Classification of time–frequency representations based on two-direction 2DLDA for gear fault diagnosis

2011 ◽  
Vol 11 (8) ◽  
pp. 5299-5305 ◽  
Author(s):  
Bing Li ◽  
Pei-lin Zhang ◽  
Dong-sheng Liu ◽  
Shuang-shan Mi ◽  
Peng-yuan Liu
Keyword(s):  
Fault Diagnosis ◽  
Time Frequency ◽  
Gear Fault ◽  
Gear Fault Diagnosis

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.

Gear fault diagnosis based on time–frequency domain de-noising using the generalized S transform

2017 ◽  
Vol 24 (15) ◽  
pp. 3338-3347 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Time Frequency ◽  
Transmission Modes ◽  
S Transform ◽  
Mining Machinery ◽  
Gear Fault ◽  
Generalized S Transform ◽  
Gear Fault Diagnosis

Gears are the most important transmission modes used in mining machinery, and gear faults can cause serious damage and even accidents. In the work process, vibration signals are influenced not only by friction, nonlinear stiffness, and nonstationary loads, but also by strong noise. It is difficult to separate the useful information from the noise, which brings some trouble to the fault diagnosis of mining machinery gears. The generalized S transform has the advantages of the short time Fourier transform and wavelet transform and is reversible. The time–frequency energy distribution of the gear vibration signal can be accurately presented by the generalized S transform, and a time–frequency filter factor can be constructed to filter the vibration signal in the time–frequency domain. These characteristics play an important role when the generalized S transform is used to remove the noise in the time–frequency domain. In this paper, a new gear fault diagnosis based on the time–frequency domain de-noising is proposed that uses the generalized S transform. The application principle, method steps, and evaluation index of the method are presented, and a wavelet soft-threshold filtering method is implemented for comparison with the proposed approach. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a gear with a tooth crack. Our analyses also indicate that the proposed method can be used for fault diagnosis of mining machinery gears.

Gear Fault Diagnosis Using Synchro-Squeezing Transform Based Feature Analysis

2013 ◽  
Vol 569-570 ◽  
pp. 449-456 ◽  
Author(s):  
Budhaditya Hazra ◽  
Sriram Narasimhan
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Mathematical Structure ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
The Rich ◽  
The Individual ◽  
Non Stationary Signal

Synchro-squeezing transform has recently emerged as a powerful signal processing tool in non-stationary signal processing. Premised upon the concept of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane and extract the individual components of a non-stationary multi-component signal, akin to empirical mode decomposition (EMD). The rich mathematical structure based on continuous wavelet transform (CWT) makes synchro-squeezing powerful for gear fault diagnosis, as faulty gear signal is frequently constituted out of multiple amplitude-modulated and frequency-modulated signals embedded in noise. This work utilizes the decomposing power of synchro-squeezing transform to extract the IMFs from a gear signal followed by the application of standard gearbox condition indicators which promises greater prognostic power than that can be achieved by applying condition indictors directly to the inherently complex gear signals. The efficacy and the robustness of the algorithm are demonstrated with the aid of practical experimental data obtained from a helicopter gear box.

scholarly journals Gear fault diagnosis using the general linear chirplet transform with vibration and acoustic measurements

2018 ◽  
Vol 38 (1) ◽  
pp. 36-52 ◽  
Author(s):  
Dennis Hartono ◽  
Dunant Halim ◽  
Gethin W Roberts
Keyword(s):  
Fault Diagnosis ◽  
Accurate Determination ◽  
Diagnostic Methods ◽  
General Linear ◽  
Acoustic Measurements ◽  
Transform Method ◽  
Time Frequency ◽  
Chirplet Transform ◽  
Gear Fault ◽  
Gear Fault Diagnosis

This work is aimed to develop a parameterized time–frequency analysis method combined with vibration and acoustic measurements for gear fault diagnosis. To achieve this aim, the work introduces the combined use of the residual method and general linear chirplet transform using acoustic and vibration measurements from a single stage spur gearbox. Experimental works were undertaken on a developed gearbox test rig. It was found from experiments that despite acoustic measurements were heavily corrupted by measurement noise, the use of the combined general linear chirplet transform method provided more accurate fault severity assessment compared to other commonly used diagnostic methods: continuous wavelet transform and pseudo Wigner–Ville distribution methods. The combined general linear chirplet transform method allows an accurate determination of the angular location of gear fault and a better representation of sidebands associated with the severity level of gear fault. The results demonstrate the potential of using non-contact acoustic measurement using the combined general linear chirplet transform method as an alternative sensing method for gear condition monitoring applications.

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.

scholarly journals Time-Frequency Enhancement Technique for Bevel Gear Fault Diagnosis

2016 ◽  
Vol 70 ◽  
pp. 02003
Author(s):  
Dennis Hartono ◽  
Dunant Halim ◽  
Achmad Widodo ◽  
Gethin Wyn Roberts
Keyword(s):  
Fault Diagnosis ◽  
Bevel Gear ◽  
Enhancement Technique ◽  
Time Frequency ◽  
Gear Fault ◽  
Gear Fault Diagnosis
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