Haijie Yu, Haijun Wei, Daping Zhou, Jingming Li and Hong Liu
This study aims to reconstruct the frictional vibration signal from noise and characterize the running-in process by frictional vibration.
Abstract
Purpose
This study aims to reconstruct the frictional vibration signal from noise and characterize the running-in process by frictional vibration.
Design/methodology/approach
There is a strong correlation between tangential frictional vibration and normal frictional vibration. On this basis, a new frictional vibration reconstruction method combining cross-correlation analysis with ensemble empirical mode decomposition (EEMD) was proposed. Moreover, the concept of information entropy of friction vibration is introduced to characterize the running-in process.
Findings
Compared with the wavelet packet method, the tangential friction vibration and the normal friction vibration reconstructed by the method presented in this paper have a stronger correlation. More importantly, during the running-in process, the information entropy of friction vibration gradually decreases until the equilibrium point is reached, which is the same as the changing trend of friction coefficient, indicating that the information entropy of friction vibration can be used to characterize the running-in process.
Practical implications
The study reveals that the application EEMD method is an appropriate approach to reconstruct frictional vibration and the information entropy of friction vibration represents the running-in process. Based on these results, a condition monitoring system can be established to automatically evaluate the running-in state of mechanical parts.
Originality/value
The EEMD method was applied to reconstruct the frictional vibration. Furthermore, the information entropy of friction vibration was used to analysis the running-in process.
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Keywords
Qingxiang Zhou, Fang Liu, Jingming Li, Jiankui Li, Shuangnan Zhang and Guixi Cai
This study aims to solve the problem of weld quality inspection, for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin…
Abstract
Purpose
This study aims to solve the problem of weld quality inspection, for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness (2–4 mm), the conventional nondestructive testing method of weld quality is difficult to implement.
Design/methodology/approach
In order to solve this problem, the ultrasonic creeping wave detection technology was proposed. The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks. The detection technology was used to test the actual welded test blocks, and compared with the results of X-ray test and destructive test (tensile test) to verify the accuracy of the ultrasonic creeping wave test results.
Findings
It is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects. However, due to special detection method and protection, the detection speed is slow, which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body. It can be used as an auxiliary detection method for a small number of sampling inspection. The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more, the results of creeping wave detection correspond well with the actual incomplete penetration defects.
Originality/value
The results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints. It is recommended to use the echo amplitude of the 10 mm × 0.2 mm × 0.5 mm notch as the criterion for weld qualification.
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This study aims to apply deep convolutional neural network Mask-R-CNN algorithm based on transfer learning to realize the segmentation of online wear fragments.
Abstract
Purpose
This study aims to apply deep convolutional neural network Mask-R-CNN algorithm based on transfer learning to realize the segmentation of online wear fragments.
Design/methodology/approach
Wear debris analysis is considered to be one of the most effective methods to maintain the condition of mechanical equipment. In this paper, the friction and wear testing machine was used to design pin-disk rotation, pin-disk reciprocation and four-ball test to produce cutting, sliding, laminar and fatigue debris. A semi-online sampling system was designed to collect ferrographic images containing various fragments. The images were rotated and flipped to augment the data and enhance the generalization ability of the model. The data set required for data analysis is established. Using COCO pre-trained Mask R-CNN data set as a benchmark, the region proposal network (RPN) is trained with labeled wear debris images to enhance the ability of RPN to recognize background and wear debris. Two transfer learning scenarios are tested in the network head of the Mask R-CNN.
Findings
The results show that the deep convolutional neural network is suitable for the automatic classification and detection of wear fragments. Through transfer learning and proper training configuration, the ferrographic image recognition based on Mask R-CNN achieves high accuracy.
Originality/value
The results show that the deep convolutional neural network is suitable for the automatic classification and detection of wear fragments. Through transfer learning and proper training configuration, the ferrographic image recognition based on Mask R-CNN achieves high accuracy.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0182/
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Hong Liu, Haijun Wei, Haibo Xie, Lidui Wei and Jingming Li
The possibility of using a pattern recognition system for wear particle analysis without the need of a human expert holds great promise in the condition monitoring industry…
Abstract
Purpose
The possibility of using a pattern recognition system for wear particle analysis without the need of a human expert holds great promise in the condition monitoring industry. Auto-segmentation of their images is a key to effective on-line monitoring system. Therefore, an unsupervised segmentation algorithm is required. The purpose of this paper is to present a novel approach based on a local color-texture feature. An algorithm is specially designed for segmentation of wear particles’ thin section images.
Design/methodology/approach
The wear particles were generated by three kinds of tribo-tests. Pin-on-disk test and pin-on-plate test were done to generate sliding wear particles, including severe sliding ones; four-ball test was done to generate fatigue particles. Then an algorithm base on local texture property is raised, it includes two steps, first, color quantization reduces the total quantity of the colors without missing too much of the detail; second, edge image is calculated and by using a region grow technique, the image can be divided into different regions. Parameters are tested, and a criterion is designed to judge the performances.
Findings
Parameters have been tested; the scale chosen has significant influence on edge image calculation and seeds generation. Different size of windows should be applied to varies particles. Compared with traditional thresholding method along with edge detector, the proposed algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the present method is suited for wear particles’ image segmentation and can be put into practical use in wear particles’ identification system.
