Demodulation Analysis Research Articles

Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition

The conventional method of detecting a gear fault is to demodulate the vibration signal collected from the gearbox based on the Hilbert transform; however, this requires human intervention and lacks sophistication. Empirical mode decomposition (EMD) is a significant timefrequency tool for adaptively decomposing vibration signals into a collection of intrinsic mode functions (IMFs); a fault feature can be extracted from one of IMFs to reveal the fault location and fault level of a gear or bearing in the mechanical drive system. In this paper, a multi-harmonic vibration model of a gearbox with fault modulation is presented, a conventional demodulation analysis using Hilbert transform is introduced, and the principle of EMD is illustrated. The Hilbert demodulation analysis and EMD are applied to processing field vibration signals collected from a wind turbine gearbox to detect a gear-pitting fault. The results show that EMD can extract the fault modulation information more adaptively and intelligently than Hilbert demodulation analysis can.

CQFB and PBP in Diagnosis of Local Gear Fault

The vibration signal of local gear fault is mainly composed of two components. One is the resonant signal and noise signal and the other one is the transient impulse signal including fault information. The quality factors corresponding to the two components are different. Hence, a method to diagnose local gear fault based on composite quality factor basis and parallel basis pursuit is proposed. First, two different quality factors bases are established using wavelet transform of variable quality factors to obtain the decomposition coefficient. Next, the parallel basis pursuit is adopted for the optimization of the decomposition coefficient. With the derived optimal decomposition coefficient, the resonant components with different quality factors can be reconstructed. By discussing the sparsity of signals treated with different quality factors bases, the suitable composite quality factor basis is selected to perform sparse decomposition on the signal. Besides, the obtained resonant component with low quality factor is subject to demodulation analysis, so as to derive the fault information. The feasibility and validity of the algorithm are shown by the results from simulation signal and practical application of local gear faults.

Restraining Method for End Effect of Local Mean Decomposition Based on Energy Operator Demodulation of Symmetrical Differencing

The local mean decomposition(LMD) method presents its own ability for processing nonlinear and non-stationary signals. It can effectively obtain the time-frequency characteristics of non-stationary signals. But there is an involved end effect in the course of local mean decomposition, and the end effect can cause the distortion of decomposition results. Based on the demodulation analysis of vibration signal, a new method for restraining the end effects of local mean decomposition based on the energy operator demodulation of symmetrical differencing. The signal is decomposed into a series of single component signals by the local mean decomposition method, then each single component signal can be demodulated by the method of symmetrical differencing energy operator, and the instantaneous amplitude and instantaneous frequency of each single component signal can be got, the time-frequency distribution of the signal is obtained. To evaluate the effect of endpoint suppression method, an evaluation criterion is defined, compared with the other two endpoint effect suppression methods, the superiority of the proposed method is verified. The results of simulation and practical signal analysis show that the method can restrain the end effects of LMD and implement rotating machinery fault diagnosis effectively.

Vibration Measurement and Fault Diagnosis System for Experimental Teaching

Based on LabVIEW, vibration measurement and diagnosis system of equipment was developed for experimental teaching. On the platform, vibration signals from running equipment can be sampled, displayed, stored, and analyzed in time domain, frequency domain and time-frequency domain. The advanced signal processing technology such as power spectrum analysis, cepstrum analysis, demodulation analysis can also be executed. Using pattern recognition technology, the processed signals can be integrated for intelligent diagnosis of equipment state. The experimental system is helpful for students to learn signal processing methods, and to design virtual instrument.

Diagnosis of Low-Speed Helical Gears Based on Hilbert Demodulation and Empirical Mode Decomposition

As the vibration signal of helical gears is nonlinear and nonstationary, it is very difficult to diagnose their faults based on the vibration signal analysis and processing, particularly when the gears rotate at low speed. In this paper, an applicable fault diagnosis approach is proposed based on Hilbert demodulation and EMD (empirical mode decomposition). Firstly, the modulated signals are extracted through Hilbert envelope demodulationand low pass filter. Furthermore, EMD is used to decompose the multi-component demodulated signal into a series of intrinsic mode functions (IMFs) whose instantaneous frequencies have a physically meaningful characterization of the original signal. Finally, the fault features of low-speed helical gear are obtained by the spectrum analysis to each IMF. The experiments of tooth broken fault diagnosis show that this method is more effective than traditional Hilbert demodulation analysis.

Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes

The vibration signals of faulty planetary gearboxes have complicated spectral structures due to the amplitude modulation and frequency modulation (AMFM) nature of gear damage induced vibration and the additional multiplicative amplitude modulation (AM) effect caused by the time-varying vibration transfer paths (for local gear damage case) and the passing planets (for distributed gear damage case). The spectral complexity leads to the difficulty in fault diagnosis of planetary gearboxes. Observing that both the amplitude envelope and the instantaneous frequency of planetary gearbox vibration signals are associated with the characteristic frequency of the faulty gear, a joint amplitude and frequency demodulation method is proposed for fault diagnosis of planetary gearboxes. In order to satisfy the mono-component requirement by instantaneous frequency estimation, a signal is firstly decomposed into product functions (PF) using the local mean decomposition (LMD) method. Then, the earliest extracted PF that has an instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is chosen for further analysis, because it contains most of the information about the gear fault. The amplitude demodulation analysis can be accomplished through Fourier transforming the amplitude envelope of the chosen PF. For the frequency demodulation analysis, Fourier transform is applied to the estimated instantaneous frequency of the chosen PF to reveal its fluctuating frequency, thus obtaining the spectrum of the instantaneous frequency. By joint application of the amplitude and frequency demodulation methods, planetary gearbox faults can be diagnosed by matching the dominant peaks in the envelope spectrum and the spectrum of instantaneous frequency with the theoretical characteristic frequencies of faulty gears. The performance of the proposed method is illustrated by simulated signal analysis, and is validated by experimental signal analysis of a lab planetary gearbox with intentionally created pitting and naturally developed wear.

A kurtosis-guided adaptive demodulation technique for bearing fault detection based on tunable-Q wavelet transform

This paper presents an adaptive demodulation technique for bearing fault detection. It is implemented via the tunable-Q wavelet transform (TQWT). With the TQWT, the bearing vibration signal is decomposed into sub-signals corresponding to different band-pass filters of the TQWT. Kurtosis as an effective indicator of signal impulsiveness is adopted to guide the merging of the sub-signals leading to a signal component which contains information most relevant to the bearing fault. The purpose of the proposed approach is to adaptively search for the best filter for envelope demodulation analysis. In fact, the implementation of the proposed method can be interpreted as the process to obtain the optimal filter for the Hilbert demodulation analysis by two steps of merging of the band-pass filters of the TQWT. The effectiveness of the proposed method has been demonstrated by both simulation and experimental analyses.

Resonance-Based Nonlinear Demodulation Analysis Method of Rolling Bearing Fault

Numerous mechanical nonstationary fault signals are a mixture of sustained oscillations and nonoscillatory transients, which are difficult to efficiently analyze using linear methods. We propose a nonlinear demodulation analysis method based on resonance and apply it to the fault diagnosis of rolling bearings. Unlike conventional demodulation methods that use frequency-based analysis and filtering techniques, our nonlinear demodulation analysis method is a decomposition demodulation of the signals according to different resonance based on Q-factors. When a local rolling bearing fault such as pitting is present, the fault vibration signals consist of the regular vibration signals and noise (a high resonance component containing multiple simultaneous sustained oscillations) and a transient impulse signal (a low resonance component being a signal containing nonoscillatory transients of faults). The regular vibration signal is a narrowband signal that has a high Q-factor, and the transient impulse signal is a wideband signal that has a low Q-factor. Using our resonance-based nonlinear demodulation analysis method, we decompose the signal into high resonance, low resonance, and residual components. Then, we perform a demodulation analysis on the low resonance component that includes the fault information. We have verified the feasibility and validity of the algorithm by analyzing the results of experimental and engineering signals.

Fault diagnosis of planetary gearboxes via torsional vibration signal analysis

Torsional vibration signals are theoretically free from the amplitude modulation effect caused by time variant vibration transfer paths due to the rotation of planet carrier and sun gear, and therefore their spectral structure are simpler than transverse vibration signals. Thus, it is potentially easy and effective to diagnose planetary gearbox faults via torsional vibration signal analysis. We give explicit equations to model torsional vibration signals, considering both distributed gear faults (like manufacturing or assembly errors) and local gear faults (like pitting, crack or breakage of one tooth), and derive the characteristics of both the traditional Fourier spectrum and the proposed demodulated spectra of amplitude envelope and instantaneous frequency. These derivations are not only effective to diagnose single gear fault of planetary gearboxes, but can also be generalized to detect and locate multiple gear faults. We validate experimentally the signal models, as well as the Fourier spectral analysis and demodulation analysis methods.

