Gear Fault Detection and Diagnosis Based on its Fabrication Material

2011 ◽  
Vol 321 ◽  
pp. 140-145
Author(s):  
Shu Feng Ai
Keyword(s):  
Fault Detection And Diagnosis ◽  
Series Data ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Intrinsic Oscillation ◽  
Detection And Diagnosis ◽  
The Hilbert Transform

A novel application of Hilbert-Huang transform method to fault diagnosis of gear crack is presented. The methodology developed in this paper decomposes the original times series data in intrinsic oscillation modes, using the empirical mode decomposition. Then the Hilbert transform is applied to each intrinsic mode function. Therefore, the time-frequency distribution is obtained.

Teager-Huang Transform Based Gear Crack Fault Detection and Diagnosis

2013 ◽  
Vol 739 ◽  
pp. 418-422
Author(s):  
Ya Ning Wang
Keyword(s):  
Energy Operator ◽  
Fault Detection And Diagnosis ◽  
Series Data ◽  
Transform Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Intrinsic Oscillation ◽  
Detection And Diagnosis ◽  
Gear Crack

The newly developed Teager-Huang transform (THT) enables one to look at the evolution in time of a signals frequency components. In this paper, a novel application of Teager-Huang transform method to fault diagnosis of gear crack is presented. The methodology developed in this paper decomposes the original times series data in intrinsic oscillation modes, using the empirical mode decomposition (EMD). Then the Teager Kaiser Energy Operator (TKEO) technique is applied to each intrinsic mode function (IMF). Therefore, the time-frequency distribution of the vibration is obtained. The basic method is introduced in detail. The Teager-Huang transform is applied in the research of the faults diagnosis of the gear crack. The experimental results show that Teager-Huang transform can effectively diagnosis the fault of the gear crack.

EMD and Envelope Spectrum Based Bearing Fault Detection

2012 ◽  
Vol 459 ◽  
pp. 233-237 ◽  
Author(s):  
Zhen Tao Li ◽  
Hui Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Oscillation Modes ◽  
Intrinsic Oscillation ◽  
Detection And Diagnosis ◽  
Envelope Spectrum

A novel method to fault diagnosis of bearing based on empirical mode decomposition (EMD) and envelope spectrum is presented. EMD method is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original vibration signal in intrinsic oscillation modes, using the empirical mode decomposition. Then the envelope spectrum is applied to the selected intrinsic mode function that stands for the bearing faults. The basic principle is firstly introduced in detail. Then the EMD is applied in the research of the fault detection and diagnosis of the bearing. The experimental results show that the proposed method based on EMD and envelope spectrum analysis technique can effectively diagnose the faults of bearing.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

scholarly journals Gear Fault Detection Based on Teager-Huang Transform

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Li ◽  
Haiqi Zheng ◽  
Liwei Tang
Keyword(s):  
Fault Detection ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Instantaneous Amplitude ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Novel Method ◽  
Detection And Diagnosis

Gear fault detection based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique is presented. This novel method is named as Teager-Huang transform (THT). EMD can adaptively decompose the vibration signal into a series of zero mean Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the Intrinsic Mode Functions at any instant. The experimental results provide effective evidence that Teager-Huang transform has better resolution than that of Hilbert-Huang transform. The Teager-Huang transform can effectively diagnose the fault of the gear, thus providing a viable processing tool for gearbox defect detection and diagnosis.

FAST EMPIRICAL MODE DECOMPOSITIONS OF MULTIVARIATE DATA BASED ON ADAPTIVE SPLINE-WAVELETS AND A GENERALIZATION OF THE HILBERT–HUANG–TRANSFORMATION (HHT) TO ARBITRARY SPACE DIMENSIONS

2010 ◽  
Vol 02 (03) ◽  
pp. 337-358 ◽  
Author(s):  
ROLAND PABEL ◽  
ROBIN KOCH ◽  
GABRIELA JAGER ◽  
ANGELA KUNOTH
Keyword(s):  
Hilbert Transform ◽  
Multivariate Data ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Local Means ◽  
Spline Wavelets ◽  
Mode Decomposition ◽  
Arbitrary Space ◽  
The Hilbert Transform

The Hilbert–Huang-Transform (HHT) has proven to be an appropriate multiscale analysis technique specifically for nonlinear and nonstationary time series on non-equidistant grids. It is empirically adapted to the data: first, an additive decomposition of the data (empirical mode decomposition, EMD) into certain multiscale components is computed, denoted as intrinsic mode functions. Second, to each of these components, the Hilbert transform is applied. The resulting Hilbert spectrum of the modes provides a localized time-frequency spectrum and instantaneous (time-dependent) frequencies. For the first step, the empirical decomposition of the data, a different method based on local means has been developed by Chen et al. (2006). In this paper, we extend their method to multivariate data sets in arbitrary space dimensions. We place special emphasis on deriving a method which is numerically fast also in higher dimensions. Our method works in a coarse-to-fine fashion and is based on adaptive (tensor-product) spline-wavelets. We provide some numerical comparisons to a method based on linear finite elements and one based on thin-plate-splines to demonstrate the performance of our method, both with respect to the quality of the approximation as well as the numerical efficiency. Second, for a generalization of the Hilbert transform to the multivariate case, we consider the Riesz transformation and an embedding into Clifford-algebra valued functions, from which instantaneous amplitudes, phases and orientations can be derived. We conclude with some numerical examples.

