Space-time frequency spectra analyses of the unsteady cavitating vortical flows inside a mixed-flow pump

To reduce cavitation-induced pressure fluctuations in a mixed-flow pump under impeller inflow distortion, the dynamic pressure signal at different monitoring points of a mixed-flow pump with a dustpan-shaped inlet conduit under normal and critical cavitation conditions was collected using high-precision digital pressure sensors. Firstly, the nonuniformity of the impeller inflow caused by inlet conduit shape was characterized by the time–frequency-domain spectra and statistical characteristics of pressure fluctuation at four monitoring points (P4–P7) circumferentially distributed at the outlet of the inlet conduit. Then, the cavity distribution on the blade surface was captured by a stroboscope. Lastly, the characteristics of cavitation-induced pressure fluctuation were obtained by analyzing the time–frequency-domain spectra and statistical characteristic values of dynamic pressure signals at the impeller inlet (P1), guide vanes inlet (P2), and guide vanes outlet (P3). The results show that the flow distribution of impeller inflow is asymmetric. The pav values at P4 and P6 were the smallest and largest, respectively. Compared with normal conditions, the impeller inlet pressure is lower under critical cavitation conditions, which leads to low pav, pp-p and a main frequency amplitude at P1. In addition, the cavity covered the whole suction side under H = 13.6 m and 15.5 m, which led the pp-p and dominant frequency amplitude of pressure fluctuation at P2 and P3 under critical cavitation to be higher than that under normal conditions.

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Correlation research of rotor–stator interaction and shafting vibration in a mixed-flow pump

Journal of Low Frequency Noise Vibration and Active Control ◽

10.1177/1461348419836530 ◽

2019 ◽

Vol 39 (1) ◽

pp. 72-83 ◽

Cited By ~ 1

Author(s):

Wei Li ◽

Leilei Ji ◽

Weidong Shi ◽

Yongfei Yang ◽

Muhammad Awais ◽

...

Keyword(s):

Interaction Effect ◽

Pressure Fluctuation ◽

Pressure Pulsation ◽

Mixed Flow ◽

Time Frequency ◽

Shaft System ◽

Blade Passing Frequency ◽

Axis Orbit ◽

Rotor Stator Interaction ◽

Flow Pump

In order to study the shaft system vibration of mixed-flow pump under rotor–stator interaction, the unsteady pressure fluctuation characteristics are measured and the rotor axis orbit obtained based on the Bentley 408 data acquisition system. The relationship between pressure fluctuation and vibration characteristics of shaft system at the blade passing frequency is analyzed. The results show that the pressure fluctuation amplitude is the largest and the rotor–stator interaction effect is the most obvious in the middle of the impeller. Along the direction of the main stream, the velocity energy is converted into pressure energy, the rotor–stator interaction effect is gradually weakened, and the main frequency of the pressure pulsation gradually turns from the 4 X frequency to the 1 X frequency of the impeller rotation frequency. The hydraulic stirring vibration and other factors lead to jagged sharp corners on the original axis orbit. The axis orbit of 1 X frequency is an ellipse with little difference between long and short axis while the 2 X frequency is the opposite, from which the existence of arcuate rotary whirl and misalignment phenomenon of the rotor can be judged. Combined with time–frequency characteristics of pressure pulsation, it can be found that the hydraulic imbalance has a great influence on the vibration of the shafting, while the rotor–stator interaction at the blade passing frequency takes the second place, which is the main factor of inducing the 4 X frequency vibration of the axis orbit. This study targets is that providing practical guidance for improving operation stability and preventing the vibration failure of the mixed-flow pump.

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Statistical space-time-frequency characterization of MIMO shallow water acoustic channels

OCEANS 2009 ◽

10.23919/oceans.2009.5422278 ◽

2009 ◽

Author(s):

Alenka G. Zajic

Keyword(s):

Shallow Water ◽

Space Time ◽

Time Frequency ◽

Statistical Space

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Improving Space-Time-Frequency MIMO-OFDM with ICI Self-Cancellation Scheme using Least Square Error Estimator

Scientific Research Journal ◽

10.24191/srj.v12i1.5436 ◽

2015 ◽

Vol 12 (1) ◽

pp. 25

Author(s):

Nur Farahiah Ibrahim ◽

Zahari Abu Bakar ◽

Azlina Idris

Keyword(s):

Channel Estimation ◽

Orthogonal Frequency Division Multiplexing ◽

Symbol Error Rate ◽

Space Time ◽

Least Square ◽

Error Estimator ◽

Frequency Diversity ◽

Time Frequency ◽

Mimo Ofdm ◽

Subcarrier Mapping

Channel estimation techniques for Multiple-input Multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based on comb type pilot arrangement with least-square error (LSE) estimator was investigated with space-time-frequency (STF) diversity implementation. The frequency offset in OFDM effected its performance. This was mitigated with the implementation of the presented inter-carrier interference self-cancellation (ICI-SC) techniques and different space-time subcarrier mapping. STF block coding in the system exploits the spatial, temporal and frequency diversity to improve performance. Estimated channel was fed into a decoder which combined the STF decoding together with the estimated channel coefficients using LSE estimator for equalization. The performance of the system was compared by measuring the symbol error rate with a PSK-16 and PSK-32. The results show that subcarrier mapping together with ICI-SC were able to increase the system performance. Introduction of channel estimation was also able to estimate the channel coefficient at only 5dB difference with a perfectly known channel.

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Deep Learning Approach for Vibration Signals Applications

Sensors ◽

10.3390/s21113929 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3929

Author(s):

Han-Yun Chen ◽

Ching-Hung Lee

Keyword(s):

Surface Roughness ◽

Tool Wear ◽

Parameters Optimization ◽

Frequency Spectra ◽

Vibration Signals ◽

Bearing Faults ◽

Time Frequency ◽

And Performance ◽

The One ◽

Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

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