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<p><span style="font-family: 宋体; font-size: medium;">In order to realize the safe traceability of the whole life cycle of the “Old Godmother Flavor Food”, the RFID (Radio Frequency Identification Devices) technology is applied to the data acquisition of raw material purchasing, production processing, warehousing management, logistics and transportation. A kind of special food safety traceability network model was put forward based on RFID technology, and the RFID label EPC encoding rules are designed on “Old Godmother Flavor Food”; according to the demand analysis of characteristic food safety traceability system, the system design and implementation are carried out based on B/S and C/S architecture.</span></p>

As crucial equipment in the industrial field, the stable operation of centrifugal pumps has drawn noteworthy attention. Relevant studies in the open literature have shown that intense pressure fluctuations have a major effect on the reliability and lifetime of centrifugal pumps. In the present paper, the pressure fluctuations in the centrifugal pumps are discussed in detail from different perspectives. The details of the studies are as follows. Firstly, the pressure fluctuation characteristics in centrifugal pumps are studied without considering clearance flow. Secondly, the pressure fluctuation property is investigated in detail for the pumps, with consideration for clearance flow. The pressure fluctuation characteristics in the wear ring, the pump-chamber clearance region, and the main stream region are studied, and the effect of clearance flow on the external performance of the pumps is analyzed. Thirdly, measures to reduce the pressure fluctuations and forces are summarized to improve the operational reliability of centrifugal pumps. Finally, conclusions and future research perspectives in the field of centrifugal pumps are presented. This review presents the research highlights and progress in the field of pressure fluctuations, which is beneficial to the stable operation of centrifugal pumps in engineering.

Intense fluid-dynamic interaction at the impeller outlet strongly affects the unsteady flow and pressure stability within the centrifugal pump. In order to have a better understanding of the pressure fluctuation of centrifugal pumps, a numerical calculation is carried out by using the RNG k-epsilon turbulence model under various flow rates. The numerical calculation results are compared with the experimental results in order to verify the reliability of the calculation model. The amplitude and frequency distribution of pressure fluctuation at the impeller outlet is obtained and analyzed in the time and frequency domain. The research results show that the blade passing frequency is the dominant frequency of the pressure fluctuation. And the pressure fluctuation is a periodic fluctuation. As the flow rate decreases, the periodicity of the pressure fluctuation decreases. Besides, the amplitude and intensity of pressure fluctuation are closely related to flow rate and spatial location. At the low flow rate, the amplitude of pressure fluctuation in the time domain and frequency domain is enlarged greatly, especially near the tongue region. The pressure difference distribution on both sides of the blade surface is extremely uneven, and the pressure changes significantly.

To enhance fault characteristics and improve fault detection accuracy in bearing vibration signals, this paper proposes a fault diagnosis method using a wavelet packet energy spectrum and an improved deep confidence network. Firstly, a wavelet packet transform decomposes the original vibration signal into different frequency bands, fully preserving the original signal’s frequency information, and constructs feature vectors by extracting the energy of sub-frequency bands via the energy spectrum to extract and enhance fault feature information. Secondly, to minimize the time-consuming manual parameter adjustment procedure and increase the diagnostic accuracy, the sparrow search algorithm–deep belief network method is proposed, which utilizes the sparrow search algorithm to optimize the hyperparameters of the deep belief networks and reduce the classification error rate. Finally, to verify the effectiveness of the method, the rolling bearing data from Casey Reserve University were selected for verification, and compared to other commonly used algorithms, the proposed method achieved 100% and 99.34% accuracy in two sets of comparative experiments. The experimental results demonstrate that this method has a high diagnostic rate and stability.