Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

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Gear Fault Diagnosis Based on Empirical Mode Decomposition and 1.5 Dimension Spectrum

Shock and Vibration ◽

10.1155/2016/5915762 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Jianhua Cai ◽

Xiaoqin Li

Keyword(s):

Fault Diagnosis ◽

Power Spectrum ◽

Correlation Coefficient ◽

Empirical Mode Decomposition ◽

Vibration Signal ◽

Intrinsic Mode Function ◽

Dimension Spectrum ◽

Mode Function ◽

Mode Decomposition ◽

Gear Fault

Aiming at the nonlinear and nonstationary feature of mechanical fault vibration signal, a new fault diagnosis method, which is based on a combination of empirical mode decomposition (EMD) and 1.5 dimension spectrum, is proposed. Firstly, the vibration signal is decomposed by EMD and the correlation coefficient between each intrinsic mode function and original signal is calculated. Then these intrinsic mode function components, which have a big correlation coefficient, are selected to estimate its 1.5 dimension spectrum. And this method uses 1.5 dimension spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums are summed to obtain the primary power spectrum of gear fault signal. Finally, the information feature of fault is extracted from the reconstructed 1.5 dimension spectrum. A model to reconstruct 1.5 dimension spectrum is established, and the principle and steps of the method are presented. Some simulated and measured gear fault signals have been processed to demonstrate the effectiveness of new method. The result shows that this method can greatly inhibit the interference of Gauss noise to raise the SNR and recognize the secondary phase coupling feature of the signal. The proposed method has a good real-time performance and provides an effective method to determine the early crack fault of gear root.

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Gearbox Fault Diagnosis Based on Empirical Mode Decomposition and Hilbert Transform

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.542-543.238 ◽

2012 ◽

Vol 542-543 ◽

pp. 238-241

Author(s):

Yan Li Liu ◽

De Xiang Zhang ◽

Ming Wei Ji

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Hilbert Transform ◽

Intrinsic Mode Function ◽

Hilbert Spectrum ◽

Time Frequency ◽

Mode Function ◽

Mode Decomposition ◽

Wide Range ◽

Vibration Signal Analysis

Gearbox is vital components in a wide range of industrial and transport applications. It is very important how to monitor operating state of automobile gearbox and detect incipient faults. This paper applies the empirical mode decomposition (EMD) and Hilbert spectrum 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 Hilbert spectrum of intrinsic mode function at different fault characteristic frequencies is obtained by Hilbert transform. Finally, the time-frequency fault characteristics of gearbox are analyzed by the Hilbert spectrum value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and Hilbert spectrum characteristic method in fault diagnosis and fault message abstraction.

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