Multiple-domain manifold for feature extraction in machinery fault diagnosis

Gan, Meng; Wang, Cong; Zhu, Changan

doi:10.1016/j.measurement.2015.07.042

Cited by 40 publications

(18 citation statements)

References 39 publications

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“…ese methods are easy to be implemented. However, as linear methods, they fail to capture nonlinear characteristic of the dataset and thus are likely to exhibit poor performance for nonlinear feature fusion [23][24][25][26]. In recent years, many manifold learning techniques have been investigated, such as locally linear embedding (LLE) [27], isometric feature mapping (IsoMap) [28], local tangent space alignment (LTSA) [29], etc.…”

Section: Introductionmentioning

confidence: 99%

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Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

Lü

Zhang²,

Zhang

et al. 2019

Shock and Vibration

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Feature extraction is a key procedure in the fault diagnosis of rotating machinery. To obtain fault features with lower dimensionality and higher sensitivity, a feature extraction method based on adaptive multiwavelets transform (AMWT) and local tangent space alignment (LTSA) is proposed in this paper. AMWT is first used to obtain multiple features from the vibration signals of the machine under test to form a high-dimensional feature set. en, in order to avoid the adverse effect of the irrelevant features in this high-dimensional feature set on the fault diagnosis result, a detection index (DI) is investigated to evaluate the sensitivity of the features and those with lower sensitivity are removed. After that, LTSA is applied for feature fusion to reduce the redundant features in the high-dimensional feature set. To validate the proposed method, performance of four feature extraction schemes based on (i) wavelet and LTSA, (ii) Geronimo, Hardin, and Massopust (GHM) multiwavelets and LTSA, (iii) AMWT and principal component analysis (PCA), and (iv) AMWT and multidimensional scaling (MDS) is compared with the proposed method. e feature extraction results by these methods are then fed into K-medoids classifier to discriminate the faults. e results show that the proposed method can improve the sensitivity of the extracted features and obtain higher fault recognition rate.

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Section: Introductionmentioning

confidence: 99%

“…Among them, LTSA aims to preserve local structures in small neighborhoods and discovers the intrinsic features of nonlinear manifolds [30]. It has been proven to be superior to LLE and IsoMap in obtaining intrinsic data structure [31,32] and applied in vibration signals feature extraction [23,33,34].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

Lü

Zhang²,

Zhang

et al. 2019

Shock and Vibration

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“…And the Shannon wavelet support vector machine (SWSVM) is established to recognize faults by using the mixed-domain features extracted. Gan et al [20] obtain the time domain and frequency domain characteristics of vibration signals by singular value decomposition (SVD) and utilize the multidomain manifold learning to achieve this method to realize the fault diagnosis of mechanical equipment. Shen et al [21] decompose the vibration signal into IMFs by empirical mode decomposition (EMD).…”

Section: Introductionmentioning

confidence: 99%

Research on Multidomain Fault Diagnosis of Large Wind Turbines under Complex Environment

Jia

Dang

et al. 2018

Complexity

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Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two-dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods.

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“…In recent years, the application of weak fault signal extraction in spindle bearings has made significant progress [12][13][14]. Jiang proposed an improved method to accumulate envelope spectrum of all or part of sub-band signals rather than to demodulate selected sub-band signals, which was more robust to stochastic impulse disturbance and was able to capture weak period information as compared with FIR-based or WPT-based kurtograms [15].…”

Section: Introductionmentioning

confidence: 99%

Investigation assembly state of spindle bearing based on improved maximum correlated kurtosis deconvolution and support vector machine

Zhang¹,

Li²,

Wang³

et al. 2018

J. vibroeng.

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Aiming at the skew and misalignment between the inner and outer rings of the spindle bearing during the installation process, a new method for detecting the spindle bearing assembly state is proposed combined an improved maximum correlated kurtosis deconvolution (IMCKD) and support vector machine (SVM). The vibration and impulse signals induced by bearing assembly skew or dislocation are quite weak. In this paper, the collected vibration signals were preprocessed by IMCKD algorithm with optimized parameters between the normal and skewed state of the outer rings at first, which aims to deconvolve periodic impulse characteristic and enhance the periodic impulse component from a new assembly spindle vibration signal. Then, wavelet packet decomposition method was adopted to extract the characteristic energy value of different frequency bands, and the eigenvector of the assembly state of the spindle bearing was calculated. Furthermore, the support vector machine classification method is implemented to analyze the eigenvectors under different assembly states. Finally, the proposed method was verified by experimental results. The results show that the IMCKD and SVM methods can effectively detect the bearing assembly skew state of the spindle with an accuracy of more than 95 %.

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Multiple-domain manifold for feature extraction in machinery fault diagnosis

Cited by 40 publications

References 39 publications

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

Feature Extraction Based on Adaptive Multiwavelets and LTSA for Rotating Machinery Fault Diagnosis

Research on Multidomain Fault Diagnosis of Large Wind Turbines under Complex Environment

Investigation assembly state of spindle bearing based on improved maximum correlated kurtosis deconvolution and support vector machine

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