Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions

2019 ◽  
Vol 116 ◽  
pp. 462-479 ◽  
Author(s):  
Fuhao Li ◽  
Rong Li ◽  
Lili Tian ◽  
Lin Chen ◽  
Jian Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Frequency Analysis ◽  
Data Driven ◽  
Variational Mode Decomposition ◽  
Analysis Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis

scholarly journals Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Shangkun Liu ◽  
Guiji Tang ◽  
Xiaolong Wang ◽  
Yuling He
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Energy Operator ◽  
Turbine Rotor ◽  
Variational Mode Decomposition ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Teager Energy

A time-frequency analysis method based on improved variational mode decomposition and Teager energy operator (IVMD-TEO) is proposed for fault diagnosis of turbine rotor. Variational mode decomposition (VMD) can decompose a multicomponent signal into a number of band-limited monocomponent signals and can effectively avoid model mixing. To determine the number of monocomponents adaptively, VMD is improved using the correlation coefficient criterion. Firstly, IVMD algorithm is used to decompose a multicomponent signal into a number of monocompositions adaptively. Second, all the monocomponent signals’ instantaneous amplitude and instantaneous frequency are demodulated via TEO, respectively, because TEO has fast and high precision demodulation advantages to monocomponent signal. Finally, the time-frequency representation of original signal is exhibited by superposing the time-frequency representations of all the monocomponents. The analysis results of simulation signal and experimental turbine rotor in rising speed condition demonstrate that the proposed method has perfect multicomponent signal decomposition capacity and good time-frequency expression. It is a promising time-frequency analysis method for rotor fault diagnosis.

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.

Envelope demodulation based on variational mode decomposition for gear fault diagnosis

2016 ◽  
Vol 231 (4) ◽  
pp. 864-870 ◽  
Author(s):  
Xueli An ◽  
Hongtao Zeng ◽  
Chaoshun Li
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Decomposition Method ◽  
Modulation Frequency ◽  
Vibration Signal ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Component Amplitude

A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. According to the envelope spectrum, gear faults can be diagnosed. In essence, the variational mode decomposition method can decompose a multi-component signal into a number of single component amplitude modulation–frequency modulation signals. The method is suited to the handling of multi-component amplitude modulation–frequency modulation signals. The simulated signal and the actual gear fault vibration signals were analyzed. The results showed that the method can be effectively applied to gear fault diagnosis.

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 Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

2020 ◽  
Vol 10 (5) ◽  
pp. 1696 ◽  
Author(s):  
Xiaoan Yan ◽  
Ying Liu ◽  
Wan Zhang ◽  
Minping Jia ◽  
Xianbo Wang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Simulated Data ◽  
Energy Operator ◽  
Analysis Tool ◽  
Variational Mode Decomposition ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Compound Signal

Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).

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