Position and Speed Detection of Synchronous Motor Rotor

This article describes a method to detect the position and speed in a rotary transformer pole AC synchronous motor. This method gives the pole position and velocity signal demodulation circuit and analyses the demodulation principle. After practical testing, this approach is reliable with high detection accuracy. Therefore, it is fully able to meet the needs of CNC machine tools with high performance in AC server system.

Research on Fault Diagnoses of Rolling Bearing Based on the Energy Operator Demodulation Approach

This paper will introduce and optimize a new model of demodulation algorithm named Energy Operator Demodulation Approach to improve the limitation of the demodulation analysis widely used in many fields at present. Compared with Hilbert transform method, and optimized algorithm through Simulation test, which shows optimized Energy Operator algorithm is more simple ,faster and accurate than other algorithms. Finally, it is verified that research on fault diagnoses of rolling bearing based on the energy operator demodulation approach is very effective.

Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet

Due to the character of diversity and complexity, the compound faults detection of rotating machinery under non-stationary operation turns into a challenging task. Multiwavelet with two or more base functions and many excellent properties provides a possibility to detect and extract all the features of compound faults at one time. However, the fixed basis functions independent of the vibration signal may decrease the accuracy of fault detection. Moreover, the decomposition result of discrete multiwavelet transform does not possess time invariance, which is harmful to extract the feature of periodical impulses. To overcome these deficiencies, based on the Hermite splines interpolation, taking the minimum envelope spectrum entropy as the optimization objective, adaptive redundant lifting multiwavelet is developed. Additionally, in order to eliminate error propagation of decomposition results, adaptive redundant lifting multiwavelet is improved by adding the normalization factors. As an effective method, Hilbert transform demodulation analysis is used to extract the fault feature from the high frequency modulation signal. The proposed method incorporating improved adaptive redundant lifting multiwavelet (IARLM) with Hilbert transform demodulation analysis is applied to compound faults detection for the simulation experiment, rolling element bearing test bench and traveling unit of electric locomotive. Compared with some other fault detection methods, the results show the superior effectiveness and reliability on the compound faults detection.

A two-step procedure for estimation of instantaneous rotational speed with large fluctuations

This paper addresses the issue of estimating the machine rotational speed based on vibration signal measurement. Advantages and limitations of existing methods are presented. Most of these methods give insufficient results when dealing either with significant variations of the rotational speed or with signals of relatively complex spectral characteristic. In order to overcome these obstacles, a new two-step method for instantaneous frequency estimation is proposed. The described method is based on principles of phase demodulation and joint time-frequency analysis, taking advantage of both of them. The paper presents the method and the algorithm for its implementation on the example of a simulated vibration signal. Finally, test rig experiment vibration data is used to test the efficiency of the method.

Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation

Planetary gearboxes play an important role in wind turbine drive trains. Fault diagnosis of planetary gearboxes is a key topic for maintenance of wind turbines. Considering the spectral complexity of planetary gearbox vibration signals as well as their amplitude modulation and frequency modulation (AMFM) nature, we propose a simple yet effective method to diagnose planetary gearbox faults based on amplitude and frequency demodulations. We use the energy separation algorithm to estimate the amplitude envelope and instantaneous frequency of modulated signals for further demodulation analysis, by exploiting the adaptability of Teager energy operator to instantaneous changes in signals and the fine time resolution. However, the energy separation algorithm requires signals to be mono-components. To satisfy this requirement, we decompose signals into intrinsic mode functions (IMFs) using the ensemble empirical mode decomposition (EEMD) method as it can decompose any signal into mono-components. We further propose a criterion to guide the selection of the most relevant IMF for demodulation analysis according to the wavelet-like filter nature of EEMD and the AMFM characteristics of the planetary gearbox vibration signals. By matching the dominant peaks in the Fourier spectra of the obtained amplitude envelope and instantaneous frequency with the theoretical characteristic frequency of each gear, we can diagnose planetary gearbox faults. The principle and effectiveness of the proposed method are illustrated by simulation and the experimental analysis of signals from a planetary gearbox of a wind turbine test rig. With the proposed method, both the wear and chipping faults can be detected and located for a sun gear of the planetary gearbox test rig.

