Cyclostationary Analysis Based Gear Fault Diagnosis

2012 ◽  
Vol 459 ◽  
pp. 190-194
Author(s):  
Zhen Tao Li ◽  
Hui Li
Keyword(s):  
Vibration Signal ◽  
Random Processes ◽  
Spectral Structure ◽  
Spectral Correlation ◽  
Detection And Identification ◽  
Spectral Correlation Density ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Cyclostationary Signals ◽  
Fault Characteristic

Gearbox vibrations acquired by sensors are random cyclostationary signals, which are a combination of periodic and random processes due to the machine’s rotation cycle and interaction with the real world. Since the spectral structure of a gear vibration signal is mainly characterized by the interaction between the meshing harmonics and their sidebands, the spectral correlation density (SCD) function has been applied to gear monitoring. This approach is capable of completely extracting the fault characteristic frequencies related to the defect. This gives a desirable ability to detect the singularity characteristic of a signal precisely. This technique permits both fault detection and identification of the damaged gear. The experimental results show that the proposed method based on cyclostationary analysis can effectively diagnose the faults of gear.

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

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.

Periodical Feature Extraction and Fault Diagnosis for Gearbox Using Local Cepstrum Technology

2015 ◽  
Author(s):  
B. Li ◽  
X. N. Zhang
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Background Noise ◽  
Average Effect ◽  
Premature Failure ◽  
Cepstrum Analysis ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Detection Principle

Results of numerous studies and experiments show that cepstrum analysis has the ability of simplifying the equally spaced sideband feature in the spectrum and highlights the signal components of defects. However, for most cases of early gear failure, the periodic phenomenon is always buried in strong background noises and the interference of the rotating frequency with its harmonics. Moreover, the features would be further weakened by the average effect of Fourier transform after cepstrum processing. In this paper, an improved cepstrum method named local cepstrum is proposed. The detection principle of local cepstrum is mainly based on the part of spectrum information to enhance the capability of extracting periodical features of detected signals. Besides, the autocorrelation and extended Shannon Entropy Function are also involved enhancing the periodic impulsive features. In the end, only several distinct lines with larger magnitudes would be left in the local cepstrum, which is very effective for gear fault detection and identification. Both simulation and experimental analysis show that the proposed method, which is more sensitive to the gear failure compared with conventional cepstrum analysis, could partially eliminate the interference of background noise and extract the periodical features of premature failure signals effectively.

Gear Fault Diagnosis Based on Second Order Cyclostationary Analysis

2011 ◽  
Vol 474-476 ◽  
pp. 1012-1018 ◽  
Author(s):  
Hui Li
Keyword(s):  
Fault Diagnosis ◽  
Real World ◽  
Random Processes ◽  
Second Order ◽  
Experimental Result ◽  
Feature Extracting ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Cyclostationary Signals ◽  
Gear Crack

Gearbox vibrations are random cyclostationary signals which are a combination of periodic and random processes due to the machine’s rotation cycle and interaction with the real world. The combinations of such components are best considered as cyclostationary. This paper discusses which second order cyclostationary statistics should be used for fault diagnosis of gear crack. The second order cyclostationary statistical methods are firstly introduced and then applied to fault diagnosis of gear crack. This approach is capable of completely extracting the characteristic fault frequencies related to the defect. Experiment results show that the second order cyclostationary statistics is powerful and effective in feature extracting and fault detecting for gearbox. The experimental result shows that the second order cyclostationary statistics can effectively diagnosis gear localized crack fault.

Application of slice spectral correlation density to gear defect detection

2006 ◽  
Vol 220 (9) ◽  
pp. 1385-1392 ◽  
Author(s):  
G Bi ◽  
J Chen ◽  
F C Zhou ◽  
J He
Keyword(s):  
Phase Modulation ◽  
Early Stage ◽  
Vibration Signal ◽  
Signal Amplitude ◽  
Spectral Correlation ◽  
Stage Of Development ◽  
Spectral Correlation Density ◽  
Character Identification ◽  
Strong Amplitude ◽  
Direct Reflection

The most direct reflection of gear defect is the change in the amplitude and phase modulations of vibration. The slice spectral correlation density (SSCD) method presented in this paper can be used to extract modulation information from the gear vibration signal; amplitude and phase modulation information can be extracted either individually or in combination. This method can detect slight defects with comparatively evident phase modulation as well as serious defects with strong amplitude modulation. Experimental vibration signals presenting gear defects of different levels of severity verify to its character identification capability and indicate that the SSCD is an effective method, especially to detect defects at an early stage of development.

