Rotor Rub-impact Fault Research Articles

Application of Feature Extraction Method Based on Empirical Fourier Decomposition in Rotor Rub-impact Fault

This paper provides a feature extraction technique primarily by using empirical Fourier decomposition (EFD) to reveal the time-frequency features of rotor rub-impact fault. The Hilbert transform estimates the instantaneous frequency (IF) and instantaneous amplitude (IA) of the signal generated by EFD. Finally, high-quality time-frequency representation (TFR) is reconstructed by the adaptive time-frequency spectrum (ATFS). An example of the rotor rub-impact fault is utilized to demonstrate efficiency of this technique, and the results are contrasted with those of empirical mode decomposition (EMD). EFD performs better in the modal mixing problem than EMD and has excellent decomposition efficiency. It realizes IF feature extraction and offers a more precise TFR.

Research on a new diagnosis index for fixed-point rub-impact of rotor system

A model for a rotor system simulated with finite element method and a test bench are established in this paper. The fixed-point rub-impact fault on rotor system is studied (There are six clearances to be discussed). It is found that the nonlinear compositions are contained in the output responses by analyzing these vibration signals of the rotor system with fixed-point rub-impact, which implies that a nonlinear term appears in the system. In order to extract these compositions, a nonlinear method, named weighted contribution rate of nonlinear output frequency response functions (WNOFRFs), is adopted for analyzing the mechanism of these nonlinear problems in frequency domain. Furthermore, the Clenshaw-Curtis numerical integration method and the minimum mutual entropy principle (MMEP) are used to extract the fault diagnosis index J for identifying the fixed-point rub-impact fault of the system. At last, according to the simulation and experiment research, WNOFRFs can improve these higher-order nonlinear characteristics. And these findings prove that the index J has certain advantages compared with other indexes in the diagnosis of the rotor rub-impact fault.

Theoretical and Experimental Study on the Transient Time-Frequency Characteristics of the Bending-Torsional Coupling Motions of a Rub-Impact Dual-Rotor System

This paper focuses on the fault characteristics of the bending and torsional motions of a rub-impact dual-rotor system caused by aircraft flight maneuvers. The equations of the bending-torsional coupling motion of a dual-rotor system are established considering a low-pressure rotor rub-impact fault and the transient barrel roll flight of an aircraft. The 4th Runge-Kutta method with varied steps is used to obtain the bending and torsional responses. Then, the influences of the system parameters, including the rub-impact stiffness, friction coefficient, and rotating speed, on the bending and torsional motions of the dual-rotor system are investigated in detail. At last, a rotor rubbing experiment is carried out, verifying the validity of the simulation results. The results show that the rub-impact stiffness affects bending vibration significantly and the torsional motion is sensitive to the friction coefficient. Correspondingly, the torsional responses show apparent fractional fault frequencies and rotating fault frequencies within the whole region of the rub-impact stiffness. The bending responses can only display fault frequencies at certain rub-impact stiffness. As for the rotating speed, the torsional responses are also more effective than the bending responses for the rub-impact fault detection at the low- and high-speed regions. The results will contribute to a comprehensive basis for the rub-impact fault detection.

General synchroextracting chirplet transform: Application to the rotor rub-impact fault diagnosis

The rub-impact fault between the rotor and the stator surface is a common and serious failure in rotating machinery. The vibration signals acquired during the rotating machinery operation will carry the time-varying characteristics of rapid fluctuating instantaneous frequency (IF) when the healthy condition of rotor system changes. The proposed method is termed as the general synchroextracting chirplet transform which is based on the classical chirplet transform and employs the second-order synchroextracting transform to concentrate the time-frequency representation (TFR). Moreover, an adaptive demodulation parameters determination method based on kurtosis is proposed. The proposed method is non-sensitivity to noise, allows for signal reconstruction, and retains certain processing ability for multicomponent signals. The effectiveness and practical value of the method are verified by the numerical signals and the vibration signals of the heavy oil catalytic cracking machine which has the rotor rub-impact fault.

