Curvature enhanced bearing fault diagnosis method using 2D vibration signal

Weifang Sun,Xincheng Cao 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.6

As a novel representation method, two dimensional (2D) segmentation is gaining ground as an effective condition monitoring method due to its high-level information descriptional ability. However, the accuracy of extracting frequency information is still limited by the finite gray-level and the extraction ability of distinguishable texture for each fault. To overcome these drawbacks, this research proposes a bearing fault diagnosis method using the converted 2D vibrational signal matrices. In this method, 1D vibration signals are converted into 2D matrices to exploit the fault signatures from the converted images. Curvature filtering (mean curvature) algorithm is applied to eliminate the overwhelming interfering contents and preserves the necessary edge information contained in the 2D matrix. In addition, the histogram of oriented gradients features is employed for the effective fault feature extraction. Finally, a one-versus-one support vector machine is utilized for the automatically fault classification. An experimental investigation was carried out for the performance evaluation of the proposed method. Comparison results indicate that the established method is capable of bearing fault detection with considerable accuracy.

Complex wavelet enhanced shape from shading transform for estimating surface roughness of milled mechanical components

Sun Weifang,Chen Binqiang,Yao Bin,Cao Xincheng,Feng Wei 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.2

The metal surface topology contains abundant information related to the health states of the cutting tool as well as the cutting operation. In this paper, we attempt to adopt 2D digital images of the machined metal surface, acquired via non-contact photo-imaging techniques, as the monitoring media. A Wallis filter based dodging algorithm is applied to cure the uneven contrast phenomenon caused by imperfect lighting illumination. 3D digital models were derived and retrieved from the digital image using a wavelet enhanced Shape from shading (SFS) transform. The minimization based SFS is presented to retrieve the 3D digital surface from the milled workpiece. The dual tree complex wavelet transform is adopted to enhance SFS such that the interfering noise can be suppressed. In the end, quantitative surface roughness indicators are utilized to estimate the surface roughness numerically. A milling cutting experiment of aero-material of aluminum alloy 7075 was carried out to verify the effectiveness of the proposed approach. The comparison results demonstrate that the proposed approach was capable of retrieving 3D surfaces of high precision. With the approach, the digital image emerges as a promising vehicle for machining condition monitoring of CNC machines.

Crack detection in concrete slabs by graph-based anomalies calculation

Yuqing Zhou,Weifang Sun,Jiawei Xiang,Binqiang Chen,Wei Feng 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.3

Concrete slab cracks monitoring of modern high-speed railway is important for safety and reliability of train operation, to prevent catastrophic failure, and to reduce maintenance costs. This paper proposes a curvature filtering improved crack detection method in concrete slabs of high-speed railway via graph-based anomalies calculation. Firstly, large curvature information contained in the images is extracted for the crack identification based on an improved curvature filtering method. Secondly, a graph-based model is developed for the image sub-blocks anomalies calculation where the baseline of the subblocks is acquired by crack-free samples. Once the anomaly is large than the acquired baseline, the sub-block is considered as crack-contained block. The experimental results indicate that the proposed method performs better than convolutional neural network method even under different curvature structures and illumination conditions. This work therefore provides a useful tool for concrete slabs crack detection and is broadly applicable to variety of infrastructure systems.

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Zhi Lei,Qinsong Zhu,Yuqing Zhou,Bintao Sun,Weifang Sun,Xiaoming Pan 한국정밀공학회 2021 International Journal of Precision Engineering and Vol.8 No.3

Tools are the most vulnerable components in milling processes conducted using numerical control milling machines, and their wear condition directly influences work-product quality and operational safety. As such, tool wear estimation is an essential component of NC milling operations. This study addresses this issue by proposing an extreme learning machine (ELM) method enhanced by a hybrid genetic algorithm and particle swarm optimization (GAPSO) approach for conducting tool wear estimation based on workpiece vibration signals. Here, a few feature parameters in the time, frequency, and time–frequency (Ensemble empirical mode decomposition, EEMD) domains of the workpiece vibration signals are extracted as the input of the ELM model. Then, the initialized weights and thresholds of the ELM model are optimized based on the GAPSO approach with training dataset. Finally, tool wear is estimated using the optimized ELM model with testing dataset. The effectiveness of the proposed method is verified by its application to vibration signals collected from two milling tool wear experiments (an open-access benchmark dataset and a milling tool wear experiment) by comparison to the ELM, GA-ELM, and PSO-ELM methods. The results indicate that the estimation accuracy and optimization efficiency of the proposed method outperforms that of other three methods.

Generating Grinding Profile between Screw Rotor and Forming Tool by Digital Graphic Scanning (DGS) Method

Zhihuang Shen,Bin Yao,Weibin Teng,Wei Feng,Weifang Sun 한국정밀공학회 2016 International Journal of Precision Engineering and Vol.17 No.1

This work presents a digital graphic scanning (DGS) method, based on computer scanning graphics, to generate a grinding profile avoiding the difficulties appeared from the complex equations of the contact line. First the enveloping surface between the forming tool (rotor) profile and its corresponding cutting locus was developed, then based on Bresenham algorithm, the best possible pixels of the enveloping surface in the pixel matrix of screen were demonstrated using a specified color. Finally, the grinding profile data of the rotor (forming tool) were collected by scanning the pixel matrix of screen, capturing the coordinates of the indicated color of the best possible pixels. Comparing the analytical gearing envelope method and the DGS method, the feasibility of the DGS method was indicated. The DGS method was shown as a precise, rapid, efficient and stable computing tool to generate a grinding profile. In addition, such an approach can be applied in designing other similarly conjugated products such as gears, perpetual screws and milling cutters.

Remaining useful life prediction of circuit breaker operating mechanisms based on wavelet‑enhanced dual‑tree residual networks

Tailong Wu,Yuan Yao,Zhihao Li,Binqiang Chen,Yue Wu,Weifang Sun 전력전자학회 2024 JOURNAL OF POWER ELECTRONICS Vol.24 No.1

The remaining useful life prediction of circuit breaker operating mechanisms is crucial for the condition-based maintenance of national power grids. To realize accurate remaining useful life prediction, a novel wavelet-enhanced dual-tree residual network is proposed in this paper. Through this wavelet transform, the time series is decomposed into two components (high frequency and low frequency). Then the two decomposed components are fed into two lightweight residual neural network structures. By concatenating the dual-tree features, the remaining useful life of a circuit breaker operating mechanism can be predicted. The proposed network is validated using a full-life cycle experiment of the circuit breaker operating mechanism. Results show that the proposed method has good capability when it comes to predicting the remaining useful life of the circuit breaker operating mechanism. Along with application in the construction of smart grids and green energy, it is expected that the proposed method has potential in running state prognostics of circuit breakers.

Cloning and Characterization of a β-Glucosidase from Marine Microbial Metagenome with Excellent Glucose Tolerance

( Wei Fang ),( Bao Lin Sun ),( Yu Zhi Hong ),( Ze Min Fang ),( Juan Juan Liu ),( Hui Peng ),( Xue Cheng Zhang ),( Ya Zhong Xiao ) 한국미생물 · 생명공학회 2010 Journal of microbiology and biotechnology Vol.20 No.9