scholarly journals Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. H29-H37 ◽  
Author(s):  
Bradley Matthew Battista ◽  
Camelia Knapp ◽  
Tom McGee ◽  
Vaughn Goebel
Keyword(s):  
Signal Processing ◽  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
The Hilbert Transform

Advancements in signal processing may allow for improved imaging and analysis of complex geologic targets found in seismic reflection data. A recent contribution to signal processing is the empirical mode decomposition (EMD) which combines with the Hilbert transform as the Hilbert-Huang transform (HHT). The EMD empirically reduces a time series to several subsignals, each of which is input to the same time-frequency environment via the Hilbert transform. The HHT allows for signals describing stochastic or astochastic processes to be analyzed using instantaneous attributes in the time-frequency domain. The HHT is applied herein to seismic reflection data to: (1) assess the ability of the EMD and HHT to quantify meaningful geologic information in the time and time-frequency domains, and (2) use instantaneous attributes to develop superior filters for improving the signal-to-noise ratio. The objective of this work is to determine whether the HHT allows for empirically-derived characteristics to be used in filter design and application, resulting in better filter performance and enhanced signal-to-noise ratio. Two data sets are used to show successful application of the EMD and HHT to seismic reflection data processing. Nonlinear cable strum is removed from one data set while the other is used to show how the HHT compares to and outperforms Fourier-based processing under certain conditions.

Application of Hilbert-Huang Transform Method on Fault Diagnosis for Wind Turbine Rotor

2009 ◽  
Vol 413-414 ◽  
pp. 159-166
Author(s):  
Qian Huang ◽  
Dong Xiang Jiang ◽  
Liang You Hong
Keyword(s):  
Wind Turbine ◽  
Original Data ◽  
Turbine Rotor ◽  
Noticeable Effect ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Wind Turbine Rotor

Many signals of wind turbine faults are non-stationary and have highly complex time-frequency characteristics. Traditional time-frequency analysis method, such as Windowed Fourier Transform method, has no noticeable effect in handing non-stationary signals. Hilbert-Huang Transform (HHT) is a new signal processing method for analyzing the non-stationary mechanical signals. Based on Empirical Mode Decomposition (EMD), the Intrinsic Mode Function (IMF) in HHT can reflect the intrinsic physical characteristics of original data. Moreover, it is a good way to identify the faults involving a breakdown change. First, the principles and advantages of the HHT are presented in detail in this paper. Then, three typical faults of wind turbine rotor, such as rotor imbalance, aerodynamic asymmetry due to blade surface roughness and yaw misalignment are discussed by the HHT. Last, reasonable conclusions are drawn by the comparison between this method and the Wavelet Transform (WT) method with the help of simulation fault signals. The results show the effectiveness of HHT method for diagnosing those faults of wind turbine rotor.

scholarly journals Adaptive Overhead Transmission Lines Auto-Reclosing Based on Hilbert–Huang Transform

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5416 ◽  
Author(s):  
Arman Ghaderi Baayeh ◽  
Navid Bayati
Keyword(s):  
Transmission Lines ◽  
Threshold Level ◽  
Test Cases ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform ◽  
Automatic Reclosing ◽  
System Configurations ◽  
Fault Clearance

This paper presents a reliable and fast index to detect the instant of arc extinction for adaptive single-pole automatic reclosing (ASPAR). The proposed method is a simple technique for ASPAR on shunt compensated transmission lines using the Hilbert–Huang Transform (HHT). The HHT method is a combination of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The first intrinsic mode function (IMF1) decomposed by EMD, which contains high frequencies of the faulty phase voltage, was used to calculate the proposed index. HT calculates the first IMF spectrum in the time-frequency domain. The presented index is the sum of all frequency contents below 55 Hz, which remains very low until the fault clearance. The proposed method uses a global threshold level and therefore no adjustment is needed for different transmission systems. This method is effective for various system configurations including different fault locations, line loading, and various shunt reactor configurations, designs, compensation rates, and placement. The performance of the method was verified using 324 test cases simulated in electromagnetic transient program (EMTP) related to a 345 kV transmission line. For all the test cases, the algorithm successfully operated with an average reclosing time delay of 32 ms.

A Method of Fault Detection and Diagnosis Based on Time-Frequency Analysis

2012 ◽  
Vol 490-495 ◽  
pp. 1407-1410
Author(s):  
Ying Bo Liang ◽  
Li Hong Zhang ◽  
Jin Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Maximum Modulus ◽  
Fault Detection And Diagnosis ◽  
Time Frequency ◽  
Multi Scale ◽  
Mode Function ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Detection And Diagnosis

In the paper the authors propose a combination of the EMD (empirical mode decomposition)method and the wavelet analysis to suppress the noise and fault detection and diagnosis, It adopts empirical mode decomposition to current signal ,obtained a series of IMFs(Intrinsic Mode Function),removing the first IMF component to denosing,and then analyzed multi-scale ,using signal become mutated have the maximum modulus determine the time that the failure appeared ,the results show that this method determine the time that the failure appeared.

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