Generalized wavelet-based synchrosqueezing transform: Algorithm and applications

Time–frequency analysis is recognized as an efficient tool to characterize the time-varying feature from the oscillatory signal by transforming it into an identifiable form. Some traditional time–frequency transforms are subjected to poor time–frequency resolution or do not allow for mode reconstruction. As a postprocessing method, the synchrosqueezing transform has been utilized to tackle these problems. In this framework, a new method termed as generalized wavelet-based synchrosqueezing transform is developed in the current research work to deal with a strong modulated signal. The proposed method is capable to theoretically generate unbiased instantaneous frequency estimation at any order by defining a higher-order Taylor expansion signal model. The signal mapping procedure is also embedded in the algorithm to further improve the anti-noise robustness of the presented method. Numerical investigation of synthetic signal verifies the feasibility of the generalized wavelet-based synchrosqueezing transform as compared to previously developed approaches. Moreover, the practical implementation of the proposed method for the detection of the rotor rub-impact fault demonstrates that the generalized wavelet-based synchrosqueezing transform is qualified for machine fault diagnosis under the variable speed conditions.

Nonlinear Squeezing Time-Frequency Transform and Application in Rotor Rub-Impact Fault Diagnosis

Vibration signal analysis has been proved as an effective tool for condition monitoring and fault diagnosis for rotating machines in the manufacturing process. The presence of the rub-impact fault in rotor systems results in vibration signals with fast-oscillating periodic instantaneous frequency (IF). In this paper, a novel method for rotor rub-impact fault diagnosis based on nonlinear squeezing time-frequency (TF) transform (NSquTFT) is proposed. First, a dynamic model of rub-impact rotor system is investigated to quantitatively reveal the periodic oscillation behavior of the IF of vibration signals. Second, the theoretical analysis for the NSquTFT is conducted to prove that the NSquTFT is suitable for signals with fast-varying IF, and the method for rotor rub-impact fault diagnosis based on the NSquTFT is presented. Through a dynamic simulation signal, the effectiveness of the NSquTFT in extracting the fast-oscillating periodic IF is verified. The proposed method is then applied to analyze an experimental vibration signal collected from a test rig and a practical vibration signal collected from a dual-rotor turbofan engine for rotor rub-impact fault diagnosis. Comparisons are conducted throughout to evaluate the effectiveness of the proposed method by using Hilbert–Huang transform, wavelet-based synchrosqueezing transform (SST), and other methods. The application and comparison results show that the fast-oscillating periodic IF of the vibration signals caused by rotor rub-impact faults can be better extracted by the proposed method.

Matching synchrosqueezing transform: A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis

Abstract Synchrosqueezing transform (SST) can effectively improve the readability of the time-frequency (TF) representation (TFR) of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF). However, for signals composed of multiple components with fast varying IF, SST still suffers from TF blurs. In this paper, we introduce a time-frequency analysis (TFA) method called matching synchrosqueezing transform (MSST) that achieves a highly concentrated TF representation comparable to the standard TF reassignment methods (STFRM), even for signals with fast varying IF, and furthermore, MSST retains the reconstruction benefit of SST. MSST captures the philosophy of STFRM to simultaneously consider time and frequency variables, and incorporates three estimators (i.e., the IF estimator, the group delay estimator, and a chirp-rate estimator) into a comprehensive and accurate IF estimator. In this paper, we first introduce the motivation of MSST with three heuristic examples. Then we introduce a precise mathematical definition of a class of chirp-like intrinsic-mode-type functions that locally can be viewed as a sum of a reasonably small number of approximate chirp signals, and we prove that MSST does indeed succeed in estimating chirp-rate and IF of arbitrary functions in this class and succeed in decomposing these functions. Furthermore, we describe an efficient numerical algorithm for the practical implementation of the MSST, and we provide an adaptive IF extraction method for MSST reconstruction. Finally, we verify the effectiveness of the MSST in practical applications for machine fault diagnosis, including gearbox fault diagnosis for a wind turbine in variable speed conditions and rotor rub-impact fault diagnosis for a dual-rotor turbofan engine.

Matching Demodulation Transform With Application to Feature Extraction of Rotor Rub-Impact Fault

Conventional time-frequency representation (TFR) methods have played an important role in characterizing the time-frequency (TF) pattern of the nonstationary signal. In this paper, a new TFR algorithm, called matching demodulation transform (MDT), is introduced to extract the feature of highly oscillating frequency modulation for rotor rub-impact fault. When the early rub-impact fault occurs in the rotor system, the vibration signals will present frequency modulation feature because of the periodic rub-impact between the stator and the rotor. Through an iterative demodulation procedure, the highly oscillating frequency modulation feature is represented with satisfactory energy concentration in TF plane. The validity of the technique is then demonstrated on a real rotor system of a gas turbine with rub-impact fault. The analysis result of this application shows that the MDT method is powerful in the analysis of frequency modulation signals and is an effective tool for the feature extraction of rub-impact faults.

