Fault Evolution Characteristic Analysis of Planetary Gear Based on Multidimensional Nonlinear Frequency Response

2019 ◽  
Vol 14 (4) ◽  
Author(s):  
Haitao Wang ◽  
Zhimao Tao ◽  
Lichen Shi ◽  
Zhenya Kang
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear System ◽  
Frequency Response ◽  
Volterra Series ◽  
System Modeling ◽  
Planetary Gear ◽  
Nonlinear Frequency ◽  
Characteristic Analysis ◽  
Loading Test ◽  
Planetary Gearbox

This study presented a method for modeling the nonlinear system of a planetary gearbox and the fault diagnosis of a crack in a planetary gear based on the Volterra series theory. First, the exponential Hilbert reproducing kernel and its fast optimization algorithm was proposed and deduced in theory, and the fast solution of the fourth-order kernel of the Volterra series was successfully solved. Second, the Volterra series model estimation was compared with the least squares estimation of the actual collected signals from the planetary gearbox and the time-domain output signal was estimated using a neural network. The accuracy and the superiority of the Volterra series model of the planetary gearbox were then verified. At the same time, the convergence and the memory length of the Volterra series were discussed. In order to further mine and extract fault feature information, coupling relationship between the generalized frequency response of higher order spectrum of the Volterra series model and fault frequency was also studied. This study attempted to reflect the fault state and fault degree of a crack in a planetary gear from different observation angles and dimensions. Finally, the real condition loading test of a gearbox's comprehensive fault test platform was carried out. The validity of the method of nonlinear system modeling and fault diagnosis of the planetary gearbox, based on the Volterra series theory, was verified, and a new solution has been provided for related research in this field.

scholarly journals Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Weigang Wen ◽  
Robert X. Gao ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Manifold Learning ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Planetary Gearbox ◽  
Characteristic Extraction ◽  
Gear Fault ◽  
Planet Gear ◽  
Fault Characteristic

The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.

scholarly journals Nonlinear system modeling and identification of an onshore oscillating water column - wave energy converter, using Volterra series and Hilbert-Huang transform

2012 ◽  
Author(s):  
Γεώργιος Γκίκας
Keyword(s):  
Nonlinear System ◽  
Water Column ◽  
Wave Energy ◽  
Volterra Series ◽  
System Modeling ◽  
Oscillating Water Column ◽  
Energy Converter ◽  
Nonlinear System Modeling ◽  
Hilbert Huang Transform ◽  
System Modeling And Identification

Ο κύριος στόχος της Δ.Δ. είναι να αναπτυχθεί ένα συστημικό μοντέλοκατάλληλο να περιγράψει την λειτουργία της συσκευής κυματικήςενέργειας με ταλαντευόμενη στήλη ύδατος, επικεντρώνοντας τοενδιαφέρον στην μεταβολή της δυναμικής πίεσης μέσα στο κλωβό τηςσυσκευής.Η μεθοδολογία που αναπτύχθηκε περιλαμβάνει την χρησιμοποίησησειρών Volterra και μετασχηματισμού Hilbert-Huang με βάση τα οποίακατασκευάστηκε ακριβές και εύρωστο συστημικό μοντέλο για το μη-γραμμικο μέρος του συστήματος.Τα αποτελέσματα κρίνονται ως άκρως ικανοποιητικά και η μεθοδολογίαπου αναπτύχθηκε εκτός του υπολογιστικού κόστους που εξοικονομείμπορεί να εφαρμοστεί ακόμα σε μια πλειάδα μη-γραμμικών δυναμικώνσυστημάτων.

scholarly journals Fault Diagnosis for a Multistage Planetary Gear Set Using Model-Based Simulation and Experimental Investigation

