scholarly journals Centrifugal Pump Bearing Fault Diagnose Based on Time-Frequency Domain Analysis and BP-Neural Network

2019 ◽  
Vol 1284 ◽  
pp. 012007
Author(s):  
Wang Chaowei ◽  
Zhou Guojing ◽  
Wu Qian ◽  
Ding Lvhui ◽  
Wei Yi
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Centrifugal Pump ◽  
Bp Neural Network ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Fault Diagnose ◽  
Time Frequency ◽  
Bearing Fault

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

A Portable Detecting and Analytic Instrument of Vibration Signal

2012 ◽  
Vol 442 ◽  
pp. 305-308
Author(s):  
Jian Wei Li ◽  
Ling Wang ◽  
Hong Mei Zhang
Keyword(s):  
Frequency Domain ◽  
Virtual Instrument ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Time Frequency ◽  
Notebook Computer ◽  
Usb Bus ◽  
Data Acquisition Card

It is often needed in engineering that detecting and analyzing vibration signal of some equipment. To meet the requirement, a portable detecting and analytic instrument was designed using virtual instrument concept. In the instrument, notebook computer was used as the platform of hardware. Vibration signal was obtained by integrated piezoelectric acceleration sensor (DTS0104T), and was transferred to a notebook computer through data acquisition card (NI USB-6210) based on USB bus. The software, running on the notebook computer, was developed under LabVIEW. Vibration signal could be displayed on screen, recorded in disk or printed by printer, retrieved, and analyzed. The analysis functions of the instrument include: time-domain analysis, frequency-domain analysis, time-frequency domain analysis, and correlation analysis. The instrument is compact, portable, powerful, and with friendly interfaces, has broad application prospects.

scholarly journals Diagnosis on Surface Failure in Gear Equipment Using Time - Frequency Domain Analysis

Engineering ◽  
2011 ◽  
Vol 03 (08) ◽  
pp. 851-858 ◽  
Author(s):  
Yuji Ohue ◽  
Toshiya Kounou ◽  
Katsuhiko Yazama
Keyword(s):  
Frequency Domain ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Time Frequency ◽  
Surface Failure
Sign in / Sign up

Export Citation Format

Share Document