Rotor System Fault Diagnosis Based on Orbit Manifold Topological

A new fault diagnosis method based on orbit manifold topological space is put forward. Firstly, vibration signals from the working is reduced the noise by using EEMD for vibration signal noise reduction. Restored signal constitutes an orbit. An orbit of the orbit track points is defined as a dimension feature of the high-dimensional space; the high-dimensional characteristics space is reduced into lower-dimensional space based on LTSA algorithm, then orbit manifold sensitive characteristics in the topological space are obtained. It provides a new method for fault diagnosis of electrical system. Application of the method to test-bed of normal, misalignment, rotor rub-impact fault diagnosis of three types, and fault diagnosis of NC lathe spindle and eccentric properly, the correct rate is up to 99%. The validity of the algorithm is verified.

Normalized Complex Teager Energy Operator Demodulation Methodand Its Application to Rotor Rub-impact Fault Diagnosis

Normalized Hilbert transform(NHT) demodulation method introduces an empirical AM-FM decomposition to a monocomponent signal, achieving a separation of the envelope signal(i.e., instantaneous amplitude) and the FM signal, then instantaneous frequency can be directly extracted through Hilbert transform to the FM signal. Compared with direct HT method, NHT method could gain a better demodulation performance. However, studies show that the FM signal results from empirical AM-FM decomposition may contain riding waves, which are prone to negative frequency. Moreover, since Hilbert transform is still used, oscillations will inevitably occur near endpoints of the obtained instantaneous frequency by NHT method. Aiming at the drawbacks of NHT method, an improved empirical AM-FM decomposition for eliminating riding waves and an instantaneous frequency estimate based on complex Teager energy operator(CTEO) for the FM signal are presented, respectively. Furthermore, a new signal demodulation method named normalized complex Teager energy operator(NCTEO) demodulation is proposed, in which the improved empirical AM-FM decomposition is applied to a monocomponent signal to extract instantaneous amplitude, and NCTEO method is applied to the resulting FM signal to extract instantaneous frequency. Simulated signals experiments and earlyrub-impact fault signals analysis of rotor system verify the excellence and effectiveness of NCTEO method.

Trend analysis of rotor-to-stator impact-rub based on smooth support vector regression

Traditional methods of mathematical modelling usually get complicated non-linear models for rub-impact fault. Besides, time-frequency domain analysis is very difficult to solve the problem because of its own limitation. Based on analysis need of degree and trend of rub-impact fault, support vector regression (SVR) arithmetic is imported, which adopts method of time series analysis. Smooth support vector regression (SSVR) using smoothing method in SVR suggests a best tradeoff between complexity of model and learning ability to establish linear model which is able to reflect the implicit mechanism of series data. Model of Jeffcott rotor was established to simulate actual rotor rub-impact fault, and SSVR was applied to analyse trend of time series data of rub-impact fault. The results indicate that SSVR is obviously superior to neural network, and is effective to analyse trend of rotor-to-stator impact-rub fault, which has guiding significance for using and maintenance of rotating machine.

A Study on Localization of Acoustic Emission Source Based on Algebraic Neural Network

We propose the concept of algebraic neural network and introduce the algebraic algorithm in the network-training phase, which transforms the complex nonlinear optimization problem to a set of simple algebraic equations and achieves the best result directly. The experimental results show that rub-impact AE source localization problem is well solved by combining the nonlinear dynamic features and neural network, thus to provide cue to rotor rub-impact fault diagnosis and the application prospects and further research values are promising.

Application of D-S Evidence Deducing Theory to one Kind of Mechanical Fault Diagnosis

Rotor rub-impact fault may be diagnosed through several kind signal such as rotor vibration, stator vibration and rotor transient speed, and every signal include fault features of different side, so it is possible to improve diagnosis successful probability by mul-information fusing method. The fault identifying frame and combination diagnosis rules are determined using stator vibration and rotor transient speed signals. It is adopted to determine mass function by the S-function, and the deducing method is put up. After peak value of stator resonance demodulation and rotor transient speed fluctuation amplitude information are fused, the method is applied to diagnose rotor rub-impact fault successfully.