scholarly journals Fault diagnosis of gearbox based on improved polynomial adaptive chirp mode decomposition algorithm

2021 ◽  
Author(s):  
Lingli Cui ◽  
Yuchuan Peng ◽  
Tongtong LIU
Keyword(s):  
Fault Diagnosis ◽  
Instantaneous Frequency ◽  
Variable Speed ◽  
Vibration Signals ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Representation ◽  
Study Results ◽  
Gearbox Vibration

Abstract The adaptive chirp mode decomposition (ACMD) has good time-frequency representation results in analyzing chirp signals, while there is a time-frequency ambiguity problem in the analysis of variable speed planetary gearbox vibration signals. To address this problem, a planetary gearbox fault diagnosis method based on improved polynomial adaptive chirp mode decomposition wavelet is proposed (IPACMD). Using Adaptive chirp mode decomposition, the amplitude and instantaneous frequency of multiple signal components are estimated; To avoid over-decomposition to generate spurious signal components, the similarity conditional entropy is used to optimize the adaptive chirp mode decomposition threshold ;The polynomial chirp transform (PCT) using a polynomial function instead of the linear chirp kernel in the chirp transform to improve the time-frequency aggregation of the instantaneous frequency curve of each signal component and output high-resolution time-frequency representation results. Compared with the original method, the proposed method has better time-frequency aggregation and is more effective for the analysis of variable speed planetary gearbox vibration signals. The simulation and experimental study results show that the method can effectively identify the frequency components and time-frequency characteristics of the variable-speed planetary gearbox vibration signal and realize the fault diagnosis of the planetary gearbox.

scholarly journals NOISE CORRUPTION OF EMPIRICAL MODE DECOMPOSITION AND ITS EFFECT ON INSTANTANEOUS FREQUENCY

2010 ◽  
Vol 02 (03) ◽  
pp. 373-396 ◽  
Author(s):  
DANIEL N. KASLOVSKY ◽  
FRANÇOIS G. MEYER
Keyword(s):  
Empirical Mode Decomposition ◽  
Instantaneous Frequency ◽  
Poor Performance ◽  
Specific Mechanism ◽  
Time Frequency ◽  
Seismic Waveform ◽  
Mode Decomposition ◽  
Frequency Representation ◽  
Transition Modes ◽  
Nonstationary Data

Huang's Empirical Mode Decomposition (EMD) is an algorithm for analyzing nonstationary data that provides a localized time-frequency representation by decomposing the data into adaptively defined modes. EMD can be used to estimate a signal's instantaneous frequency (IF) but suffers from poor performance in the presence of noise. To produce a meaningful IF, each mode of the decomposition must be nearly monochromatic, a condition that is not guaranteed by the algorithm and fails to be met when the signal is corrupted by noise. In this work, the extraction of modes containing both signal and noise is identified as the cause of poor IF estimation. The specific mechanism by which such "transition" modes are extracted is detailed and builds on the observation of Flandrin and Goncalves that EMD acts in a filter bank manner when analyzing pure noise. The mechanism is shown to be dependent on spectral leak between modes and the phase of the underlying signal. These ideas are developed through the use of simple signals and are tested on a synthetic seismic waveform.

Gearbox Fault Diagnosis Based on EMD and Coefficient-Energy Value

2012 ◽  
Vol 542-543 ◽  
pp. 234-237
Author(s):  
Ping Wang ◽  
De Xiang Zhang ◽  
Yan Li Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Energy Value ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
Vibration Signal Analysis ◽  
Fault Information

This paper applies the empirical mode decomposition (EMD) methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then the fault information diagnosis of the gearbox vibration signals can be extracted from the coefficient-energy value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and energy characteristic method in fault diagnosis and fault message abstraction. It is significant for the monitor operating state of gearbox and detects incipient faults as soon as possible.

