WP-DRnet: A novel wear particle detection and recognition network for automatic ferrograph image analysis

2020 ◽  
Vol 151 ◽  
pp. 106379
Author(s):  
Yeping Peng ◽  
Junhao Cai ◽  
Tonghai Wu ◽  
Guangzhong Cao ◽  
Ngaiming Kwok ◽  
...  
Sensors ◽  
2017 ◽  
Vol 17 (3) ◽  
pp. 586 ◽  
Author(s):  
Jon Mabe ◽  
Joseba Zubia ◽  
Eneko Gorritxategi

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3162 ◽  
Author(s):  
Ran Jia ◽  
Biao Ma ◽  
Changsong Zheng ◽  
Xin Ba ◽  
Liyong Wang ◽  
...  

The electromagnetic wear particle detector has been widely studied due to its prospective applications in various fields. In order to meet the requirements of the high-precision wear particle detector, a comprehensive method of improving the sensitivity and detectability of the sensor is proposed. Based on the nature of the sensor, parallel resonant exciting coils are used to increase the impedance change of the exciting circuit caused by particles, and the serial resonant topology structure and an amorphous core are applied to the inductive coil, which improves the magnetic flux change of the inductive coil and enlarges the induced electromotive force of the sensor. Moreover, the influences of the resonance frequency on the sensitivity and effective particle detection range of the sensor are studied, which forms the basis for optimizing the frequency of the magnetic field within the sensor. For further improving the detectability of micro-particles and the real-time monitoring ability of the sensor, a simple and quick extraction method for the particle signal, based on a modified lock-in amplifier and empirical mode decomposition and reverse reconstruction (EMD-RRC), is proposed, which can effectively extract the particle signal from the raw signal with low signal-to-noise ratio (SNR). The simulation and experimental results show that the proposed methods improve the sensitivity of the sensor by more than six times.


Author(s):  
Xiaomou Zhou ◽  
Xiaoping Ma

In order to accurately detect various micro particles in patient's urinary samples, a urinary micro particle detection system based on spatial coordinate tracking method was developed. A hierarchical microscopic image analysis method was described and the detailed spatial coordinate tracking algorithm was designed. The architecture and the procedure of proposed system were presented. Moreover, the prototype system was introduced and six patients' urinary samples were analyzed to verify the reliability and simplicity of unattended operation.


1999 ◽  
Vol 121 (1) ◽  
pp. 169-176 ◽  
Author(s):  
Z. Peng ◽  
T. B. Kirk

Although the morphology of wear debris generated in a machine has a direct relationship to wear processes and machine condition, studying wear particles for machine condition monitoring has not been widely applied in Industry as it is time consuming and requires certain expertise of analysts. To overcome these obstacles, automatic wear particle analysis and identification systems need to be developed. In this paper, laser scanning confocal microscopy has been used to obtain three-dimensional images of metallic wear particles. An analysis system has been developed and applied to study the boundary morphology and surface topography of the wear debris. After conducting the image analysis procedure and selecting critical criteria from dozens of available parameters, neural networks and grey systems have been investigated to classify unknown patterns using the numerical descriptors. It is demonstrated that the combination of the image analysis system and automatic classification systems has provided an automatic package for wear particle study which may be applied to industrial applications in the future.


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