An Improved Empirical Mode Decomposition Based on Adaptive Weighted Rational Quartic Spline for Rolling Bearing Fault Diagnosis

Ye, Xuerong; Hu, Y.; Shen, Jun‐Xian; Feng, Rui; Zhai, Guofu

doi:10.1109/access.2020.3006030

Cited by 32 publications

(16 citation statements)

References 41 publications

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“…. , N }, label(y t , W ) = label(y s , W ), (9) where label(y s , W) and label(y s , W) are the predicted health state of the short data segment y s and y t using the classifier W, respectively. It should be mentioned that the short data segments y s and y t only differ in the different starting points of the vibration data y statel under the health state l.…”

Section: ) Shift-invariance Property When Predicting Health Statesmentioning

confidence: 99%

“…Among the feature extraction methods, the classical time-domain statistics and spectral analysis techniques usually fail to detect the incipient weak faults due to the strong harmonic interferences and heavy background noises. To accurately extract and characterize the fault-related features from the complex multi-component signals of rotating machine, many advanced vibration-based signal processing algorithms have been developed, such as spectral kurtosis (SK) [7], minimum entropy deconvolution [8], empirical mode decomposition [9], stochastic resonance [10], time-frequency representation [11]- [13], wavelet transforms [14]- [16], and sparsity-based fault diagnosis algorithms [17]- [19]. However, these above extraction feature methods require a prior feature knowledge such as the fault characteristic frequency (FCF), and thus are not suitable when the explicit prior feature knowledge are not available due to the dynamic and uncertainty issues in the complex mechanical systems [20].…”

Section: Introductionmentioning

confidence: 99%

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Sparse Representation Classification With Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis

et al. 2021

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Fault diagnosis technique is the core of Prognostics and Health Management (PHM) system, which plays a crucial role in the intelligent operation and maintenance of various rotating machineries. In this paper, we present a novel sparse representation classification framework with structured dictionary design strategy (SRC-SDD) for intelligent fault diagnosis of rotating machineries. The proposed SRC-SDD method consists of two stages, i.e., the structured dictionary design stage and the sparsity-based intelligent diagnosis stage. In the first stage, the novelty of SRC-SDD lies in the overlapping segmentation strategy for structured dictionary design, which leverages the structured prior knowledge of rotating machinery vibration signals, namely, the periodic self-similarity and shift-invariance properties. In the second stage, SRC-SDD achieves fault recognitions of testing samples using a sparsity-based diagnosis strategy based on the minimum sparse reconstruction error. The proposed structured dictionary design strategy can enhance the representation power of dictionaries and thus promote the recognition performance of the sparsity-based diagnosis strategy. Finally, the effectiveness of SRC-SDD has been validated on the gearbox fault dataset from IEEE PHM society. The diagnosis results show that SRC-SDD achieves the excellent recognition accuracy of 100% for predicting six different gearbox health states. Further, the comparative studies with three conventional SRC methods prove the superiority of SRC-SDD in terms of both the recognition performance and computation efficiency.

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Section: ) Shift-invariance Property When Predicting Health Statesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Representation Classification With Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis

et al. 2021

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“…In this study, the mutation probability MR is introduced to specify whether to change the position of each particle in the specified dimension. The mutation operation formula is shown in Equation (14). If Rand(i) (i = 1, 2, .…”

Section: Escape the Local Trap Based On Mutation Operationmentioning

confidence: 99%

“…CWT solves the problems of FFT and STFT with an adjustable window size [13]. However, once the decomposition scale of CWT is defined, CWT can only decompose signals in the defined frequency band, which makes CWT non-adaptive [14]. Based on the above analysis, adaptive signal processing technology may be able to analyze vibration signals more effectively [15].…”