Fault diagnosis of planetary gearbox under variable-speed conditions using an improved adaptive chirp mode decomposition

2020 ◽  
Vol 468 ◽  
pp. 115065 ◽  
Author(s):  
Shiqian Chen ◽  
Minggang Du ◽  
Zhike Peng ◽  
Zhipeng Feng ◽  
Wenming Zhang
Keyword(s):  
Fault Diagnosis ◽  
Variable Speed ◽  
Planetary Gearbox ◽  
Mode Decomposition

scholarly journals Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Sheraz Ali Khan ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Operating Speed ◽  
Shaft Speed ◽  
Variable Speed ◽  
Vibration Acceleration ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Acceleration Signals

Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal. These defect frequencies depend upon the inherently nonstationary shaft speed. Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed, whereas design variations require retraining a new instance of the classifier for each operating speed. This paper presents an automated approach for fault diagnosis in bearings based upon the 2D analysis of vibration acceleration signals under variable speed conditions. Images created from the vibration signals exhibit unique textures for each fault, which show minimal variation with shaft speed. Microtexture analysis of these images is used to generate distinctive fault signatures for each fault type, which can be used to detect those faults at different speeds. A k-nearest neighbor classifier trained using fault signatures generated for one operating speed is used to detect faults at all the other operating speeds. The proposed approach is tested on the bearing fault dataset of Case Western Reserve University, and the results are compared with those of a spectrum imaging-based approach.

scholarly journals Application of Reassigned Wavelet Scalogram in Wind Turbine Planetary Gearbox Fault Diagnosis under Nonstationary Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowang Chen ◽  
Zhipeng Feng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Frequency Resolution ◽  
Vibration Signals ◽  
Cross Term ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Gear Fault ◽  
Nonstationary Conditions

Wind turbine planetary gearboxes often run under nonstationary conditions due to volatile wind conditions, thus resulting in nonstationary vibration signals. Time-frequency analysis gives insight into the structure of an arbitrary nonstationary signal in joint time-frequency domain, but conventional time-frequency representations suffer from either time-frequency smearing or cross-term interferences. Reassigned wavelet scalogram has merits of fine time-frequency resolution and cross-term free nature but has very limited applications in machinery fault diagnosis. In this paper, we use reassigned wavelet scalogram to extract fault feature from wind turbine planetary gearbox vibration signals. Both experimental and in situ vibration signals are used to evaluate the effectiveness of reassigned wavelet scalogram in fault diagnosis of wind turbine planetary gearbox. For experimental evaluation, the gear characteristic instantaneous frequency curves on time-frequency plane are clearly pinpointed in both local and distributed sun gear fault cases. For in situ evaluation, the periodical impulses due to planet gear fault are also clearly identified. The results verify the feasibility and effectiveness of reassigned wavelet scalogram in planetary gearbox fault diagnosis under nonstationary conditions.

scholarly journals Multi-Scale Demodulation for Fault Diagnosis Based on a Weighted-EMD De-Noising Technique and Time–Frequency Envelope Analysis

2020 ◽  
Vol 10 (21) ◽  
pp. 7796 ◽  
Author(s):  
Wei-tao Du ◽  
Qiang Zeng ◽  
Yi-min Shao ◽  
Li-ming Wang ◽  
Xiao-xi Ding
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Fast Fourier Transformation ◽  
Intrinsic Mode Functions ◽  
Envelope Analysis ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Envelope Signal

Demodulation is one of the most useful techniques for the fault diagnosis of rotating machinery. The commonly used demodulation methods try to select one sensitive sub-band signal that contains the most fault-related components for further analysis. However, a large number of the fault-related components that exist in other sub-bands are ignored in the commonly used envelope demodulation methods. Based on a weighted-empirical mode decomposition (EMD) de-noising technique and time–frequency (TF) impulse envelope analysis, a multi-scale demodulation method is proposed for fault diagnosis. In the proposed method, EMD is first employed to divide the signal into some IMFs (intrinsic mode functions). Then, a new weighted-EMD de-noising technique is presented, and different weights are assigned to IMFs for construction according to their fault-related degrees; thus, the fault-unrelated components are suppressed to improve the signal-to-noise ratio (SNR). After that, continuous wavelet transformation (CWT) is adopted to obtain the time–frequency representation (TFR) of the de-noised signal. Subsequently, the fault-related components in the entire frequency range scale are calculated together, referring to the TF impulse envelope signal. Finally, a fault diagnosis result can be obtained after the fast Fourier transformation of the TF impulse envelope signal. The proposed method and three commonly used methods are applied to the fault diagnosis of a planetary gearbox with a sun gear spalling fault and a fixed shaft gearbox with a crack fault. The results show that the proposed method can effectively detect gear faults and yields better performance than other methods.

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