Mine Hoist Bearing Condition Monitoring and Fault Diagnosis System Based on Labview

2011 ◽  
Vol 317-319 ◽  
pp. 1232-1236
Author(s):  
Li Rong Wan ◽  
Guang Yu Zhou ◽  
Cheng Long Wang ◽  
Wen Ming Zhao
Keyword(s):  
Fault Diagnosis ◽  
Program Development ◽  
Condition Monitoring ◽  
Virtual Instrument ◽  
Signal Analysis ◽  
Rolling Bearing ◽  
Bearing Failure ◽  
Bearing Condition Monitoring ◽  
Fault Diagnosis System ◽  
Mine Hoist

By taking full advantage of the technologies of data acquisition, signal analysis and processing and fault diagnosis, this thesis carries out a research on the realization method of mine hoist bearing condition monitoring and fault diagnosis. Firstly, this thesis takes a technical analysis for rolling bearing. Secondly, based on determining the overall framework and using a virtual instrument software (Labview), it carries out a program development of the system. The developed system not only integrates the functions of traditional instruments, but also describes the bearing states and the types of bearing failure accurately according to the running status of the monitored bearings. It provides technical support for the mine hoist repair and maintenance and scientific protection for its safe running.

Application Research of Kalman Filter and SVM Applied to Condition Monitoring and Fault Diagnosis

2011 ◽  
Vol 121-126 ◽  
pp. 268-272 ◽  
Author(s):  
Ke Li ◽  
Yue Lei Zhang ◽  
Zhi Xiong Li
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Rolling Bearing ◽  
Support Vector ◽  
Measured Signal ◽  
Fault Features ◽  
Bearing Condition Monitoring ◽  
Filtering Technique

In the condition monitoring and fault diagnosis, useful information about the incipient fault features in the measured signal is always corrupted by noise. Fortunately, the Kalman filtering technique can filter the noise effectively, and the impending system fault can be revealed to prevent the system from malfunction. This paper has discussed recent progress of the Kalman filters for the condition monitoring and fault diagnosis. A case study on the rolling bearing condition monitoring and fault diagnosis using Kalman filter and support vector machine (SVM) has been presented. The analysis result showed that the integration of the Kalman filter and SVM was feasible and reliable for the rolling bearing condition monitoring and fault diagnosis and the fault detection rate was over 96.5%.

Refinement Envelope Analysis for Small Failures of Rolling Bearing

2011 ◽  
Vol 383-390 ◽  
pp. 2622-2627
Author(s):  
Shu Shang Zhao ◽  
Juan Juan Pan
Keyword(s):  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Vibration Spectrum ◽  
Rolling Bearing ◽  
Bearing Failure ◽  
Wavelet Filter ◽  
Bearing Defect ◽  
Small Fault ◽  
Bearing Vibration ◽  
Envelope Spectrum

In the rotating machinery, rolling bearing is used widespread in many places. Due to various reasons, there is great dispersion in the life of bearing. Therefore, it is very important to have fault diagnosis of rolling bearing, especially the small fault diagnosis of rolling bearing. According to the characteristics of rolling bearing defect signals and the features integrated with wavelet transform, Hilbert transform and envelope spectrum detailed analysis, this text proposed a method to judge the bearing failure. At first, bearing vibration signals are reconstructed from wavelet filter and envelope signals are obtained by Hilbert transform and then vibration spectrum is obtained from the refining envelope spectrum. Bearing failure is judged from the refining frequency spectrum. Bearing failure is also estimated by experiment to verify the correctness of theoretical analysis.

scholarly journals A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Gu ◽  
Jiawei Cao ◽  
Xin Song ◽  
Jian Yao
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bp Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Approach ◽  
Fault Diagnosis System ◽  
Original Time

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.

Bearing Scuffing Detection and Condition Monitoring Using Virtual Instrumentation

2014 ◽  
Vol 657 ◽  
pp. 604-608 ◽  
Author(s):  
Carmen Bujoreanu ◽  
Florin Breabăn
Keyword(s):  
Condition Monitoring ◽  
Virtual Instrument ◽  
Friction Torque ◽  
Diagnostic Methods ◽  
Virtual Instrumentation ◽  
Friction Forces ◽  
Data Acquisition Board ◽  
Bearing Condition Monitoring ◽  
Braking Torque ◽  
Vibration Signature

Bearing condition monitoring confronts the most machine users. Diagnostic methods used to include bearing problems represent one of the most important challenges. The scuffing phenomenon initiation of the bearing elements produces an important increase in the vibration level and can be emphasized by the analysis of the bearing friction forces which are the most sensitive indicator of the bearing failure. Commonly used technique for damage detection is the vibration signature analysis that must be carefully utilized in conjunction with the friction torque monitoring through the strain gauges measurements. In order to detect the scuffing onset, the paper presents an experimental setup for the scuffing tests performed on a 7206 ball bearing. A virtual instrument monitoring the friction force respectively the braking torque was created. An accelerometer captures the signal from the bearing outer ring then it is processed using PCI-4451 National Instruments data acquisition board and LabVIEW soft.

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