Study on Remote Condition Monitoring and Fault Diagnosis System for Rotating Machinery Based on LabVIEW

2012 ◽  
Vol 430-432 ◽  
pp. 1939-1942 ◽  
Author(s):  
Chuan Hui Wu ◽  
Yan Gao ◽  
Yu Guo
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Signal Acquisition ◽  
Diagnosis System ◽  
Time Frequency ◽  
Machinery Condition Monitoring ◽  
Temperature Signal ◽  
Fault Diagnosis System

In order to suit the demand of monitoring and fault diagnosis of modern small and medium machinery devices better, this paper discusses the development of machinery condition monitoring and fault diagnosis system of good universality and strong expansibility using LabVIEW. Mainly illuminates vibration signal, temperature signal and electric current signal acquisition module using NI data acquisition hardware; signal analysis module in time domain, frequency domain and joint time–frequency domain using signal processing technology. DataSocket, database and fuzzy diagnosis technique have been utilized enabling this system to monitor and diagnose machinery fault remotely.

Based on the Online Monitoring and Fault Diagnosis System of the Main Hoist for Mine

2010 ◽  
Vol 42 ◽  
pp. 250-254
Author(s):  
Zhi Yong Pan ◽  
Quan Cai Wang ◽  
Wei Hong Ren
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Online Monitoring ◽  
Signal Acquisition ◽  
Diagnosis System ◽  
Current Voltage ◽  
Fault Diagnosis System ◽  
Real Time Database ◽  
Signal Acquisition And Processing ◽  
Main Hoist

According to the reality, an online monitoring and fault diagnosis system of the main hoist for Mine was designed in this article. The system adopts the signal acquisition and processing, fault diagnosis, Web visualization, network real-time database and other related technologies, Real-time monitoring the current, voltage, temperature, speed, vibration and other parameters of the main elevators to Achieve the goals that Increasing efficiency by downsizing, protecting the safe operation of equipment, reducing the maintenance costs.

Gear fault diagnosis based on time–frequency domain de-noising using the generalized S transform

2017 ◽  
Vol 24 (15) ◽  
pp. 3338-3347 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Time Frequency ◽  
Transmission Modes ◽  
S Transform ◽  
Mining Machinery ◽  
Gear Fault ◽  
Generalized S Transform ◽  
Gear Fault Diagnosis

Gears are the most important transmission modes used in mining machinery, and gear faults can cause serious damage and even accidents. In the work process, vibration signals are influenced not only by friction, nonlinear stiffness, and nonstationary loads, but also by strong noise. It is difficult to separate the useful information from the noise, which brings some trouble to the fault diagnosis of mining machinery gears. The generalized S transform has the advantages of the short time Fourier transform and wavelet transform and is reversible. The time–frequency energy distribution of the gear vibration signal can be accurately presented by the generalized S transform, and a time–frequency filter factor can be constructed to filter the vibration signal in the time–frequency domain. These characteristics play an important role when the generalized S transform is used to remove the noise in the time–frequency domain. In this paper, a new gear fault diagnosis based on the time–frequency domain de-noising is proposed that uses the generalized S transform. The application principle, method steps, and evaluation index of the method are presented, and a wavelet soft-threshold filtering method is implemented for comparison with the proposed approach. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a gear with a tooth crack. Our analyses also indicate that the proposed method can be used for fault diagnosis of mining machinery gears.

SOA-based remote condition monitoring and fault diagnosis system

2009 ◽  
Vol 46 (9-12) ◽  
pp. 1191-1200 ◽  
Author(s):  
Fagang Zhao ◽  
Jin Chen ◽  
Guangming Dong ◽  
Lei Guo
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Diagnosis System ◽  
Fault Diagnosis System

Study on open equipment condition monitoring and fault diagnosis system based on Internet

2005 ◽  
Author(s):  
Hui-Long He ◽  
Tai-Yong Wang ◽  
Hui Deng ◽  
Ju-Xiang Zeng ◽  
Guo-Feng Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Diagnosis System ◽  
Fault Diagnosis System

scholarly journals Fault Diagnosis System of Rotating Machinery Vibration Signal

2011 ◽  
Vol 15 ◽  
pp. 671-675 ◽  
Author(s):  
Lei You ◽  
Jun Hu ◽  
Fang Fang ◽  
Lintao Duan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Diagnosis System ◽  
Fault Diagnosis System

Application of Virtual Instrument on Condition Monitoring and Fault Diagnosis System of the Rotating Machinery

2012 ◽  
Vol 542-543 ◽  
pp. 161-164
Author(s):  
Yong Ying Du ◽  
Yu Ning Wang ◽  
Ming Ang Yin
Keyword(s):  
Fault Diagnosis ◽  
Data Acquisition ◽  
Frequency Domain ◽  
Condition Monitoring ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Rotating Machinery ◽  
Waveform Data ◽  
Time Frequency ◽  
Acceleration Sensors

In the paper it can be easier to realize the acquisition of the rotating machinery vibration signal and condition monitoring through the configuration the platform of virtual instrumentation. For the data acquisition it is enough to be plus with two acceleration sensors and a counter. The system is divided into parameter setting module, data acquisition, storage and display module, amplitude domain analysis module, time-domain analysis module, frequency domain analysis module, time-frequency domain analysis module and fault diagnosis module. The signal acquisition is got by using the PCI-6024E data acquisition card. And it is can be saved as binary data stream files and waveform data file according to the requirements of the sequence data processing. Signal analysis is conducted by using LabVIEW software and draw out the vibration spectrum diagram in order to achieve fault diagnosis of rotating machinery.

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