A fast filtering method based on adaptive impulsive wavelet for the gear fault diagnosis

2020 ◽  
pp. 095440622090624
Author(s):  
Gang Yu ◽  
Mang Gao ◽  
Chengli Jia
Keyword(s):  
Fault Diagnosis ◽  
Characteristic Frequency ◽  
High Efficiency ◽  
Damping Ratio ◽  
Engineering Application ◽  
Vibration Signal ◽  
Optimal Parameters ◽  
Gear Fault ◽  
Shannon Wavelet ◽  
Fault Characteristic

The useful fault features applied for the fault diagnosis are usually overwhelmed by noise and other interference factors in rotation machinery. The impulses masked in vibration signals can represent the faults of gears or bearings in a gearbox. The key to finding impulsive components is to identify the modeling parameters (such as damping ratio, central frequency) of a transient (Morlet wavelet, Laplace wavelet), which can be used as an adaptive filter to denoise the vibration signal. However, its engineering application is limited by the time-consuming computation. In order to tackle this issue, a fast algorithm based on an adaptive impulsive wavelet is proposed to filter the fault signal so that the fault characteristic frequency can be identified. Firstly, a correlation coefficient maximum criterion is employed to find one of the optimal parameters of the impulsive wavelet. Then, the other parameter is optimized by the minimum Shannon wavelet entropy criterion. Finally, the impulsive wavelet filter with optimal parameters is applied to extract the fault characteristic frequency. Simulation signals are applied to verify the efficiency of the proposed approach, and comparison analysis is conducted as well. Further, the proposed method is applied to detect the gear fault of a gearbox. The experimental results show that the proposed method is effective with high efficiency.

Robust fault-tolerant flight control of quadrotor against faults in multiple motors

2021 ◽  
pp. 095441002199954
Author(s):  
Dinesh D Dhadekar ◽  
S E Talole
Keyword(s):  
Flight Control ◽  
Tracking Control ◽  
Fault Tolerant ◽  
External Disturbances ◽  
Detection And Identification ◽  
Attitude Tracking ◽  
Fault Detection And Identification ◽  
Attitude Tracking Control ◽  
Disturbance Estimator

In this article, position and attitude tracking control of the quadrotor subject to complex nonlinearities, input couplings, aerodynamic uncertainties, and external disturbances coupled with faults in multiple motors is investigated. A robustified nonlinear dynamic inversion (NDI)-based fault-tolerant control (FTC) scheme is proposed for the purpose. The proposed scheme is not only robust against aforementioned nonlinearities, disturbances, and uncertainties but also tolerant to unexpected occurrence of faults in multiple motors. The proposed scheme employs uncertainty and disturbance estimator (UDE) technique to robustify the NDI-based controller by providing estimate of the lumped disturbance, thereby enabling rejection of the same. In addition, the UDE also plays the role of fault detection and identification module. The effectiveness and benefits of the proposed design are confirmed through 6-DOF simulations and experimentation on a 3-DOF Hover platform.

scholarly journals A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2922
Author(s):  
Fan Zhang ◽  
Ye Wang ◽  
Yanbin Gao
Keyword(s):  
Fault Detection ◽  
Parametric Bootstrap ◽  
Integrated System ◽  
Range Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Multiple Faults ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Tightly Coupled

Fault detection and identification are vital for guaranteeing the precision and reliability of tightly coupled inertial navigation system (INS)/global navigation satellite system (GNSS)-integrated navigation systems. A variance shift outlier model (VSOM) was employed to detect faults in the raw pseudo-range data in this paper. The measurements were partially excluded or included in the estimation process depending on the size of the associated shift in the variance. As an objective measure, likelihood ratio and score test statistics were used to determine whether the measurements inflated variance and were deemed to be faulty. The VSOM is appealing because the down-weighting of faulty measurements with the proper weighting factors in the analysis automatically becomes part of the estimation procedure instead of deletion. A parametric bootstrap procedure for significance assessment and multiple testing to identify faults in the VSOM is proposed. The results show that VSOM was validated through field tests, and it works well when single or multiple faults exist in GNSS measurements.

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