Undecimated Multiwavelet and Hilbert-Huang Time-frequency Analysis and Its Application in the Incipient Fault Diagnosis of Planetary Gearboxes

2013 ◽  
Vol 49 (03) ◽  
pp. 56 ◽  
Author(s):  
Hailiang SUN
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

Current-Aided Time-Frequency Analysis of Vibration Signals for Gearbox Fault Diagnosis

2018 ◽  
Author(s):  
Xiaotong Tu ◽  
Yue Hu ◽  
Fucai Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Current Signal ◽  
Reliable Technique ◽  
Time Frequency Analysis ◽  
Rotating Speed ◽  
Planetary Gearbox ◽  
Time Frequency

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.

scholarly journals A Fast Three-Dimensional Display Method for Time-Frequency Spectrogram Used in Embedded Fault Diagnosis Devices

2018 ◽  
Vol 8 (10) ◽  
pp. 1930 ◽  
Author(s):  
Lina Wang ◽  
Chengdong Wang ◽  
Yong Chen
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Low Cost ◽  
Three Dimensional ◽  
Surface Generation ◽  
Depth Information ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mesh Surface ◽  
General Computer

Time-frequency analysis is usually used to reveal the appearance of different frequency components varying with time, in signals, of which time-frequency spectrogram is an important visual tool to display the information. The Mesh Surface Generation (MSG) algorithm is widely used in three-dimensional (3D) modeling. Removing hidden lines from the mesh plot is an essential process that produces explicit depth information. In this paper, a fast and effective method has been proposed for a time-frequency Spectrogram Mesh Surface Generation (SMSG) display, especially, based on the painter’s algorithm. In addition, most portable fault diagnosis devices have little function to generate a 3D spectrogram, which generally needs a general computer to realize the complex time-frequency analysis algorithms and a 3D display. However, general computer is not portable and then not suitable for field test. Hence, the proposed SMSG algorithm is applied to an embedded fault diagnosis device, which is light, low-cost, and real-time. The experimental results show that this approach can realize a high degree of accuracy and save considerable time.

Application of the Blind Source Separation Based on Time-Frequency Analysis in Mechanical Fault Diagnosis

2014 ◽  
Vol 945-949 ◽  
pp. 1054-1062 ◽  
Author(s):  
Zhi Nong Li ◽  
Fen Zhang ◽  
Xu Ping He ◽  
Yao Xian Xiao
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Frequency Analysis ◽  
Source Separation ◽  
Spectral Feature ◽  
Frequency Domain Analysis ◽  
Mechanical Equipment ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Non Stationary Signal

Blind source separation provides a new method for the separation of mechanical sources under high level background noise, as well as the diagnosis of the compound fault. At present, the blind source separation has been successfully applied to the mecanical fault diagnosis. But the traditional mechanical source separation methods are restricted to non-gauss, stationary and mutually independent source signals. However, the mechanical fault signals do not suffice to these conditions, and generally exhibit non-stationarity and non-independence. For the non-stationary signal, its spectral feature is time-varying. Thus only the time-domain or frequency-domain analysis is not sufficient to describe the characteristics of non-stationary signal. The time-frequency analysis, which can provide the information about that the spectrum of the signal varies with the time, is a useful tool for non-stationary signal analysis. In this paper, combined time-frequency analysis with blind source separation, a blind source separation method for the non-stationary signal of the mechanical equipment based on time-frequency analysis is proposed and studied. The simulation and experimental results show that the proposed approach is feasible and effective.

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