scholarly journals Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory

2021 ◽  
Vol 22 ◽  
pp. 24
Author(s):  
Yang Dalian ◽  
Zhang Fanyu ◽  
Miao Jingjing ◽  
Zhang Hongxian ◽  
Li Renjie ◽  
...  
Keyword(s):  
Mutual Information ◽  
Error Rate ◽  
Evidence Theory ◽  
Rotor System ◽  
Identification Accuracy ◽  
Traditional Methods ◽  
Vibration Signals ◽  
Quantitative Identification ◽  
Misalignment Fault ◽  
Main Factor

Misalignment fault is the main factor that affects the normal running of dual-rotor system. Quantitative identification the misalignment fault is an important way to ensure the safe and stable service of the dual-rotor system, while the identification accuracy of traditional methods is low. Aiming at the above problems, this paper proposed a dual-rotor misalignment fault quantitative identification method based on DBN and D-S evidence theory improved by mutual information measure (MIMD-S). Seven groups experiments were conducted and several vibration signals were collected. By comparing it with the traditional methods D-S, and Pignistic improved D-S (PD-S) evidence theory, the results show that the method proposed in this paper improves the accuracy of the misalignment fault quantitative identification of the dual-rotor, the identification error rate was only 0.36%.

Misalignment Characteristic Analysis Based on Advanced Empirical Mode Decomposition

2013 ◽  
Vol 325-326 ◽  
pp. 1559-1563
Author(s):  
Hui Min Li ◽  
Wei Zhao ◽  
Yun Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Rotor System ◽  
Experimental Results ◽  
Intrinsic Mode Functions ◽  
Characteristic Analysis ◽  
Wavelet Method ◽  
Vibration Signals ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Misalignment Fault

A new method of misalignment characteristic analysis, which is based on advanced empirical mode decomposition (AEMD), is presented in this paper. At first the vibration signals of a rotor system with different misalignments is collected separately. Then the multicomponent signal x (t) is decomposed into a number of the so-called intrinsic mode functions (IMFs) by use of AEMD respectively. For these IMFs the wavelet method is used to extract the interesting features. It is found that the IMF2 contains the interesting misalignment character. Additionally the experimental results substantiate that the proposed method for misalignment analysis can identify the varying trend of misalignment fault clearly.

scholarly journals Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3429 ◽  
Author(s):  
Chu ◽  
Yuan ◽  
Hu ◽  
Pan ◽  
Pan
Keyword(s):  
Mutual Information ◽  
Parameter Identification ◽  
Wind Turbines ◽  
Closed Loop ◽  
Control Design ◽  
Identification Accuracy ◽  
Mass Model ◽  
Identification Methods ◽  
Advanced Control ◽  
Mechanical Dynamics

With increasing size and flexibility of modern grid-connected wind turbines, advanced control algorithms are urgently needed, especially for multi-degree-of-freedom control of blade pitches and sizable rotor. However, complex dynamics of wind turbines are difficult to be modeled in a simplified state-space form for advanced control design considering stability. In this paper, grey-box parameter identification of critical mechanical models is systematically studied without excitation experiment, and applicabilities of different methods are compared from views of control design. Firstly, through mechanism analysis, the Hammerstein structure is adopted for mechanical-side modeling of wind turbines. Under closed-loop control across the whole wind speed range, structural identifiability of the drive-train model is analyzed in qualitation. Then, mutual information calculation among identified variables is used to quantitatively reveal the relationship between identification accuracy and variables’ relevance. Then, the methods such as subspace identification, recursive least square identification and optimal identification are compared for a two-mass model and tower model. At last, through the high-fidelity simulation demo of a 2 MW wind turbine in the GH Bladed software, multivariable datasets are produced for studying. The results show that the Hammerstein structure is effective for simplify the modeling process where closed-loop identification of a two-mass model without excitation experiment is feasible. Meanwhile, it is found that variables’ relevance has obvious influence on identification accuracy where mutual information is a good indicator. Higher mutual information often yields better accuracy. Additionally, three identification methods have diverse performance levels, showing their application potentials for different control design algorithms. In contrast, grey-box optimal parameter identification is the most promising for advanced control design considering stability, although its simplified representation of complex mechanical dynamics needs additional dynamic compensation which will be studied in future.

scholarly journals The Dynamic Characteristic of a Faulted Rotor System with Multi-Objective Optimization Designed SFD

2019 ◽  
Vol 9 (17) ◽  
pp. 3628 ◽  
Author(s):  
Liang Ma ◽  
Jun Wang ◽  
Guichang Zhang
Keyword(s):  
Fundamental Frequency ◽  
Working Conditions ◽  
Optimization Method ◽  
Rotor System ◽  
Dynamic Responses ◽  
Multi Objective Optimization ◽  
Operational Parameters ◽  
Multi Objective ◽  
Coupling Fault ◽  
Misalignment Fault

