scholarly journals Improvement of Fast Kurtogram Combined with PCA for Multiple Weak Fault Features Extraction

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1059
Author(s):  
Yongxing Song ◽  
Jingting Liu ◽  
Linhua Zhang ◽  
Dazhuan Wu
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Rotating Machinery ◽  
Features Extraction ◽  
Signal To Noise ◽  
Fault Features ◽  
Weak Fault ◽  
Noise Ratio

Demodulation plays an important role in fault feature extraction for rotating machinery. The fast kurtogram method was proved to be effective for rotating machinery demodulation. However, the demodulation effectiveness of fast kurtogram was poor for multiple fault features extraction under low signal-to-noise ratio. In this paper, an improved method of fast kurtogram, called P-kurtogram, is presented. The proposed method extracted the multiple weak fault features from multiple envelope signals-based principal component analysis. Compared with extracting features from one envelope signal of fast kurtogram, P-kurtogram showed a better demodulation performance for multiple faults. Combined with principal component analysis method, the proposed method also showed a good performance under low signal-to-noise ratio(SNR). By simulation analysis, the P-kurtogram method showed good performance for multiple modulation features extraction and robust performance in demodulation under low SNR. Then, the proposed method was demonstrated by applications of bearing faults detection and propeller detection. The results verified that the P-kurtogram has a better demodulation performance than fast kurtogram for multiple weak fault features extraction, especially under low signal-to-noise ratio. The proposed method provides a reliable basis for multiple weak fault features extraction of rotating machinery.

scholarly journals Near-field sound source localization using principal component analysis–multi-output support vector regression

2020 ◽  
Vol 16 (4) ◽  
pp. 155014772091640
Author(s):  
Lanmei Wang ◽  
Yao Wang ◽  
Guibao Wang ◽  
Jianke Jia
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Covariance Matrix ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Signal To Noise ◽  
Noise Ratio

In this article, principal component analysis method, which is applied to image compression and feature extraction, is introduced into the dimension reduction of input characteristic variable of support vector regression, and a method of joint estimation of near-field angle and range based on principal component analysis dimension reduction is proposed. Signal-to-noise ratio and calculation amount are the decisive factors affecting the performance of the algorithm. Principal component analysis is used to fuse the main characteristics of training data and discard redundant information, the signal-to-noise ratio is improved, and the calculation amount is reduced accordingly. Similarly, support vector regression is used to model the signal, and the upper triangular elements of the signal covariance matrix are usually used as input features. Since the covariance matrix has more upper triangular elements, training it as a feature input will affect the training speed to some extent. Principal component analysis is used to reduce the dimensionality of the upper triangular element of the covariance matrix of the known signal, and it is used as the input feature of the multi-output support vector regression machine to construct the near-field parameter estimation model, and the parameter estimation of unknown signal is herein obtained. Simulation results show that this method has high estimation accuracy and training speed, and has strong adaptability at low signal-to-noise ratio, and the performance is better than that of the back-propagation neural network algorithm and the two-step multiple signal classification algorithm.

Enhancing the Signal-to-Noise Ratio of X-ray Diffraction Profiles by Smoothed Principal Component Analysis

2005 ◽  
Vol 77 (20) ◽  
pp. 6563-6570 ◽  
Author(s):  
Zeng Ping Chen ◽  
Julian Morris ◽  
Elaine Martin ◽  
Robert B. Hammond ◽  
Xiaojun Lai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
X Ray Diffraction ◽  
Signal To Noise ◽  
X Ray ◽  
Noise Ratio

scholarly journals Pemampatan Citra Pas Foto dengan Menggunakan Algoritma Kompresi Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA)

2018 ◽  
Vol 17 (1) ◽  
pp. 102
Author(s):  
M. Azman Maricar ◽  
Oka Widyantara
Keyword(s):  
Principal Component Analysis ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

Penelitian ini bertujuan untuk membandingkan hasil kompresi dari algoritma Joint-Photograpic Experts Group (JPEG) dan Principal Component Analysis (PCA) terhadap citra pas foto, guna menemukan hasil terbaik dari hasil citra kompresi yang kualitas hasilnya tidak berbeda jauh dengan citra aslinya. Alat ukur yang digunakan adalah Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR). Hasil yang diperoleh dalam penelitian ini adalah rata-rata MSE dan PNSR algoritma PCA dapat dikatakan tinggi jika dibandingkan dengan algoritma JPEG. Namun dari segi kualitas citra yang dihasilkan tidak jauh berbeda dengan algoritma JPEG.Dapat dikatakan bahwa algoritma JPEG mampu menghasilkan citra yang lebih baik dibandingkan algoritma PCA. Namun, algoritma PCA tidaklah buruk untuk dijadikan alternatif dalam kompresi citra pas foto.

scholarly journals Defect detection of GFRP/Nomex honeycomb sandwich structure by linear frequency modulation infrared thermal imaging

2021 ◽  
Vol 25 (6 Part B) ◽  
pp. 4611-4619
Author(s):  
Qing-Ju Tang ◽  
Wei-Ming Fan ◽  
Juan Ji ◽  
Ya-Fei Song
Keyword(s):  
Principal Component Analysis ◽  
Sandwich Structure ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Signal To Noise ◽  
Honeycomb Sandwich ◽  
Water Accumulation ◽  
Nomex Honeycomb ◽  
Honeycomb Sandwich Structure

Honeycomb sandwich material is a new material widely used in many fields, but it is easy to produce defects such as delamination and ponding in the process of manufacturing and service. First, a honeycomb sandwich sample containing delamination defects and water accumulation was built. Then, a linear frequency modulated driving halogen lamp is used as the excitation source. Finally, the surface thermal image sequence of the test sample is acquired by infrared thermal imager. Image sequences are processed by inter-frame difference-multi-frame cumulative average method, principal component analysis, Fourier transform method, and logarithmic polynomial fitting method, respectively. Define and calculate the signal-to-noise ratio of the heat map processed by each algorithm. Compared with the other three algorithms, the principal component analysis method processed the image with the highest signal-to-noise ratio and high contrast. This algorithm achieves effective identification of delamination defects and water accumulation in GFRP/Nomex honeycomb sandwich structure.

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

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