Transformer Winding Fault Diagnosis by Vibration Monitoring

The subject of condition monitoring is becoming increasingly popular on engineering courses. One of the principal techniques used to assess the condition of plant and machinery is vibration monitoring. Traditional teaching methods can adequately present the analytical techniques to students and case studies illustrate their application but the art of diagnosing faults in machinery is usually not addressed to any significant extent. To overcome this deficiency a ‘game’ has been devised which gives students the opportunity to apply aspects of vibration monitoring to a simulated piece of real plant.

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Current-Aided Time-Frequency Analysis of Vibration Signals for Gearbox Fault Diagnosis

Volume 8: 30th Conference on Mechanical Vibration and Noise ◽

10.1115/detc2018-85550 ◽

2018 ◽

Author(s):

Xiaotong Tu ◽

Yue Hu ◽

Fucai Li

Keyword(s):

Fault Diagnosis ◽

Frequency Analysis ◽

Vibration Signal ◽

Vibration Monitoring ◽

Current Signal ◽

Reliable Technique ◽

Time Frequency Analysis ◽

Rotating Speed ◽

Planetary Gearbox ◽

Time Frequency

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.

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A Novel Transformer Winding Fault Diagnosis Method Based on Damped Oscillation Wave

Journal of Physics Conference Series ◽

10.1088/1742-6596/1619/1/012008 ◽

2020 ◽

Vol 1619 ◽

pp. 012008

Author(s):

Zhuorui Jin ◽

Jian Sun ◽

Qing Yang ◽

Peiyu Su

Keyword(s):

Fault Diagnosis ◽

Transformer Winding ◽

Diagnosis Method ◽

Damped Oscillation

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Automobile Gearbox Fault Diagnosis Using Naive Bayes and Decision Tree Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.813-814.943 ◽

2015 ◽

Vol 813-814 ◽

pp. 943-948 ◽

Cited By ~ 2

Author(s):

P.G. Sreenath ◽

Gopalakrishnan Praveen Kumare ◽

Sundar Pravin ◽

K.N. Vikram ◽

M. Saimurugan

Keyword(s):

Fault Diagnosis ◽

Decision Tree ◽

Vital Role ◽

Condition Based Maintenance ◽

Vibration Monitoring ◽

Machine Learning Techniques ◽

Decision Tree Algorithm ◽

Vibration Signals ◽

Learning Techniques ◽

Technique Comparison

Gearbox plays a vital role in various fields in the industries. Failure of any component in the gearbox will lead to machine downtime. Vibration monitoring is the technique used for condition based maintenance of gearbox. This paper discusses the use of machine learning techniques for automating the fault diagnosis of automobile gearbox. Our experimental study monitors the vibration signals of actual automobile gearbox with simulated fault conditions in the gear and bearing. Statistical features are extracted and classified for identifying the faults using decision tree and Naïve bayes technique. Comparison of the techniques for determining the classification accuracy is discussed.

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Vibration Monitoring and Fault Diagnosis System of Turbine-Generator

ASME 2007 Power Conference ◽

10.1115/power2007-22020 ◽

2007 ◽

Author(s):

Dongmei Du ◽

Qing He ◽

Hong Li

Keyword(s):

Fault Diagnosis ◽

Power Plants ◽

Vibration Monitoring ◽

Turbine Generator ◽

Diagnosis System ◽

Java Applet ◽

Java Technology ◽

Fault Diagnosis System ◽

The Difference ◽

Batch Data

It is very important to monitor vibration and diagnose fault for the operating safety of turbine-generator. The remote monitor and diagnosis via the cyber-based technology is a necessity. The difference between browser/server mode and client/server mode is discussed. There are many advantages of applying Java technology. Using Java, a vibration monitoring and fault diagnosis system of turbine-generator based on browser/server mode is developed. The functions as well as the structure of the whole system are analyzed. Online transmission of batch data via Internet is presented, especially for different program languages. Java Applet technology is used to develop client program. With double-buffer method, a lot of graphic interfaces of dynamic making online are presented, which are not blinking. It is proved that the system is already adopted and functions well in several power plants.