As an important part of the turbomachinery, the rotor–bearing system has been upgraded to provide a high rotating speed in order to meet the demand of high power production. With increasing demand for stability, the squeeze film damper (SFD) has been widely used in industrial machinery because it can reduce the vibration amplitude and suppress the external force. Usually, it shows inadaptability under the different working conditions where the SFD parameters didn’t change appropriately. Therefore, the reasonable choice of operational parameters of SFD is the key solution that can provide viscous damping effectively and restrain the nonlinear vibration generated by faults. In this paper, the mathematical model of a rotor-ball bearing-SFD system considering the misalignment fault and misalignment-rubbing coupling fault is built first. Then the dynamic characteristics under typical working conditions (ω = 1000 rad/s) of the faulted rotor are discussed. The vibration attenuation effects of the SFD parameters selected by using the multi-objective optimization method on the dynamic responses are analyzed. The results show that when the rotor system operates under different states, the value and the sensitivity of optimization parameters are altered. With no fault, the amplitude of fundamental frequency decrease 23%. With the misalignment fault, the amplitude of the fundamental frequency decreases by 43.4%, the amplitude of 2× fundamental frequency decreases by 27.5%, and the amplitude of 3× fundamental frequency decreases by 66.7%. With the misalignment-rubbing coupling fault, the amplitude of fundamental frequency reduces by 7.4%, the amplitude of 2× fundamental frequency drops by 51.5%, and the amplitude of 3× fundamental frequency drops by 16.8%. Overall, the feasibility of the optimization method of the variable-structured SFD operational parameters for the faulted rotor system is verified. These parametric analyses are very helpful in the development of a high-speed rotor system and provide a theoretical reference for the vibration control and optimal design of rotating machinery.

A Numerical Study on the Effect of Unbalance and Misalignment Fault Parameters in a Rigid Rotor Levitated by Active Magnetic Bearings

2019 ◽  
Author(s):  
Prabhat Kumar ◽  
Rajiv Tiwari
Keyword(s):  
Numerical Study ◽  
Equations Of Motion ◽  
Rotor System ◽  
Magnetic Bearings ◽  
Inertia Force ◽  
Dimensional System ◽  
Rigid Rotor ◽  
Active Magnetic Bearings ◽  
Fault Parameters ◽  
Misalignment Fault

Abstract This paper focusses on analysing the vibration behaviour of a rigid rotor levitated by active magnetic bearings (AMB) under the influence of unbalance and misalignment parameters. Unbalance in rotor and misalignment between rotor and both supported AMBs are key fault parameters in the rotor system. To demonstrate this dynamic analysis, an unbalanced rigid rotor with a disc at the middle levitated by two misaligned active magnetic bearings has been mathematically modelled. One of the novel concepts is also described as how the force due to active magnetic bearings on the rigid rotor is modified when the rotor is parallel misaligned with AMBs. With inclusion of inertia force, unbalance force and force due to misaligned AMBs, the equations of motion of the rigid rotor system are derived and converted into dimensionless form in terms of various non-dimensional system and fault parameters. Numerical simulations have been performed to yield the dimensionless rotor displacement and controlling current responses at AMBs. The prime intention of the present paper is to study the effect on the displacement response of the rigid rotor system and the current consumption of AMBs for different ranges of disc eccentricities and rotor-AMB misalignments.

scholarly journals Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xinyu Pang ◽  
Jie Shao ◽  
Xuanyi Xue ◽  
Wangwang Jiang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Fault Diagnosis ◽  
Mathematical Morphology ◽  
Bp Neural Network ◽  
Feature Vector ◽  
Rotor System ◽  
Identification Accuracy ◽  
Frequency Noise ◽  
Axis Orbit

The shape characteristic of the axis orbit plays an important role in the fault diagnosis of rotating machinery. However, the original signal is typically messy, and this affects the identification accuracy and identification speed. In order to improve the identification effect, an effective fault identification method for a rotor system based on the axis orbit is proposed. The method is a combination of ensemble empirical mode decomposition (EEMD), morphological image processing, Hu invariant moment feature vector, and back propagation (BP) neural network. Experiments of four fault forms are performed in single-span rotor and double-span rotor test rigs. Vibration displacement signals in the X and Y directions of the rotor are processed via EEMD filtering to eliminate the high-frequency noise. The mathematical morphology is used to optimize the axis orbit including the dilation and skeleton operation. After image processing, Hu invariant moments of the skeleton axis orbits are calculated as the feature vector. Finally, the BP neural network is trained to identify the faults of the rotor system. The experimental results indicate that the time of identification of the tested axis orbits via morphological processing corresponds to 13.05 s, and the identification accuracy rate ranges to 95%. Both exceed that without mathematical morphology. The proposed method is reliable and effective for the identification of the axis orbit and aids in online monitoring and automatic identification of rotor system faults.

scholarly journals Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Shuang Pan ◽  
Tian Han ◽  
Andy C. C. Tan ◽  
Tian Ran Lin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Induction Motors ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Vibration Signals ◽  
Vibration Data ◽  
Motor Vibration

An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.

