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.

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.

Gear fault feature extraction based on fractional Fourier transform

2016 ◽  
pp. 931-936
Author(s):  
Hongfang Chen ◽  
Yanqiang Sun ◽  
Zhaoyao Shi ◽  
Jiachun Lin ◽  
Zaihua Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Gear Fault ◽  
Fault Feature Extraction

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.

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 Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

2009 ◽  
Vol 19 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Tomasz Barszcz
Keyword(s):  
Fault Detection ◽  
Adaptive Filter ◽  
Complex Structure ◽  
Reference Signal ◽  
The Self ◽  
Noise Component ◽  
Vibration Signals ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Self Adaptive

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and IdentificationThe paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then applied—first to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

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