The inductive debris sensor has been studied because of its wide application prospects in mechanical health monitoring. In order to ensure a high-precision detection performance, a comprehensive method to improve the detection sensitivity and detection ability of the inductive sensor for non-ferromagnetic metal debris is proposed. Based on the characteristics of the eddy current inside the metal, the change of the coil impedance caused by the metal debris is increased by enhancing the magnetic field strength and selecting the optimal excitation frequency. The impedance detection method involving inductance and resistance parameters is used to improve the detection limit of non-ferromagnetic metal debris. The experimental results verify that the magnetic field in the detection region can be enhanced by adding a silicon steel strip (paramagnetic material) in the central hole of the coil, thereby greatly improving the detection sensitivity of the inductive sensor, and the concentrated distribution of the magnetic field avoids the double-peak signals generated by a single particle. The characteristics of the signal amplitude of non-ferromagnetic debris with excitation frequency are studied. Higher inductance, resistance amplitudes, and signal-to-noise ratio (SNR) can be obtained by using a high-frequency alternating current. Compared with inductance parameter detection, resistance parameter detection can detect smaller non-ferromagnetic debris. Combining the detection results of the inductance and resistance parameters can effectively improve the sensor's ability to detect non-ferromagnetic debris.

Improving the Detection Ability of Inductive Micro-Sensor for Non-Ferromagnetic Wear Debris.

The progress in this wear particle detection is expounded initially in the paper. And the merits and demerits of the detection techniques currently are reviewed on the basis of the detailed analysis on the particle counting technology, the developmental status of the ultrasonic technology which is employed on that aspect is investigated principally. At the end of the paper,the improving direction of the wear particle detection technology in the future is presented.

Review on wear particle detection and measurement

The strengths and weaknesses of Fourier transform and wavelet transform were discussed in the process of image-denoising.The image noise reduction methods were proposed using Fourier-wavelet transforms context-based model.The original image noise level was reduced by estimation of power spectral density of the original image in the Fourier domain and the use of Wiener filter.The remaining noise was removed by using adaptive wavelet threshold method based on context modeling.A series of wavelet coefficients were obtained by using the stationary wavelet transform to decompose the image signal.According to the correlation among those coefficients,the variance of wavelet coefficients was calculated by using the context model.The adaptive threshold was acquired by substituting the variance into the threshold expression estimated by the GGD model.Processing wavelet coefficients with soft thresholding function,the image denoise was completed by means of the wavelet inverse transform to wavelet coefficients.Simulation results show that the methods proposed can not only effectively filter out image noise,but also keep the edges of the image detail signal well and inhibit Gibbs phenomenon caused by noise reduction.

Fourier-wavelet image reduction using context-based model

In this study, a new adaptive morphological filter is developed based on the mathematical morphology algorithm and characteristics of the subtle differences in the waveform morphology in seismic data. The algorithm improves the traditional morphological dilation and corrosion operations. In this study, we propose a multiscale adaptive operator based on the principle of morphological structural “probes” and present the corresponding mathematical proof. Simulation experiments and actual seismic data processing results show that compared with traditional morphological filters, the constructed OCCO-based multistructure adaptive morphological filter can suppress noise to the greatest extent. Moreover, it can effectively improve the SNR of the images, and offers great application prospects.

Study and application analysis of random noise adaptive morphological filter algorithm reconstruction for seismic signals

The ultrasonic echo signal of wear debris is influenced by many matters. It causes so much more noise. Therefore, it puts forward an improved online de-noising method for ultrasonic echo signal of wear debris in oil. In the dual-tree complex wavelet transform (DTCWT) field, a method, which combines the new non-linear threshold function with adaptive threshold, utilizes particle swarm optimization (PSO) for optimizing the parameter of non-linear threshold function to get the optimal solution. The result of de-noising method can be evaluated. Experimental results show that the proposed method has obvious effect on signal de-noising.

An Improved Online De-Noising Method for Ultrasonic Echo Signal of Wear Debris in Oil

Image segmentation plays an important role in wear particles analysis. A new segmentation method based on multiscale mathematical morphology is proposed for wear particles image segmentation. The newly introduced method employs different scale structuring elements to detect the image edge, the final edge is calculated by the weighted average method. Edge details can be remained by small scale structuring element (SE) and noise can be depressed effectively by large scale SE, therefore, the new method has great effect in edge accuracy, strong and weak edge extraction and noise suppression. The efficiency of the method is evaluated by a set of wear particles images. The comparison with the single scale SE and other traditional methods demonstrates the improvement of the new algorithm.

Wear Particles Image Segmentation Method Based on Multiscale Mathematical Morphology

Compared with wavelet transform, Curvelet transform has characteristics of anisotropy and good curve singularity expression abilities. To advance the validity and reliability of wear particle feature extraction as well as recognition, an image feature enhancement method based on Curvelet transform was proposed. Wear particle images were decomposed into different frequency components by Curvelet transform. Scale enhancement coefficients were introduced into the medium-frequency and high-frequency components to enhance images’ edges and details. Then, enhanced wear particle images were achieved utilizing inverse Curvelet transform. Experiment results indicate that the proposed method can effectively improve image quality. As the details and edges are clear, enhanced images are more suitable for feature extraction and recognition.

Chao Xu

Papers

An Improved Online De-Noising Method for Ultrasonic Echo Signal of Wear Debris in Oil

Wear Particles Image Segmentation Method Based on Multiscale Mathematical Morphology

Feature Enhancement of Wear Particle Images Based on Curvelet Transform