Improving accuracy of cavitation severity recognition in axial piston pumps by denoising time-frequency images

Vibration signal is a good indicator of cavitation in axial piston pumps. Some vibration-based machine learning methods have been developed for recognizing the pump cavitation. However, their fault diagnostic performance is often unsatisfactory in industrial applications due to the sensitivity of the vibration signal to noise. In this paper, we presented an intelligent method to recognize the cavitation severity of an axial piston pump under noisy environment. First, we adopted short-time Fourier transformation to convert the raw vibration data into spectrograms that acted as input images of a modified LeNet-5 convolutional neural network (CNN). Second, we proposed a denoising method for the converted spectrograms based on frequency spectrum characteristics. Finally, we verified the proposed method on the dataset from a test rig of high-speed axial piston pump. The experimental results indicate that the denoising method significantly improves the diagnostic performance of the CNN model under noisy environment. For example, the accuracy rate of the cavitation recognition increases from 0.52 to 0.92 at SNR of 4 dB by the denoising method.

A New Biomedical Image Denoising Method Using an Adaptive Multi-resolution Technique

In the world of digital image processing, image denoising plays a vital role, where the primary objective was to distinguish between a clean and a noisy image. However, it was not a simple task. As a consequence of everyone's understanding of the practical challenge, a variety of methods have been presented during the last few years. Of those, wavelet transformer-based approaches were the most common. But wavelet-based methods have their own limitations in image processing applications like shift sensitivity, poor directionality, and lack of phase information, and they also face difficulties in defining the threshold parameters. As a result, this study provides an image de-noising approach based on Bi-dimensional Empirical Mode Decomposition (BEMD). This project's main purpose is to disintegrate noisy images based on their frequency and construct a hybrid algorithm that uses existing de-noising techniques. This approach decomposes the noisy picture into numerous IMFs with residue, which were subsequently filtered independently based on their specific properties. To quantify the success of the proposed technique, a comprehensive analysis of the experimental results of the benchmark test images was conducted using several performance measurement matrices. The reconstructed image was found to be more accurate and pleasant to the eye, outperforming state-of-the-art denoising approaches in terms of PSNR, MSE, and SSIM.

Research on the Denoising Method of Intelligent Ultrasonic Fuel Consumption Testing in Automobiles

Using ultrasonic time difference method to test automobile fuel consumption, the test accuracy mainly depends on the testing system timing accuracy and ultrasonic flow sensor output signal-to-noise ratio. At present, the timing accuracy of the single-chip can reach the level of picosecond, and the noise mixed in the output signal of the ultrasonic converter is the main factor affecting the accuracy of fuel consumption testing. When the receiving signal contains noise, it will cause the signal amplitude to fluctuate, making the measurement time error. The analysis of same-frequency noise, circuit noise and colored noise is carried out, and the feasible measures to eliminate noise are put forward to provide reference for accurate calculation of sound and development of high-precision automobile fuel consumption test instruments.