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Guoyan Li ◽  
Fangyi Li ◽  
Yifan Wang ◽  
Dehao Dong
Keyword(s):  
Fault Diagnosis ◽  
Spectral Characteristics ◽  
Planetary Gear ◽  
Spectral Structure ◽  
Mesh Stiffness ◽  
Planetary Gearbox ◽  
Frequency Components ◽  
Close Frequency ◽  
Simulated Analysis ◽  
Potential Energy Method

The gear damage will induce modulation effects in vibration signals. A thorough analysis of modulation sidebands spectral structure is necessary for fault diagnosis of planetary gear set. However, the spectral characteristics are complicated in practice, especially for a multistage planetary gear set which contains close frequency components. In this study, a coupled lateral and torsional dynamic model is established to predict the modulation sidebands of a two-stage compound planetary gear set. An improved potential energy method is used to calculate the time-varying mesh stiffness of each gear pair, and the influence of crack propagation on the mesh stiffness is analyzed. The simulated signals of the gear set are obtained by using Runge-Kutta numerical analysis method. Meanwhile, the sidebands characteristics are summarized to exhibit the modulation effects caused by sun gear damage. At the end, the experimental signals collected from an industrial SD16 planetary gearbox are analyzed to verify the theoretical derivations. The results of experiment agree well with the simulated analysis.

scholarly journals A Novel Method for Fault Diagnosis of the Two-Input Two-Output Nonlinear Mass-Spring-Damper System Based on NOFRF and MBPCA

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jialiang Zhang ◽  
Jie Wu ◽  
Xiaoqian Zhang
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Nonlinear System ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Principal Component ◽  
Component Analysis ◽  
Novel Method ◽  
Mass Spring

For fault diagnosis of the two-input two-output mass-spring-damper system, a novel method based on the nonlinear output frequency response function (NOFRF) and multiblock principal component analysis (MBPCA) is proposed. The NOFRF is the extension of the frequency response function of the linear system to the nonlinear system, which can reflect the inherent characteristics of the nonlinear system. Therefore, the NOFRF is used to obtain the original fault feature data. In order to reduce the amount of feature data, a multiblock principal component analysis method is used for fault feature extraction. The least squares support vector machine (LSSVM) is used to construct a multifault classifier. A simplified LSSVM model is adopted to improve the training speed, and the conjugate gradient algorithm is used to reduce the required storage of LSSVM training. A fault diagnosis simulation experiment of a two-input two-output mass-spring-damper system is carried out. The results show that the proposed method has good diagnosis performance, and the training speed of the simplified LSSVM model is significantly higher than the traditional LSSVM.

scholarly journals Establishment and analysis of nonlinear frequency response model of planetary gear transmission system

2021 ◽  
Vol 12 (2) ◽  
pp. 1093-1104
Author(s):  
Hao Dong ◽  
Yue Bi ◽  
Zhen-Bin Liu ◽  
Xiao-Long Zhao
Keyword(s):  
Planetary Gear ◽  
Nonlinear Frequency ◽  
Time Varying ◽  
Vibration Displacement ◽  
Nonlinear Characteristics ◽  
Planetary Gearbox ◽  
Meshing Stiffness ◽  
Gear Transmission System ◽  
Nonlinear Frequency Response ◽  
Simulation Results

Abstract. Based on the lumped parameter theory, a nonlinear bending torsion coupling dynamic model of planetary gear transmission system was established by considering the backlash, support clearance, time-varying meshing stiffness, meshing damping, transmission error and external periodic excitation. The model was solved by the Runge–Kutta method, the dynamic response was analyzed by a time domain diagram and phase diagram, and the nonlinear vibration characteristics were studied by the response curve of the speed vibration displacement. The vibration test of the planetary gearbox was carried out to verify the correctness of frequency domain response characteristics. The results show that the vibration response in the planetary gear system changes from a multiple periodic response to a single periodic response with the increase in input speed. Under the action of the backlash, time-varying meshing stiffness and meshing damping, the speed vibration displacement response curves of internal and external meshing pairs appear to form a nonlinear jump phenomenon and have a unilateral impact area, and the system presents nonlinear characteristics. The nonlinear vibration of the system can be effectively suppressed by decreasing the mesh stiffness or increasing the mesh resistance, while the vibration response displacement of the system increases by increasing the external exciting force, and the nonlinear characteristics of the system remain basically unchanged. The backlash is the main factor affecting the nonlinear frequency response of the system, but it can restrain the resonance of the system in a certain range. The spectrum characteristics of the vibration displacement signal of the planetary gearbox at different speeds are similar to the simulation results, which proves the validity of the simulation analysis model and the simulation results. It can provide a theoretical basis for the system vibration and noise reduction and a dynamic structural stability design optimization.

