Two-Stage Adaptive Line Enhancer and Sliced Wigner Trispectrum for the Characterization of Faults from Gear Box Vibration Data

1999 ◽  
Vol 121 (4) ◽  
pp. 488-494 ◽  
Author(s):  
S. K. Lee ◽  
P. R. White
Keyword(s):  
Background Noise ◽  
Two Stage ◽  
Vibration Signals ◽  
Time Frequency ◽  
Vibration Data ◽  
Frequency Representation ◽  
Frequency Domains ◽  
Adaptive Line Enhancer ◽  
Gear Fault

Impulsive sound and vibration signals in gears are often associated with faults which result from impacting and as such these impulsive signals can be used as indicators of faults. However it is often difficult to make objective measurements of impulsive signals because of background noise signals. In order to ease the measurement of impulsive sounds embedded in background noise, it is proposed that the impulsive signals are enhanced, via a two stage ALE (Adaptive Line Enhancer), and that these enhanced signals are then analyzed in the time and frequency domains using a Wigner higher order time-frequency representation. The effectiveness of this technique is demonstrated by application to gear fault data.

Design of Adaptive Filter for Health Monitoring on a Gearbox

2006 ◽  
Vol 321-323 ◽  
pp. 1237-1240
Author(s):  
Sang Kwon Lee ◽  
Jung Soo Lee
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Adaptive Filter ◽  
Health Monitoring ◽  
Background Noise ◽  
Adaptive Signal Processing ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Signal

Impulsive vibration signals in gearbox are often associated with faults, which lead to due to irregular impacting. Thus these impulsive vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive signal embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within laboratory gearbox.

Fault Identification for Rotating Machinery Using Adaptive Signal Processing and Time-Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Signal Processing ◽  
Background Noise ◽  
Combustion Engine ◽  
Adaptive Signal Processing ◽  
Rotating Machinery ◽  
Fault Identification ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Signal

Abstract Impulsive sound and vibration signals in rotating machinery are often associated with faults which lead to due to irregular impacting. Thus these impulsive sound and vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive sounds embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within internal combustion engine and industrial gear.

scholarly journals Characterization of Frequency Domains of Bandlimited Frame Multiresolution Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1050
Author(s):  
Zhihua Zhang
Keyword(s):  
Frequency Domain ◽  
Multiresolution Analysis ◽  
Background Noise ◽  
Sparse Reconstruction ◽  
Time Frequency ◽  
Processing Data ◽  
Narrowband Signal ◽  
Frequency Domains ◽  
Frame Multiresolution Analysis

Framelets have been widely used in narrowband signal processing, data analysis, and sampling theory, due to their resilience to background noise, stability of sparse reconstruction, and ability to capture local time-frequency information. The well-known approach to construct framelets with useful properties is through frame multiresolution analysis (FMRA). In this article, we characterize the frequency domain of bandlimited FMRAs: there exists a bandlimited FMRA with the support of frequency domain G if and only if G satisfies G⊂2G, ⋃m2mG≅Rd, and G\G2⋂G2+2πν≅∅(ν∈Zd).

TIME-FREQUENCY ANALYSIS BY EVOLUTIONARY PERIODOGRAM WITH APPLICATION IN GEAR FAULT DIAGNOSIS

2010 ◽  
Vol 08 (05) ◽  
pp. 679-693 ◽  
Author(s):  
M. A. AL-MANIE ◽  
W. J. WANG
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Background Noise ◽  
Alternative Technique ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Single Function ◽  
Gear Fault ◽  
Gear Fault Diagnosis

The evolutionary periodogram has been introduced to mechanical fault diagnosis and relationship between the evolutionary periodogram and time-frequency spectrogram has been investigated. The evolutionary periodogram is unveiled as an especially windowed spectrogram, and is applied to gearbox fault diagnosis. It has been shown that the window used in the evolutionary periodogram is not a single function but a combination of a set of functions. Two cases of gearbox diagnosis are presented as examples of application. Vibration signals and a synchronous signal are collected for the analysis. The time synchronous averaging is used to reduce background noise or random transients to enhance the periodicity of a specific gear rotation. The performance of the evolutionary periodogram has been compared with the spectrogram for gear diagnosis, showing that the evolutionary periodogram is an alternative technique in time-frequency analysis for fault detection and better resolution can be obtained as more choices are offered by the way of constructing the window.

Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ravikumar KN ◽  
Hemantha Kumar ◽  
Kumar GN ◽  
Gangadharan KV
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Internal Combustion ◽  
Frequency Characteristics ◽  
Ic Engine ◽  
Vibration Signals ◽  
Content Type ◽  
Time Frequency ◽  
Gear Fault

PurposeThe purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.Design/methodology/approachVibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.FindingsThe obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.Practical implicationsThe proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.Originality/valueIn this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

A novel health indicator developed using filter-based feature selection algorithm for the identification of rotor defects

2020 ◽  
pp. 1748006X2091695 ◽  
Author(s):  
Anil Kumar ◽  
CP Gandhi ◽  
Xiaoyang Liu ◽  
Yi Liu ◽  
Yuqing Zhou ◽  
...  
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Signal Frequency ◽  
Health Indicator ◽  
Angular Misalignment ◽  
Time Frequency ◽  
Vibration Data ◽  
Time Domain Signal ◽  
Frequency Representation ◽  
Parallel Misalignment

In this work, a novel health indicator is developed for the identification of rotor defects. The indicator is developed by extracting features from vibration data acquired from horizontal and vertical directions of rotors. A total of 38 features were initially extracted from time-domain signal, frequency-domain signal, and time–frequency representation. Out of many features, six most important features were selected using filter-based feature selection process. Thereafter, important features were fused together using manifold learning to develop health indicator. The developed indicator is used to identify misalignments (angular misalignment and parallel misalignment), rub, and unbalance. The major benefit of the proposed method is that it not only indicates the presence of defect in the rotor but also indicates the severity of defect. The experimental study presented in this article justifies that the proposed method is sensitive to the increasing levels of horizontal and angular misalignment and unbalance. The developed indicator is sensitive enough to indicate the presence of rub.

scholarly journals Time-Frequency Characterization of Rotating Instabilities in a Centrifugal Pump with a Vaned Diffuser

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
G. Pavesi ◽  
G. Cavazzini ◽  
G. Ardizzon
Keyword(s):  
Large Scale ◽  
Pressure Fluctuations ◽  
Computer Code ◽  
Design Flow ◽  
Vaned Diffuser ◽  
Time Frequency ◽  
Ansys Cfx ◽  
Frequency Domains ◽  
Radial Pump

This paper presents acoustic and flowdynamic investigations of large-scale instabilities in a radial pump with a vaned diffuser. Pressure fluctuations were measured with transducers placed flush at the inlet duct, at the impeller discharge, and in the vane diffuser walls. Two impeller rotation speeds were analyzed in the study, at design, and at off-design flow rates. A spectral analysis was carried out on the pressure signals in frequency and in time-frequency domains to identified precursors, inception, and evolution of the pressure instabilities. The results highlighted the existence of a rotating pressure structure at the impeller discharge, having a fluid-dynamical origin and propagating both in the radial direction and inside the impeller. The experimental data were then compared with the results obtained with help of ANSYS CFX computer code; focusing on the changing flow field at part load. Turbulence was reproduced by DES model.

Application of S transform and morphological pattern spectrum for gear fault diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2963-2972 ◽  
Author(s):  
B Li ◽  
P-L Zhang ◽  
Z-J Wang ◽  
S-S Mi ◽  
D-S Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Transforms ◽  
Morphological Pattern ◽  
Vibration Signals ◽  
Pattern Spectrum ◽  
Time Frequency ◽  
S Transform ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Time–frequency representations (TFR) have been intensively employed for analysing vibration signals in gear fault diagnosis. However, in many applications, TFR are simply utilized as a visual aid to detect gear defects. An attractive issue is to utilize the TFR for automatic classification of faults. A key step for this study is to extract discriminative features from TFR as input feature vector for classifiers. This article contributes to this ongoing investigation by applying morphological pattern spectrum (MPS) to characterize the TFR for gear fault diagnosis. The S transform, which combines the separate strengths of the short-time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals from gear. Then, the MPS scheme is applied to extract the discriminative features from the TFR. The promise of MPS is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experiment results demonstrate the MPS to be a satisfactory scheme for characterizing TFRs for an accurate classification of gear faults.

scholarly journals Time-frequency Analysis of Multicomponent LFM signal based on Hough and Chirplet Transform

2018 ◽  
Vol 173 ◽  
pp. 03054
Author(s):  
Xueqin Zhang ◽  
Ruolun Liu
Keyword(s):  
Fourier Transform ◽  
Line Detection ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
Chirplet Transform ◽  
Frequency Map ◽  
Linear Frequency Modulated Signal ◽  
Short Time

The Chirplet Transform (CT) is effective in the characterization of IF for mono-component linear-frequency-modulated signal. However, During the initialization process, using the peak of the time-frequency map of the short-time Fourier transform to fit the line is greatly affected by noise. For the multi-component signals, it is more difficult to distinguish and fit different IF lines. Since the Hough is good at a common algorithm for the line detection, the ridge edge fitting is replaced by the Hough transform in this paper. The experiment results show significant improvement in the obtained time-frequency representation.

Characterization of the cerebral activity by time–frequency representation of evoked EEG potentials

2011 ◽  
Vol 32 (8) ◽  
pp. 1327-1346 ◽  
Author(s):  
Francesc Clariá ◽  
Montserrat Vallverdú ◽  
Jordi Riba ◽  
Sergio Romero ◽  
Manuel J Barbanoj ◽  
...  
Keyword(s):  
Time Frequency ◽  
Cerebral Activity ◽  
Frequency Representation
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