Section: Introductionmentioning

confidence: 99%

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

Lee

Zhuo

2021

Mathematics

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The accurate localization of the rolling element failure is very important to ensure the reliability of rotating machinery. This paper proposes an efficient and anti-noise fault diagnosis model for rolling elements. The proposed model is composed of feature extraction, feature selection and fault classification. Feature extraction is composed of signal processing and signal noise reduction. Signal processing is carried out by local mean decomposition (LMD), and signal noise reduction is performed by product function (PF) selection and wavelet packet decomposition (WPD). Through the steps of signal noise reduction, high-frequency noise can be effectively removed, and the fault information hidden under the noise can be extracted. To further improve the effectiveness of the diagnostic model, an improved binary particle swarm optimization (IBPSO) is proposed to find the most important features from the feature space. In IBPSO, cycling time-varying inertia weight is introduced to balance exploitation and exploration and improve the capability to escape from local solutions, and crossover and mutation operations are also introduced to improve exploration and exploitation capabilities, respectively. The main contributions of this research are briefly described as follows: (1) The feature extraction process applied in this research can effectively remove noise and establish a high-accuracy feature set. (2) The proposed feature selection algorithm has higher accuracy than the other state-of-the-art feature selection algorithms. (3) In a strong noise environment, the proposed rolling element fault diagnosis model is compared with the state-of-the-art fault diagnosis model in terms of classification accuracy. Experimental results show that the model can maintain high classification accuracy in a strong noise environment. Therefore, it can be proved that the fault diagnosis model proposed in this paper can be effectively applied to the fault diagnosis of rotating machinery.

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“…However, different mother wavelets, threshold selection methods, and different decomposition levels achieve different de-noising effects [31]. The EEMD method is an extension algorithm for the empirical mode decomposition (EMD) method [44][45][46][47][48][49][50][51], which has no requirement for prior knowledge of transform basis functions and overcomes the mode mixing, false mode, and endpoint problems of the EMD method by taking advantage of the uniform frequency distribution of Gaussian white noise. It also has significant advantages in dealing with nonstationary and nonlinear data.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Cutoff Frequency Selection Approach for Fast Fourier Transform Method and Its Application into Short-Term Traffic Flow Forecasting

Wang

Shi

Liu

et al. 2020

IJGI

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Historical measurements are usually used to build assimilation models in sequential data assimilation (S-DA) systems. However, they are always disturbed by local noises. Simultaneously, the accuracy of assimilation model construction and assimilation forecasting results will be affected. The fast Fourier transform (FFT) method can be used to acquire de-noised historical traffic flow measurements to reduce the influence of local noises on constructed assimilation models and improve the accuracy of assimilation results. In the practical signal de-noising applications, the FFT method is commonly used to de-noise the noisy signal with known noise frequency. However, knowing the noise frequency is difficult. Thus, a proper cutoff frequency should be chosen to separate high-frequency information caused by noises from the low-frequency part of useful signals under the unknown noise frequency. If the cutoff frequency is too high, too much noisy information will be treated as useful information. Conversely, if the cutoff frequency is too low, part of the useful information will be lost. To solve this problem, this paper proposes an adaptive cutoff frequency selection (A-CFS) method based on cross-validation. The proposed method can determine a proper cutoff frequency and ensure the quality of de-noised outputs for a given dataset using the FFT method without noise frequency information. Experimental results of real-world traffic flow data measurements in a sub-area of a highway near Birmingham, England, demonstrate the superior performance of the proposed A-CFS method in noisy information separation using the FFT method. The differences between true and predicted traffic flow values are evaluated using the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage (MAPE) values. Compared to the results of the two commonly used de-noising methods, i.e., discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) methods, the short-term traffic flow forecasting results of the proposed A-CFS method are much more reliable. In terms of the MAE value, the average relative improvements of the assimilation model built using the proposed method are 19.26%, 3.47%, and 4.25%, compared to the model built using raw data, DWT method, and EEMD method, respectively; the corresponding average relative improvements in RMSE are 19.05%, 5.36%, and 3.02%, respectively; lastly, the corresponding average relative improvements in MAPE are 18.88%, 2.83%, and 2.28%, respectively. The test results show that the proposed method is effective in separating noises from historical measurements and can improve the accuracy of assimilation model construction and assimilation forecasting results.

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An Improved Empirical Mode Decomposition Based on Adaptive Weighted Rational Quartic Spline for Rolling Bearing Fault Diagnosis

Cited by 32 publications

References 41 publications

Sparse Representation Classification With Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis

Sparse Representation Classification With Structured Dictionary Design Strategy for Rotating Machinery Fault Diagnosis

Localization of Rolling Element Faults Using Improved Binary Particle Swarm Optimization Algorithm for Feature Selection Task

An Adaptive Cutoff Frequency Selection Approach for Fast Fourier Transform Method and Its Application into Short-Term Traffic Flow Forecasting

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