Research limitations/implications
One major problem is when small particles with similar texture are attached, the algorithm will not take them as two but as one big particle. The other problem is when dealing with thin particles, mainly abrasive particles, the algorithm usually takes it as a single line instead of an area. These problems might be solved by introducing a smaller scale of 9 × 9 window or by making use of some edge enhance technique. In this way, the subtle edges between small particles or thin particles might be detected. But the effectiveness of a scale this small shall be tested. One can also magnify the original picture to double or even triple its size, but it will dramatically increase the calculating time.
Originality/value
A new unsupervised segmentation algorithm is proposed. Using the property of the edge image, we can get target out of its background, automatically. A rather complete research is done. The method is not only introduced but also completely tested. The authors examined parameters and found the best set of parameters for different kinds of wear particles. To ensure that the proposed method can work on images under different condition, three kinds of tribology tests have been carried out to simulate different wears. A criterion is designed so that the performances can be compared quantitatively which is quite valuable.
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Hong Liu, Haijun Wei, Lidui Wei, Jingming Li and Zhiyuan Yang
This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking…
Abstract
Purpose
This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking place between mechanical components. Identification of the type of wear particles by image processing and pattern recognition is key to effective online monitoring algorithm. There are three kinds of particles that are particularly difficult to distinguish: severe sliding wear particles, fatigue spall particles and laminar particles.
Design/methodology/approach
In this study, an identification method is tested using the deterministic tourist walking (DTW) method. This study examined whether this algorithm can be used in particle identification. If it does, can it outperform the traditional texture analysis methods such as Discrete wavelet transform or co-occurrence matrix. Different parameters such as walk’s memory size, size of image samples, different inputting vectors and different classifiers were compared.
Findings
The DTW algorithm showed promising result compared to traditional texture extraction methods: discrete wavelet transform and co-occurrence matrix. The DTW method offers a higher identification accuracy and a simple feature vector. A conclusion can be drawn that the DTW method is suited for particle identification and can be put into practical use in condition monitoring systems.
Originality/value
This paper combined DTW algorithm with wear particle identification problem.
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Jingming Hou, Xuan Nan, Guodong Li, Xuedong Wang, Sipeng Zhu and Yongde Kang
Since surface runoff clogs stormwater grates, leading to deterioration of drainage capacity, and also it is difficult to complete the study with actual dimensions in experiments…
Abstract
Purpose
Since surface runoff clogs stormwater grates, leading to deterioration of drainage capacity, and also it is difficult to complete the study with actual dimensions in experiments, a numerical simulation work was established in this study to investigate the surface runoff clogging stormwater grate patterns. The purpose of this study is to describe the mechanisms of storm grate clogging and storm well deposition for different flow rate floods and granular materials.
Design/methodology/approach
In the work of this study, the volume of fluid (VOF) method and the discrete element method (DEM) are used to solve the gas–liquid and particle flows. In order to solve the evolution of the gas–liquid interface during surface runoff, the VOF was used. To simulate the rain grate and set up different material particles to represent the surface floating materials, the DEM was utilized.
Findings
The research results show that the clogging distribution and clogging rate of the rainwater grate are closely related to the fluid flow velocity and the physical characteristics of the particles, and the higher the clogging rate of the rainwater grate and the higher the number of particles deposited in the rainwater well at the same surface runoff velocity, the higher the density of the clogged particles. The surface runoff velocity (0.5 m/s, 1 m/s) shows that the rapid change of particle movement state at high runoff velocity makes the particle clogging more obvious.
Originality/value
A multi-scale CFD-DEM approach was used to simulate the particulate motion of the road surface with different incoming runoff velocities. The innovative use of DEM to model the storm grate simulation ensures the accuracy of the traction model.
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Wenhuan Ai, Zheng Qing Lei, Li Danyang, Jingming Zeng and Dawei Liu
Highway traffic systems are complex and variable, and studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable bifurcation…
Abstract
Purpose
Highway traffic systems are complex and variable, and studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable bifurcation points can alleviate traffic congestion from a new perspective. Bifurcation analysis is used to explain the changes in system stability, identify the unstable bifurcation points of the system, and design feedback controllers to realize the control of the unstable bifurcation points of the traffic system. It helps to control the sudden changes in the stable behavior of the traffic system and helps to alleviate traffic congestion, which is of great practical significance.
Design/methodology/approach
In this paper, we improve the macroscopic traffic flow model by integrating severe weather factors such as rainfall, snowfall, and dust. We use traveling wave transform to convert it into a traffic flow stability model suitable for branching analysis, thus converting the traffic flow problem into a system stability analysis problem. First, this paper derives the existence conditions of the model Hopf bifurcation and saddle-node bifurcation for the improved macroscopic model, and finds the stability mutation point of the system. Secondly, the connection between the stability mutation points and bifurcation points of the traffic system is analyzed. Finally, for the unstable bifurcation point, a nonlinear system feedback controller is designed using Chebyshev polynomial approximation and stochastic feedback control method.
Findings
The Hopf bifurcation is delayed and completely eliminated without changing the equilibrium point of the system, thus controlling the abrupt behavior of the traffic system.
Originality/value
Currently there are fewer studies to explain the changes in the stability of the transportation system through bifurcation analysis, in this paper; we design a feedback controller for the unstable bifurcation point of the system to realize the control of the transportation system. It is a new research method that helps to control the sudden change of the stable behavior of the traffic system and helps to alleviate traffic congestion, which is of great practical significance.