Numerical and experimental analysis of rubbing–misalignment mixed fault in a dual-rotor system

2020 ◽  
pp. 095440622096858
Author(s):  
Wenzhen Xie ◽  
Chao Liu ◽  
Nanfei Wang ◽  
Dongxiang Jiang
Keyword(s):  
High Speed ◽  
Experimental Tests ◽  
Rotor System ◽  
Dynamic Responses ◽  
System Model ◽  
Speed Ratio ◽  
Rotor Systems ◽  
Frequency Components ◽  
Aero Engines ◽  
Misalignment Fault

Dual-rotor systems are widely used in aero-engines, in which rubbing–misalignment mixed faults are essential, as both are frequently observed and can occur simultaneously due to the harsh working conditions of high temperature, high pressure, and high speed. To analyze the vibration characteristics of such faults, a dual-rotor system model is established and dynamic responses under varying parameters of the dual-rotor system with rubbing–misalignment mixed fault are investigated. Through numerical simulation, the effects of speed ratio, rubbing clearance, and rubbing stiffness on the dual-rotor system with rubbing–misalignment fault are revealed. Meanwhile, experimental tests are conducted for validation, the main findings of which are that the characteristic frequency components could benefit the diagnosis of mixed faults in dual-rotor systems.

A simple protein evolutionary classification method based on the mutual relations between protein sequences

2020 ◽  
Vol 15 ◽  
Author(s):  
Xiaogeng Wan ◽  
Xinying Tan
Keyword(s):  
Mutual Information ◽  
Protein Sequences ◽  
New Method ◽  
Traditional Methods ◽  
Simple Method ◽  
Information Rates ◽  
Normalized Mutual Information ◽  
Natural Vector ◽  
Protein Map ◽  
Mutual Relations

Aims: This paper presents a simple method that is efficient for protein evolutionary classification. Background: Proteins are diverse with their sequences, structures and functions. It is important to understand the relations between the sequences, structures and functions of proteins. Many methods have been developed for protein evolutionaryclassifications, these methods include machine learning methods such as the LibSVM, feature methods such as the natural vector method and the protein map. Machine learning methods use pre-labeled training sets to classify protein sequences into disjoint classes. Feature methods such as the natural vector and the protein map convert protein sequences into feature vectors and use polygenetic-trees to classify on the distance between the feature vectors. In this paper, we propose a simple method that classify the evolutionary relations of protein sequences using the distance maps on the mutual relations between protein sequences. The new method is unsupervised and model-free, which is efficient in the evolutionary classifications of proteins. Objective: In this paper, we propose a simple method that classify the evolutionary relations of protein sequences using the distance maps on the mutual relations between protein sequences. The new method is unsupervised and model-free, which is efficient in the evolutionary classifications of proteins.methodTo quantify the mutual relations and the homology of protein sequences, we use the normalized mutual information rates on protein sequences, and we define two distance maps that convert the normalized mutual information rates into 'distances', and use UPGMA trees to present the evolutionary classifications of proteins. Method:: To quantify the mutual relations and the homology of protein sequences, we use the normalized mutual information rates on protein sequences, and we define two distance maps that convert the normalized mutual information rates into 'distances', and use UPGMA trees to present the evolutionary classifications of proteins. Result: We use four classifical protein evolutionary classification examples to demonstrate the new method, where the results are compared with traditional methods such as the natural vector and the protein maps. We use the AUPRC curves to evaluate the classification qualities of the new method and the traditional methods. We found that the new method with the two distance maps is efficient in the evolutionary classification of the classical examples, and it outperforms the natural vector and the protein maps in the evolutionary classifications. Conclusion: The normalized mutual information rates with the two distance maps are efficient in protein evolutionary classifications, which outperform some classifical methods in the evolutionary classifications. Other: The results are compared with traditional protein evolutionary classification methods such as the natural vector and the protein map, and the method of AUPRC curves is applied to the new method and the traditional methods to inspect the classification accuracies.

Vibration Analysis of Coupled Faults Diagnosis in a Rotor System Using Wavelet De-Noising and KPCA Data Fusion

2012 ◽  
Vol 192 ◽  
pp. 233-236
Author(s):  
Xiu Mei Zhu
Keyword(s):  
Fault Diagnosis ◽  
Principal Component ◽  
Rotor System ◽  
Kernel Principal Component Analysis ◽  
Vibration Signals ◽  
Rotor Systems ◽  
Fault Features ◽  
Simultaneous Existence ◽  
Faults Diagnosis

In a rotor system, simultaneous existence of coupled faults, i.e. a crack couples with a misalignment, is very common. However, the single fault diagnosis has been investigated extensively in previous work while the issue of coupled faults diagnosis (i.e. considering two or more than two faults at a time) has been addressed insufficiently. In order to detect the existence of coupled faults and to prevent a fatigue crack in the rotor shaft, a new method is proposed to analyze the vibration signals using the Wavelet de-nosing and kernel principal component analysis (KPCA) in this work. The Wavelet was firstly used to de-noise the original vibration signals, and then the KPCA was adopted to extract useful fault features for the coupled faults detection. A case study on the coupled fault diagnosis of the rotor system has been implemented. The diagnosis results demonstrate that the proposed method is feasible for the coupled fault diagnosis of rotor systems. The fault detection rate is 91.0%.

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