System Design on CAN BUS Based on Computer Repair Detection

In this paper, we use the Windows as computer operating environment, VC software as development platform, USB and Access as data interface, and design the CAN bus detection platform of vehicle maintenance, and use parallel technology to improve the platform function. Improved detection platform has the capacity of parallel computing, which greatly improves the speed of vehicle maintenance. In order to verify the effectiveness and reliability of detection platform, we use the CAN bus hardware as a test environment to test the CAN bus detection platform. Through the test we found that, during the same range of detection time, method of CAN bus will detect more times than general detection method, the fault detection rate is higher, which is a very effective method for detecting and repairing automobile. It provides the technical reference for the study of automobile repair and detection.

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The Fault Detection Method of CAN Bus Based on Network Resistance Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.1948 ◽

2011 ◽

Vol 58-60 ◽

pp. 1948-1952

Author(s):

Chuan Qiang Yu ◽

Zhen Dong Qi ◽

Zhen Ye Wang ◽

Yu Wang

Keyword(s):

Fault Detection ◽

Error Detection ◽

Data Exchange ◽

Detection Method ◽

Can Bus ◽

High Reliability ◽

Detection Mechanism ◽

Network Bandwidth ◽

Resistance Model ◽

Bus Network

In the error detection mechanism of CAN bus, when the failed node or line is not in the data exchange path, you will not detect the fault, in order to solve this problem, a method is proposed which is realized by adding an external hardware detection circuit in the CAN-bus network, the fault will be detected through testing the resistance of the CAN bus network. In this paper, the network resistance model of CAN bus is established, and the principle of network resistance fault detection method is analyzed. We have carried out several experiments by the network with three nodes to test the validity of this method. As the results of our experiments, we concluded that the method can real-time and comprehensively detect the fault of network and do not take up the network bandwidth, so this method can effectively resolve the problems of current detection mechanism and have good application prospect in some high reliability requirements occasions.

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Body Parts Features-Based Pedestrian Detection for Active Pedestrian Protection System

PROMET - Traffic&Transportation ◽

10.7307/ptt.v28i2.1720 ◽

2016 ◽

Vol 28 (2) ◽

pp. 133-142 ◽

Cited By ~ 2

Author(s):

Lie Guo ◽

Mingheng Zhang ◽

Linhui Li ◽

Yibing Zhao ◽

Yingzi Lin

Keyword(s):

Detection Rate ◽

Detection Method ◽

Detection System ◽

Pedestrian Detection ◽

Urban Traffic ◽

Body Parts ◽

Detection Time ◽

Pedestrian Protection ◽

Adaboost Algorithm ◽

Threshold Adjustment

A novel pedestrian detection system based on vision in urban traffic situations is presented to help the driver perceive the pedestrian ahead of the vehicle. To enhance the accuracy and to decrease the time spent on pedestrian detection in such complicated situations, the pedestrian is detected by dividing their body into several parts according to their corresponding features in the image. The candidate pedestrian leg is segmented based on the gentle AdaBoost algorithm by training the optimized histogram of gradient features. The candidate pedestrian head is located by matching the pedestrian head and shoulder model above the region of the candidate leg. Then the candidate leg, head and shoulder are combined by parts constraint and threshold adjustment to verify the existence of the pedestrian. Finally, the experiments in real urban traffic circumstances were conducted. The results show that the proposed pedestrian detection method can achieve pedestrian detection rate of 92.1% with the average detection time of 0.2257 s.

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A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207430 ◽

2020 ◽

pp. 129-139

Author(s):

Weihai Sun ◽

Lemei Han

Keyword(s):

Fault Detection ◽

Signal Analysis ◽

Energy Analysis ◽

Detection Method ◽

Sound Signal ◽

Practical Significance ◽

Time Frequency ◽

Detection Approach ◽

Machine Fault ◽

Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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