Adaptive redundant lifting wavelet transform based on fitting for fault feature extraction of roller bearings.

A least square method based on data fitting is proposed to construct a new lifting wavelet, together with the nonlinear idea and redundant algorithm, the adaptive redundant lifting transform based on fitting is firstly stated in this paper. By variable combination selections of basis function, sample number and dimension of basis function, a total of nine wavelets with different characteristics are constructed, which are respectively adopted to perform redundant lifting wavelet transforms on low-frequency approximate signals at each layer. Then the normalized lP norms of the new node-signal obtained through decomposition are calculated to adaptively determine the optimal wavelet for the decomposed approximate signal. Next, the original signal is taken for subsection power spectrum analysis to choose the node-signal for single branch reconstruction and demodulation. Experiment signals and engineering signals are respectively used to verify the above method and the results show that bearing faults can be diagnosed more effectively by the method presented here than by both spectrum analysis and demodulation analysis. Meanwhile, compared with the symmetrical wavelets constructed with Lagrange interpolation algorithm, the asymmetrical wavelets constructed based on data fitting are more suitable in feature extraction of fault signal of roller bearings.

2 μm双平衡式外差探测IQ解调与信噪比研究

2 μm双平衡式外差探测IQ解调与信噪比研究

Feature analysis in frequency domain of Duffing system based on general local frequency

Owing to the limitations of the concept of frequency for power spectrum and the inherent defects of Fourier transform, a novel concept of general local frequency is proposed. Based on a approach to adaptive peak decomposition, the dynamic feature in frequency domain varying with parameter r of Duffing system driven by periodic signal is investigated. And a phenomenon of frequency bifurcation is found. Moreover, coninuous frequency bands exist near the central frequency of chaos time seriers at different values of parameter r and their shapes are similar. By demodulation analysis of Hilbert transform, the modulation characteristic and modulation similarity of chaos time seriers are summarized. The above study shows that the proposed approach to general local frequency based on adaptive peak is effective for freature extraction in frequency domain of Duffing system. It provides a new method to observe a continunous distribution of frequency bands for the non-linear system in chaotic state.

Vector Backscattered Signals Improve Piggyback Modulation for Sensing With Passive UHF RFID Tags

We expand our “piggyback modulation” technique to integrate sensing functions with existing standard passive UHF RFID tags through wireless coupling. Instead of focusing on the scalar backscattered power that is proportional to the radar cross section (RCS), we exploit the vector backscattered signal with piggyback modulation. Measuring both amplitude and phase of the backscattered wave makes advanced demodulation possible. The measurement setup, experimental results, demodulation analysis, and numerical simulation for the conventional amplitude-shift keying (ASK) and quasi-4-quadrature amplitude modulation (quasi-4-QAM) are presented. Quasi-4-QAM demodulation reduces the interference between two independent data streams and improves the bit error rate for the same RF power. While we do not show explicit data from any given sensor, we do show that a generic sensor signal will be improved with our technique.

Research of Acoustic Emission Signal De-Noising Based on Lifting Wavelet Packet

Aiming at the extraction of failure character signal for early acoustic emission (AE) signals, a method to combine lifting wavelet packet with spectrum zoom technology is developed. Using lifting wavelet packet de-noising method, not only filters the noise signal, but also retains the feature information. After the signals de-noising, combining the envelope demodulation analysis with the spectrum zoom technology, which can effective extract the weak fault characteristic frequency The result of simulated signals and experimental signals shows that the proposed method has better noise reduction with a higher signal noise ratio (SNR) and lower mean square error (MSE), and it can successfully extract the fault characteristic frequency of rotating machinery early AE signals.

Comparison Of Two Methods For Demodulation Of Pulse Signals - Application In Case Of Central Sleep Apnea

In the field of 24/7 human health monitoring, pervasive computing makes possible the continuous analysis of physiological parameters from an ambulatory device with a great acceptability. This paper presents two methods for obtaining cardiac and respiratory rates from a single arterial pressure signal: AM-FM demodulation and Singular Spectrum Analysis (SSA). With the aim to monitor sleep apnea, two simulated central sleep apnea were performed and recorded with Biopac reference system. The results showed a good evaluation of the cardiac rate with Singular Spectrum Analysis and bad results with AM-FM demodulation. For the respiration rate, some other signals were tested with average results for both methods. Further experiments will deal with real sleep apnea cases and algorithm improvements.