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Improved Power Transformer Winding Deformation Fault Diagnosis Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.666.149 ◽

2014 ◽

Vol 666 ◽

pp. 149-153 ◽

Cited By ~ 2

Author(s):

Hong Zhong Ma ◽

Ning Jiang ◽

Chun Ning Wang ◽

Zhi Hui Geng

Keyword(s):

Fault Diagnosis ◽

Fundamental Frequency ◽

Characteristic Frequency ◽

Power Transformer ◽

Vibration Signal ◽

Frequency Component ◽

Transformer Winding ◽

Diagnosis Method ◽

Winding Deformation ◽

Harmonic Components

according to analysing the generation principle of transformer winding deformation and its impact on the vibration signal, and make a large number of trial, it can be found in addition to the fundamental frequency component that can reflect the failure, the new characteristic frequency which conclude 50Hz frequency component and some of its harmonic components, the harmonic components of the fundamental frequency can also reflect the failure. Transformer winding deformation fault diagnosis method is proposed based on the relationship between the characteristic frequency, it can not only diagnose whether the failure inside the transformer windings, but also determine the type of fault. In order to verify the proposed method, deformation fault is set to the actual transformer winding. After de-noising, discounted processing, the acquisition monitoring points of vibration signal is used by the proposed method, and the actual transformer is diagnosed, The diagnostic result is same with actual failure. It is shown that the proposed diagnostic method is accurate and feasible.

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Orbit Shape Automatic Recognition Based on Artificial Neural Network

ASME 2005 Power Conference ◽

10.1115/pwr2005-50208 ◽

2005 ◽

Cited By ~ 2

Author(s):

Dongmei Du ◽

Qing He

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fault Diagnosis ◽

Expert System ◽

Recognition Performance ◽

Shape Recognition ◽

Vibration Monitoring ◽

Moment Invariants ◽

Artificial Neural ◽

Artificial Neural Network Ann

Orbit is a significant symptom in the fault diagnosis of rotating machine. The orbit is a 2-D image and can be described by moment invariants, the shape property of 2-D image, which is a description with translating-, rotating-, and scaling-invariants for 2-D image. The descriptive method of orbit image is investigated and an automatic orbit shape recognition based on artificial neural network (ANN) with moment invariants is proposed in this paper. The ANN of orbit shape recognition is trained by the training patterns generated by computer simulation for plenty of orbit shapes. It is shown that the trained ANN is of good recognition performance and generalization capability when applied to recognition of the measured orbits. This method can be used to the intelligent expert system of fault diagnosis to obtain automatically online orbit symptom in shafts vibration monitoring of turbine generator, which will improve the automatization of obtaining fault symptom and the automatic diagnosis in the expert system.

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Research on mechanical fault diagnosis method of power transformer winding

The Journal of Engineering ◽

10.1049/joe.2018.8712 ◽

2019 ◽

Vol 2019 (16) ◽

pp. 2096-2101

Author(s):

Zhang Bin ◽

Zhao Dan ◽

Wang Feiming ◽

Shi Kejian ◽

Zhao Zhenyang

Keyword(s):

Fault Diagnosis ◽

Power Transformer ◽

Transformer Winding ◽

Diagnosis Method

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Research of Single Component Shock Methods in Wind Turbine Fault Diagnosis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.805-806.303 ◽

2013 ◽

Vol 805-806 ◽

pp. 303-311

Author(s):

Ning Jia ◽

Tian Xia Zhang ◽

Yuan Sheng Li ◽

Tao Zhang

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Wind Farm ◽

Vibration Signal ◽

Single Component ◽

Vibration Monitoring ◽

Generator System ◽

Diagnosis Method ◽

Demodulation Method ◽

Fault Characteristic

The structure of the wind turbine generator system is complex and it is difficult to identify the fault signals because of fault frequency aliasing on the vibration characteristics. The wind turbine fault diagnosis method is raised on single component shock to solve the vibration signal feature extraction during the wind turbines operating. Based on the principle of Hilbert envelope demodulation, this envelope demodulation method is presented for the single IMF component which contains shock fault characteristic frequency to solve the possible problem which fault Frequency is difficult to identify when the original signal is directly asked to envelope. This method has been applied and verified when a wind farm CSC-855W wind turbine vibration monitoring device was presented. The results show that compared with the traditional envelope demodulation method, by this method wind turbine fault characteristic can be more effectively and directly extracted and the accuracy of fault diagnosis can be improved. It is of great practical value.

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