Optimal Design of Data Acquisition System for Fault Diagnosis of Hydraulic Machinery

Author:

Qiu H,Jin B,Zhao W,Wang Z

Abstract

Abstract Fault diagnosis of hydraulic machinery has high requirements on the synchronization of monitoring data and the anti-interference of monitoring equipment. The performance and data quality of the existing operation monitoring equipment of the unit cannot meet the requirements. At the same time, due to the variety of sensors and the different wiring methods of different types of sensors, the preparation work such as wiring of data acquisition equipment in the test is complicated. This paper analyzes the application requirements of fault diagnosis data and the problems existing in the conventional data collection process. A data collection solution specified for diagnosis of hydraulic machinery, which better solves the problem of data synchronization, is designed. The new designed system has high sampling accuracy and anti-interference characteristics, as well as the advantages of strong capability and simple equipment deployment. The proposed system has been applied in multiple power stations, pumping stations and pump test platforms. The relevant functions have been effectively verified.

Publisher

IOP Publishing

Reference5 articles.

1. Condition monitoring of pump-turbines. New challenges;Egusquiza;Measurement,2015

2. Condition monitoring of a prototype turbine. Description of the system and main results;Valero;Journal of Physics: Conference Series,2017

3. On the use of Vibrational Hill Charts for improved condition monitoring and diagnosis of hydraulic turbines;Zhao;Struct. Heal. Monit.,2022

4. On the detection of natural frequencies and mode shapes of submerged rotating disk-like structures from the casing;Presas;Mech. Syst. Signal Process.,2015

5. Application of cepstrum and neural network to bearing fault detection;Hwang;J. Mech. Sci. Technol.,2009

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