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

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Na Lu ◽  
Guangtao Zhang ◽  
Zhihuai Xiao ◽  
Om Parkash Malik
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Principal Component ◽  
Rotating Machinery ◽  
High Dimensional ◽  
Lower Sensitivity ◽  
Method Performance ◽  
Feature Extraction Method

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. Then, 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. The feature extraction results by these methods are then fed into K-medoids classifier to discriminate the faults. The results show that the proposed method can improve the sensitivity of the extracted features and obtain higher fault recognition rate.

scholarly journals KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Xiao Hu ◽  
Zhihuai Xiao ◽  
Dong Liu ◽  
Yongjun Tang ◽  
O. P. Malik ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Extraction Method ◽  
Principal Component ◽  
Rotating Machinery ◽  
Kernel Principal Component Analysis ◽  
Global Feature ◽  
Vibration Signals ◽  
Feature Extraction Method

Feature extraction plays a key role in fault diagnosis of rotating machinery. Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model. To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals. First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum. Next, the spectrum is divided into several segments. Then, local-global feature extraction is performed by applying KPCA to these segments. Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature. The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery. A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method. Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed. By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

SAR Image Recognition Combined Bidirectional 2DPCA with PCA

2013 ◽  
Vol 756-759 ◽  
pp. 4041-4044 ◽  
Author(s):  
De Gong Wang ◽  
Zuo Long Yang ◽  
Shuo Chang
Keyword(s):  
Experimental Data ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Image Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Column Vector ◽  
Experimental Result ◽  
High Dimensional ◽  
Sar Image

The method of Principal Component Analysis (PCA) needs to convert image matrix to high-dimensional column vector used in feature extraction. The 2-dimensional PCA (2DPCA) offsets disadvantages of PCA. However, 2DPCA compresses image along the rows or columns only, the number of features is still large. In order to solve the above problems, bidirectional 2DPCA was used to compress image matrix along row and column meanwhile, then use PCA reduce the number of computations and feature dimensions. Three kinds of ground static military targets images acquired by SAR were used as the experimental data. The experimental result shows that, the method of SAR image recognition presented by this paper reduced the dimensions of feature matrix and raised the recognition rate.

An image-based feature extraction method for fault diagnosis of variable-speed rotating machinery

2022 ◽  
Vol 167 ◽  
pp. 108524
Author(s):  
Jungho Park ◽  
Yunhan Kim ◽  
Kyumin Na ◽  
Byeng D. Youn ◽  
Yuejian Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Variable Speed ◽  
Feature Extraction Method

scholarly journals Multiple-Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Min Zhang ◽  
Zhenyu Cai ◽  
Wenming Cheng
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Principal Component ◽  
Rolling Bearing ◽  
Total Sample ◽  
Parameters Optimization ◽  
Support Vector ◽  
Significant Information ◽  
Multiple Faults

Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE) and multiclass support vector machine (MSVM) with particle parameter adaptive (PPA) is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1) the proposed intelligent method (MFE_PPA_MSVM) improves the classification recognition rate; (2) the accuracy will decline when the number of fault patterns increases; (3) prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

scholarly journals Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

2019 ◽  
Vol 9 (24) ◽  
pp. 5343 ◽  
Author(s):  
Yutong Wu ◽  
Xinhui Hu ◽  
Ziwei Wang ◽  
Jian Wen ◽  
Jiangming Kan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Extraction Methods ◽  
Optimal Size ◽  
Semg Signal ◽  
Scientific Database ◽  
Feature Extraction Method ◽  
Gesture Control

It is necessary to complete the two parts of gesture recognition and wireless remote control to realize the gesture control of the automatic pruning machine. To realize gesture recognition, in this paper, we have carried out the research of gesture recognition technology based on surface electromyography signal, and discussed the influence of different numbers and different gesture combinations on the optimal size. We have calculated the 630-dimensional eigenvector from the benchmark scientific database of sEMG signals and extracted the features using principal component analysis (PCA). Discriminant analysis (DA) has been used to compare the processing effects of each feature extraction method. The experimental results have shown that the recognition rate of four gestures can reach 100.0%, the recognition rate of six gestures can reach 98.29%, and the optimal size is 516~523 dimensions. This study lays a foundation for the follow-up work of the pruning machine gesture control, and p rovides a compelling new way to promote the creative and human computer interaction process of forestry machinery.

Fault Diagnosis of Rotating Machinery Base on Wavelet Packet Energy Moment and HMM

2010 ◽  
Vol 455 ◽  
pp. 558-564 ◽  
Author(s):  
Chun Liang Zhang ◽  
Xia Yue ◽  
Sheng Li ◽  
Jian Li
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Success Rates ◽  
Feature Extraction Method ◽  
Multi Scale ◽  
On Line ◽  
Moment Features ◽  
Frequency Band Energy

A feature extraction method is presented for the fault diagnosis of large rotating machinery to improve the performance of on-line monitoring. According to the characteristics of fault vibration signals, wavelet packet decomposition, a well-known tool to multi-scale analysis, is applied to extracting frequency band energy features; and Hidden Markova Model (HMM) is used to classify. The final feature array is composed of time-domain, amplitude-domain features and wavelet packet energy moment features which reflect the inherent energy distribution characteristics of different faults. The continuous density hidden Markov model (CDHMM) is adopted to recognize the state in on-line monitoring, and the diagnosis success rates are more than 91% to six typical faults on different rotation speeds. The experimental results show the fault diagnosis system is valid and robust, particularly the method of feature extraction.

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