Vibration Fault Diagnosis Method Based on Compositive Characteristics of Rotor Vibration and Stator Current

2007 ◽  
Author(s):  
Shuting Wan ◽  
Yonggang Li
Keyword(s):  
Fault Diagnosis ◽  
Short Circuit ◽  
Air Gap ◽  
Vibration Characteristics ◽  
Rotor Vibration ◽  
Stator Current ◽  
Dynamic Eccentricity ◽  
Diagnosis Method ◽  
Current Characteristics ◽  
Rotor Winding

Rotor vibration characteristics are first analyzed, when the rotor winding inter-turn short circuit fault, the air-gap dynamic eccentricity fault, the air-gap static eccentricity fault and the imbalance fault occurs. Next, the generator stator current characteristics on the faults also were analyzed, the results show that the faults can’t be diagnosed based only on rotor vibration characteristics or stator current characteristics. But considering the differences of compositive characteristics of the rotor vibration and stator current caused by different rotor faults, a new method of generator vibration fault diagnosis, based on compositive characteristics, is developed. Finally, the rotor vibration and stator current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.

INVESTIGATION ON STATOR AND ROTOR VIBRATION CHARACTERISTICS OF TURBO-GENERATOR UNDER AIR GAP ECCENTRICITY FAULT

2011 ◽  
Vol 35 (2) ◽  
pp. 161-176 ◽  
Author(s):  
Shuting Wan ◽  
Yuling He
Keyword(s):  
Magnetic Flux ◽  
Unit Area ◽  
Air Gap ◽  
Vibration Characteristics ◽  
Magnetic Flux Density ◽  
Rotor Vibration ◽  
Turbo Generator ◽  
Dynamic Eccentricity ◽  
Static Eccentricity ◽  
Theoretical Results

This paper investigates the stator and the rotor vibration characteristics of turbo-generator under the air gap eccentricity fault. Firstly the air gap magnetic flux density of the fault is deduced, and the formula of the magnetic pull per unit area acting on the stator and the unbalanced magnetic pulls of x-axis and y-axis acting on the rotor are respectively gotten. Then the static eccentricity, the dynamic eccentricity and the mixed eccentricity are respectively studied to analyze the stator and the rotor vibration characteristics. Finally experiments are done on a SDF-9 non-salient fault simulating generator to verify the theoretical results. The investigation results of this paper will be beneficial to the air gap eccentricity fault diagnosis of turbo-generator.

EFFECT OF STATIC ECCENTRICITY AND STATOR INTER-TURN SHORT CIRCUIT COMPOSITE FAULT ON ROTOR VIBRATION CHARACTERISTICS OF GENERATOR

2015 ◽  
Vol 39 (4) ◽  
pp. 767-781 ◽  
Author(s):  
Yu-Ling He ◽  
Meng-Qiang Ke ◽  
Fa-Lin Wang ◽  
Gui-Ji Tang ◽  
Shu-Ting Wan
Keyword(s):  
Magnetic Flux ◽  
Flux Density ◽  
Short Circuit ◽  
Air Gap ◽  
Vibration Characteristics ◽  
Magnetic Flux Density ◽  
Radial Deformation ◽  
Rotor Vibration ◽  
Harmonic Vibrations ◽  
Static Eccentricity

This paper investigates the radial rotor vibration characteristics under static air-gap eccentricity and stator inter-turn short circuit composite faults. The air-gap magnetic flux density is firstly deduced to obtain the unbalanced magnetic pull (UMP) on rotor. Then the rotor vibration characters, as well as the developing trend between the faulty parameters and the vibration amplitudes, are analyzed. Finally, the experiments are taken on a SDF-9 type simulating generator. It is shown that the radial deformation possibility, the 2nd, 4th, and 6th harmonic vibrations will be caused by the composite faults. Besides, the development of the inter-turn short circuit, the increment of the static eccentricity, and the rise of the exciting current will all get the deformation trend and the vibration amplitudes increased.

scholarly journals Compound Fault Diagnosis of Stator Interturn Short Circuit and Air Gap Eccentricity Based on Random Forest and XGBoost

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Rui Tian ◽  
Fuyang Chen ◽  
Shiyi Dong
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
High Speed ◽  
Wavelet Packet ◽  
Short Circuit ◽  
Principal Component ◽  
Air Gap ◽  
Fault Features ◽  
Diagnosis Method ◽  
Pca Method

Taking the traction motor of CRH2 high-speed train as the research object, this paper proposes a diagnosis method based on random forest and XGBoost for the compound fault resulting from stator interturn short circuit and air gap eccentricity. First, the U-phase and V-phase currents are used as fault diagnosis signal and then the Savitzky–Golay filtering method is used for the noise deduction from the signal. Second, the wavelet packet decomposition is used to extract the composite fault features and then the high-dimensional features are optimized by the principal component analysis (PCA) method. Finally, the random forest and XGBoost are combined to detect composite faults. Using the experimental data of CRH2 semiphysical simulation platform, the diagnosis of different fault modes is completed, and the high diagnosis accuracy is achieved, which verifies the validity of this method.

The Application of Improved SVM in Mixed Circuit

2012 ◽  
Vol 224 ◽  
pp. 493-496 ◽  
Author(s):  
Huai Long Wang ◽  
Qiang Pan ◽  
Hong Liu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Small Samples ◽  
Lifting Wavelet Transform ◽  
Fault Features ◽  
Diagnosis Method ◽  
Lifting Wavelet ◽  
Current Characteristics ◽  
Mixed Circuit ◽  
Better Than

In order to improve the speed and the rate of fault diagnosis in mixed circuit, this paper introduces a new fault diagnosis method. Through extracting fault features of current characteristics effectively and applying to Improved SVM, the ability of pattern recognition will be better than the traditional BP Neural Network and Single SVM, especially in small samples or non-linear cases. Meanwhile, this paper presents the lifting wavelet transform in order to obtain the feature information accurately. The accuracy of fault diagnosis can greatly enhance by discussing the Improved SVM combined with lifting wavelet transform in a specific monostable trigger. That points out a new direction for the fault diagnosis of mixed circuit.

scholarly journals An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoxun Zhu ◽  
Jianhong Zhao ◽  
Dongnan Hou ◽  
Zhonghe Han
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fault Identification ◽  
High Complexity ◽  
Test Rig ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Image Identification ◽  
Diagnosis Method

This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

scholarly journals Sparse Representation and SVM Diagnosis Method Inter-Turn Short-Circuit Fault in PMSM

2019 ◽  
Vol 9 (2) ◽  
pp. 224 ◽  
Author(s):  
Siyuan Liang ◽  
Yong Chen ◽  
Hong Liang ◽  
Xu Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Sparse Representation ◽  
Permanent Magnet ◽  
Short Circuit ◽  
Small Sample ◽  
Support Vector ◽  
Feature Vectors ◽  
Diagnosis Method ◽  
Short Circuit Fault

Permanent magnet synchronous motors (PMSM) has the advantages of simple structure, small size, high efficiency, and high power factor, and a key dynamic source and is widely used in industry, equipment and electric vehicle. Aiming at its inter-turn short-circuit fault, this paper proposes a fault diagnosis method based on sparse representation and support vector machine (SVM). Firstly, the sparse representation is used to extract the first and second largest sparse coefficients of both current signal and vibration signals, and then they are composed into four-dimensional feature vectors. Secondly, the feature vectors are input into the support vector machine for fault diagnosis, which is suitable for small sample. Experiments on a permanent magnet synchronous motor with artificially set inter-turn short-circuit fault and a normal one showed that the method is feasible and accurate.

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