SEQUENTIAL DIAGNOSIS FOR A CENTRIFUGAL PUMP BASED ON FUZZY NEURAL NETWORK

A sequential diagnosis method is proposed based on a fuzzy neural network realized by ＂the partially-linearized neural network （PNN）＂, by which the fault types of rotating machinery can be precisely and effectively distinguished at an early stage on the basis of the possibilities of symptom parameters. The non-dimensional symptom parameters in time domain are defined for reflecting the features of time signals measured for the fault diagnosis of rotating machinery. The synthetic detection index is also proposed to evaluate the sensitivity of non-dimensional symptom parameters for detecting faults. The practical example of condition diagnosis for detecting and distinguishing fault states of a centrifugal pump system, such as cavitation, impeller eccentricity which often occur in a centrifugal pump system, are shown to verify the efficiency of the method proposed in this paper.

Time sequential influence maximization algorithm based on neighbor node influence

In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.

Wideband and high-gain BeiDou antenna with a sequential feed network for satellite tracking

BeiDou-3 navigation satellite system was officially opened in 2020.While bringing high-performance services to people around the world,the navigation system requires well-designed BeiDou antennas.In this paper,we propose a wideband circularly polarized high-performance BeiDou antenna.The antenna realizes wideband circularly polarized radiation through a four-port sequential feed network,and the phase imbalance of the feed network from 1.05 to 1.80 GHz is less than 7°.The manufactured antenna demonstrates a return loss of more than 13 dB and an axial ratio<3 dB over the entire global navigation satellite system(GNSS)frequency band.The right-handed circular polarization(RHCP)gain of the proposed antenna is greater than 4 dB in the GNSS low-frequency band and can reach more than 7.1 dB in the high-frequency band.Dimension of the proposed antenna is 120 mm×120 mm×20 mm,i.e.,0.54λo×0.54λo×0.09λo,whereλo is the wavelength of the center frequency.The proposed antenna connected to a GNSS receiver has tracked 12 BeiDou satellites with C/N0 ratios of GNSS signals greater than 30 dB.Such a high-performance antenna provides a basis for high-quality positioning services.