Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis

Faysal, Atik; Ngui, Wai Keng; Lim, Meng Hee

doi:10.1007/s42417-021-00358-y

Cited by 24 publications

(13 citation statements)

References 39 publications

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“… Population initialization: The optimal parameters should be bounded, and the correlation between quality factors should be as low as possible. The value range of

takes as [ 8 , 15 ], the value range of

takes as [ 1 , 3 ], and the value range of redundancy factors

and

take as [ 2 , 5 ]. Then, to reduce the calculation, the accuracy of the four parameters is reserved to one single decimal.…”

Section: The Parallel Parameter Optimized Rssd Base On Woamentioning

confidence: 99%

“…Over the past two decades, many scholars have explored the rotating machinery fault diagnosis field and introduced many diagnostic theories and methods. For example, empirical mode decomposition (EMD) [ 3 , 4 ], wavelet transform (WT) [ 5 , 6 ], variational mode decomposition fault (VMD) [ 7 , 8 ], and so on. Although these methods and their combination perform well on single faults, there are some limitations: for example, the existence of modal mixing in EMD [ 9 , 10 ], the diagnosis effectiveness of wavelet transform, which depends on the constant quality factor, and the choice of wavelet basis [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

Feng

Zhang

Hong

et al. 2022

Sensors

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Wind turbines usually operate in harsh environments. The gearbox, the key component of the transmission chain in wind turbines, can easily be affected by multiple factors during the operation process and develop compound faults. Different types of faults can occur, coupled with each other and staggered interference. Thus, a challenge is to extract the fault characteristics from the composite fault signal to improve the reliability and the accuracy of compound fault diagnosis. To address the above problems, we propose a compound fault diagnosis method for wind turbine gearboxes based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and parallel parameter optimized resonant sparse decomposition (RSSD). Firstly, the MOMEDA is applied to the preprocess, setting the deconvolution period with different fault frequency types to eliminate the interference of the transmission path and environmental noise, while decoupling and separating the different types of single faults. Then, the RSSD method with parallel parameter optimization is applied for decomposing the preprocessed signal to obtain the low resonance components, further suppressing the interference components and enhancing the periodic fault characteristics. Finally, envelope demodulation of the enhanced signal is applied to extract the fault features and identify the different fault types. The effectiveness of the proposed method was verified using the actual data from the wind turbine gearbox. In addition, a comparison with some existing methods demonstrates the superiority of this method for decoupling composite fault characteristics.

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“… Population initialization: The optimal parameters should be bounded, and the correlation between quality factors should be as low as possible. The value range of

takes as [ 8 , 15 ], the value range of

takes as [ 1 , 3 ], and the value range of redundancy factors

and

take as [ 2 , 5 ]. Then, to reduce the calculation, the accuracy of the four parameters is reserved to one single decimal.…”

Section: The Parallel Parameter Optimized Rssd Base On Woamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

Feng

Zhang

Hong

et al. 2022

Sensors

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show abstract

“…introduced an EMD(AE-EMD) method based on flexible envelope, which has potential application significance for fault diagnosis of rolling bearings in large and complicated equipment (Hu and Li 2021). In addition, there are papers (Niu et al , 2020; Dong et al , 2009; Faysal et al , 2021; Leaman et al , 2021; Shrivastava and Singh 2018; Wang et al , 2014; Inturi et al , 2021). All these studies and methods are based on EMD to completely decompose the signals and then screen the IMFs from the decomposed signals, which is difficult to meet the requirements of real-time performance.…”

Section: Introductionmentioning

confidence: 99%

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

Wen

Sun

et al. 2023

IJICC

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PurposeThe purpose of this paper is to provide a shorter time cost, high-accuracy fault diagnosis method for water pumps. Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention. Considering the time-consuming empirical mode decomposition (EMD) method and the more efficient classification provided by the convolutional neural network (CNN) method, a novel classification method based on incomplete empirical mode decomposition (IEMD) and dual-input dual-channel convolutional neural network (DDCNN) composite data is proposed and applied to the fault diagnosis of water pumps.Design/methodology/approachThis paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient (MFCC) and a neural network model of DDCNN. First, the sound signal is decomposed by IEMD to get numerous intrinsic mode functions (IMFs) and a residual (RES). Several IMFs and one RES are then extracted by MFCC features. Ultimately, the obtained features are split into two channels (IMFs one channel; RES one channel) and input into DDCNN.FindingsThe Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII dataset) is used to verify the practicability of the method. Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis. Compared with EMD, 51.52% of data preprocessing time, 67.25% of network training time and 63.7% of test time are saved and also improve accuracy.Research limitations/implicationsThis method can achieve higher accuracy in fault diagnosis with a shorter time cost. Therefore, the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.Originality/valueThis method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

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“…For the characteristics of gearbox fault signals, the commonly used methods for gearbox fault diagnosis mainly include time-frequency analysis methods, [4][5][6] feature extraction classification, [7][8][9] and neural network classification. [10][11][12] For example, Faysal et al 13 proposed an improved method named noise eliminated ensemble empirical modal decomposition (NEEEMD) method that further reduces the white noise in the eigenfunction and keeps the system optimal overall, which can identify more fault feature pulses from the envelope spectrum and can be used as a more accurate rotor bearing fault diagnosis system. 13 Guo et al 14 proposed a method based on optimized wavelet packet denoising (WPD) and modulated signal bispectrum (MSB) fault diagnosis scheme, which utilizes the transient pulse enhancement of WPD and the demodulation capability of MSB to diagnose bearing faults more accurately and has a better effect on suppressing strong background noise and interference components.…”

Section: Introductionmentioning

confidence: 99%

Research on a novel fault diagnosis method for gearbox based on matrix distance feature

Li,

Dong,

Zhang

et al. 2023

Measurement and Control

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Aiming at the problem of fault diagnosis and classification of rolling bearing and gear of gearboxes, a novel method based on matrix distance features of Gramian angular field (GAF) image is proposed based on sliding window compressible GAF transformation. The method converts the one-dimensional fault signal into a two-dimensional feature matrix and constructs the discrimination matrix of each fault category by establishing the mean value of the feature matrix of a priori samples. For the new sampled signal, after converting it into a two-dimensional feature matrix, the feature matrix is obtained. The fault classification is carried out by using the matrix distance between feature matrix and the discrimination matrix of each category. The method is validated by the test data of Case Western Reserve University and the acoustic emission data from a gearbox test bench. The classification accuracy is 99.17% and 95.71%, which presented the feasibility and effectiveness of the novel method proposed in this paper.

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Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis

Cited by 24 publications

References 39 publications

Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

Compound Fault Diagnosis of a Wind Turbine Gearbox Based on MOMEDA and Parallel Parameter Optimized Resonant Sparse Decomposition

Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

Research on a novel fault diagnosis method for gearbox based on matrix distance feature

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