Fault Diagnosis of Bearing Based on the Ultrasonic Amplitude Histogram

2012 ◽  
Vol 479-481 ◽  
pp. 1361-1364
Author(s):  
Lian Cheng Su ◽  
Yan E Shi ◽  
Xiao Li Li
Keyword(s):  
Fault Diagnosis ◽  
Vibration Amplitude ◽  
Ultrasonic Method ◽  
Histogram Method ◽  
Ultrasonic Signals ◽  
Rotating Machine ◽  
Amplitude Histogram ◽  
Diagnosis Method ◽  
Frictional Forces ◽  
Vibration Measuring

The bearing of rotating machine often fails due to the frictional forces of rolling element, such frictional forces often generate a series of ultrasound, and that is to say, there must be some necessary relationships between the faults of rotating machinery equipment and the ultrasound from the equipment. In this paper, the histogram method which is the simplest method in statistics is used to analyze the ultrasonic signals. Through the analysis, the bearing faults could be preliminarily diagnosed. A verification research with respect to vibration method is carried out to analyze the effectiveness and superiority of ultrasonic method by using the histogram. Extensive experiments have been performed in a bearing vibration measuring instrument, and the results indicated that the fault diagnosis method based on the ultrasonic amplitude histogram is more useful than the method based on the vibration amplitude histogram. In addition, it is more sensitive and more effective than the method based on vibration amplitude histogram.

Research on the Fault Diagnosis Technology of Diesel Engine Based on the Instantaneous Speed

2009 ◽  
Vol 413-414 ◽  
pp. 547-552 ◽  
Author(s):  
Yi Hu ◽  
Rui Ping Zhou ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Vibration Amplitude ◽  
Good Effect ◽  
Cylinder Pressure ◽  
Angular Vibration ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Instantaneous Speed

The instantaneous speed signals of diesel contain lots of information about machine states, which is useful for fault diagnosis of diesel engine. Mixed fault diagnosis method of diesel engine based on the instantaneous speed has been proposed, which combines with the lower order angular vibration amplitude and SOM neural network to diagnose the cylinder pressure fault, then extracts three feature parameters of instantaneous speed to locate the fault cylinder. The method can detect the cylinder pressure fault accurately in diesel engine and locate the fault cylinder. The experimental confirmation shows that it has good effect on fault diagnosis of diesel engine.

Fault Diagnosis Expert System Applied on Machinery Industry

2013 ◽  
Vol 347-350 ◽  
pp. 461-465
Author(s):  
Ji Ming Huang ◽  
Min Jie Chai ◽  
Rong Ying Zhang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Expert Systems ◽  
Hydraulic System ◽  
Machinery Industry ◽  
Rotating Machine ◽  
Key Technologies ◽  
Diagnosis Method ◽  
Design Idea

This paper introduced the fault diagnosis technology and analyzed the shortcomings of traditional fault diagnosis method. Then simply introduced the intelligence fault diagnosis expert system. At last respectively introduced the design idea of three intelligence fault diagnosis expert systems, they respectively is used in rotating machine, mechanical maintenance and hydraulic system. At the same time the key technologies of each intelligence fault diagnosis expert system was respectively introduced.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method
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