Fast Spectral Correlation Based on Sparse Representation Self-Learning Dictionary and Its Application in Fault Diagnosis of Rotating Machinery

Rolling element bearing and gear are the typical supporting or rotating parts in mechanical equipment, and it has important economy and security to realize their quick and accurate fault detection. As one kind of powerful cyclostationarity signal analyzing method, spectral correlation (SC) could identify the impulsive characteristic component buried in the vibration signals of rotating machinery effectively. However, the fault feature such as impulsive characteristic component is often interfered by other background noise, and the situation is serious especially in early weak fault stage. Besides, the traditional SC method has a drawback of low computation efficiency which hinders its wide application to some extent. To address the above problems, an impulsive feature-enhanced method which combines fast spectral correlation (FSC) with sparse representation self-learning dictionary is proposed in the paper. Firstly, the sparse representation self-learning dictionary method-K-means singular value decomposition (KSVD) is improved and the improved KSVD (IKSVD) method is used to denoise the original signal, and the periodic impulses are highlighted. Then, the FSC algorithm is applied on the denoised signal and spectral correlation image could be obtained. Finally, the calculated enhanced envelope spectrum (EES) of the denoised signal is obtained by using the spectral correlation image to identify the accurate fault position. The feasibility and superiority of the proposed method is verified through simulation, experiment, and engineering application.

Different Medical Image Registration Techniques: A Comparative Analysis

Background: Image registration provides major role in real world applications and classic digital image processing. Image registration is carried out for more than one image and this image was captured from a different location, different sensors, different time and different viewpoints. Discussion: This paper deals with the comparative analysis of various registration techniques and here six registration techniques depending upon intensity, phase correlation, image feature, area, control points and mutual information are compared. Comparative analysis for different methodologies shows the advantages of one method over the other methods. The foremost objective of this paper is to deliver a complete reference source for the scholars interested in registration, irrespective of specific application extents. Conclusion: Finally performance analyses are evaluated for the medical datasets and comparison is graphically shown with the MATLAB simulation tool.

3D Gravity Inversion with Correlation Image in Space Domain and Application to the Northern Sinai Peninsula

We present a 3D inversion method to recover density distribution from gravity data in space domain. Our method firstly employs 3D correlation image of the vertical gradient of gravity data as a starting model to generate a higher resolution image for inversion. The 3D density distribution is then obtained by inverting the correlation image of gravity data to fit the observed data based on classical inversion method of the steepest descent method. We also perform the effective equivalent storage and subdomain techniques in the starting model calculation, the forward modeling and the inversion procedures, which allow fast computation in space domain with reducing memory consumption but maintaining accuracy. The efficiency and stability of our method is demonstrated on two sets of synthetic data and one set of the Northern Sinai Peninsula gravity data. The inverted 3D density distributions show that high density bodies beneath Risan Aniza and low density bodies exist to the southeast of Risan Aniza at depths between 1~10 and 20 km, which may be originated from hot anomalies in the lower crust. The results show that our inversion method is useful for 3D quantitative interpretation.

Test-object recognition in thermal images

The paper presents a comparative analysis of several methods for recognition of test-object position in a thermal image when setting and testing characteristics of thermal image channels in an automated mode. We consider methods of image recognition based on the correlation image comparison, Viola-Jones method, LeNet classificatory convolutional neural network, GoogleNet (Inception v.1) classificatory convolutional neural network, and a deep-learning-based convolutional neural network of Single-Shot Multibox Detector (SSD) VGG16 type. The best performance is reached via using the deep-learning-based convolutional neural network of the VGG16-type. The main advantages of this method include robustness to variations in the test object size; high values of accuracy and recall parameters; and doing without additional methods for RoI (region of interest) localization.