complex nonlinear mechanical vibration signals is realized. The processing of original vibration signal rarely involves artificial interference, which is more conducive to industrial application of the proposed method. Diagnostic experiments are conducted on bearing datasets from the Case Western Reserve University (CWRU) and the Machinery Failure Prevention Technology (MFPT) to verify the effectiveness of the proposed method. The results illustrating the rolling bearing fault diagnosis method based on MSA-ResNet have advantages in multi-scale feature extraction, noise immunity, and fault classification accuracy.

Geothermal resources are clean energy with a great potential for development and utilization. Gaoyang geothermal field, located in the middle of the raised area in Hebei province, China, is one of the three major geothermal fields in the Xiong’an New Area. With the development of the Xiong’an New Area, this geothermal field has become a research hotspot. According to the latest survey, the bottom-hole temperature of the D34 and D35 areas in the north of Gaoyang geothermal field is 149°C and 116°C, respectively, which indicates favorable target areas for geothermal exploitation The circulation mechanism and chemical origins of geothermal fluid are unclear in the Gaoyang geothermal field, which hinders the evaluation of geothermal resources in this region. Therefore, water chemistry and isotopic studies were performed on the Gaoyang geothermal fluid to understand the genesis of the Gaoyang geothermal field. Piper trigram and Na-K-Mg software were also used to explore the genesis of the underground hot water. Combined with stratigraphy and geothermal geology, it can be concluded that the primary hydrochemical type of the Gaoyang geothermal field is Na-HCO3·Cl. In the process of upward migration of geothermal water, leaching and cation alternating adsorption took place, and finally, high TDS geothermal water was formed. Our results are helpful for geothermal resource evaluation and utilization and provide scientific guidance for the sustainable development of geothermal resources.

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