Real time lane detection model based on Lightweight

2020 ◽  
Author(s):  
Guangkun Zhai
Keyword(s):  
Real Time ◽  
Lane Detection ◽  
Detection Model ◽  
Model Based

A novel abnormal detection method for bearing temperature based on spatiotemporal fusion

2021 ◽  
pp. 095440972110221
Author(s):  
Yong Zhi Liu ◽  
Yi Sheng Zou ◽  
Yu Wu ◽  
Hao Yang Zhang ◽  
Guo Fu Ding
Keyword(s):  
Decision Making ◽  
Anomaly Detection ◽  
Real Time ◽  
Early Warning ◽  
High Speed ◽  
Entropy Method ◽  
High Speed Train ◽  
Detection Model ◽  
Time Prediction ◽  
Model Based

The existing bearing temperature fault detection and early warning system has a high false alarm rate and insufficient early warning ability. For this reason, in this study, a method for detecting the abnormal bearing temperature of high-speed trains based on spatiotemporal fusion decision-making was proposed. First, the temperature characteristics of similar bearings were compared and analyzed with different spatial distributions. Then, a bearing abnormal temperature rise detection model based on the analytic hierarchy process (AHP) entropy method was proposed. Second, the temperature characteristics of the same bearings were compared and analyzed with different time distributions. A real-time prediction model of high-speed train bearing temperature anomalies based on Bi-directional Long Short-Term Memory (BILSTM) was proposed. Finally, the D-S evidence theory was used to combine the anomaly detection model based on the AHP entropy method and the anomaly detection model based on BILSTM real-time prediction. Through the comprehensive diagnosis and decision-making of high-speed train bearings from two dimensions of space and time, a more comprehensive and accurate anomaly detection model was realized. The experimental results showed that the spatiotemporal comparison fusion decision model successfully eliminated the misjudgment phenomenon of single-dimension model diagnosis and that it has good early warning ability.

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