scholarly journals Fault Diagnosis of an Analog Circuit Based on Hierarchical DVS

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1901
Author(s):  
Yong Deng ◽  
Yuhao Zhou
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Analog Circuit ◽  
Nearest Neighbor ◽  
Volterra Series ◽  
System Modeling ◽  
Information Criterion ◽  
Feature Extraction Method ◽  
Circuit Fault Diagnosis

Analog circuit fault diagnosis technology is widely used in the diagnosis of various electronic devices. The basic strategy is to extract circuit fault characteristics and then to use a clustering algorithm for diagnosis. The discrete Volterra series (DVS) is a common feature extraction method; however, it is difficult to calculate its parameters. To solve the problem of feature extraction in fault diagnosis, we propose an improved hierarchical Levenberg–Marquardt (LM)–DVS algorithm (IDVS). First, the DVS is simplified on the basis of the hierarchical symmetry of the memory parameters, the LM strategy is used to optimize the coefficients, and a Bayesian information criterion based on the symmetry of entropy is introduced for order selection. Finally, we propose a fault diagnosis method by combining the improved DVS algorithm and a condensed nearest neighbor algorithm (CNN) (i.e., the IDVS–CNN method). A simulation experiment was conducted to verify the feature extraction and fault diagnosis ability of the IDVS–CNN. The results show that the proposed method outperforms conventional methods in terms of the macro and micro F1 scores (0.903 and 0.894, respectively), which is conducive to the efficient application of fault diagnosis. In conclusion, the improved method in this study is helpful to simplify the calculation of the DVS parameters of circuit faults in analog electronic systems, and provides new insights for the prospective application of circuit fault diagnosis, system modeling, and pattern recognition.

scholarly journals A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion

2019 ◽  
Vol 9 (24) ◽  
pp. 5443 ◽  
Author(s):  
Zhe Wu ◽  
Qiang Zhang ◽  
Lifeng Cheng ◽  
Shengyue Tan
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbines ◽  
Planetary Gear ◽  
Energy Operator ◽  
New Method ◽  
Signal Spectrum ◽  
Strong Nonlinearity ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Full Vector

Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbines. For this reason, we propose a new method of planetary gearbox fault diagnosis based on Compressive sensing, Two-dimensional variational mode decomposition (2D-VMD) and full-vector spectrum technology. Firstly, the nonlinear reconstruction and noise reduction of the signal is carried out by using compressed sensing, and then the signal with multiple degrees of freedom is adaptively decomposed into multiple sets of characteristic scale components by using 2D-VMD. Then, Rényi entropy is used as the optimization index of 2D-VMD analysis performance to extract the effective target intrinsic mode function (IMF) component, reconstruct the dynamics signal in the planetary gearbox, and improve the signal-to-noise ratio. Then, using the full-vector spectrum technique, the homologous information collected by numerous sensors is data layer fused in the spatial domain and the time domain to increase the comprehensiveness and certainty of the fault information. Finally, the Teager–Kaiser energy operator is used to demodulate the potential low-frequency dynamics frequency characteristics from the high-frequency domain and detect the fault characteristic frequency. Furthermore, the correctness and validity of the method are verified by the fault test signal of the planetary gearbox.

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