Concept of Early Prediction and Identification of Truck Vehicle Failures Supported by In-Vehicle Telematics Platform Based on Abnormality Detection Algorithm DOI Creative Commons
Iouri Semenov, Andrzej Świderski, Anna Borucka

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(16), С. 7191 - 7191

Опубликована: Авг. 15, 2024

As automotive technology advances in the realm of digitization, vehicles are becoming smarter and, at same time, more vulnerable to various threats. This paper focuses on techniques for detecting faults mitigate risk freight transportation. Our observations show that vehicle uptime varies significantly even under similar operating conditions. variation stems from differences wear and tear moving stationary parts, characteristics transported loads, driving styles, quality maintenance, etc. These factors particularly crucial abnormal designed carry AILs (Abnormal Indivisible Loads). Such especially prone surprising threats, requiring efficient monitoring separate components providing drivers with vital information about their operational status. The presented article proposes an original concept integrated three-level system based AOP (All-in-One Platform) principle, using DBSCAN (Density-Based Spatial Clustering Applications Noise) algorithm, which is a tool oriented distinguish points three categories: basic, boundary, external. solution not yet found literature. It assessments LOFs (Local Outlier Factors) detect anomalies measured values parameters. purpose our study was determine whether truck current active threat warning could help reduce unplanned downtimes.

Язык: Английский

Concept of Early Prediction and Identification of Truck Vehicle Failures Supported by In-Vehicle Telematics Platform Based on Abnormality Detection Algorithm DOI Creative Commons
Iouri Semenov, Andrzej Świderski, Anna Borucka

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(16), С. 7191 - 7191

Опубликована: Авг. 15, 2024

As automotive technology advances in the realm of digitization, vehicles are becoming smarter and, at same time, more vulnerable to various threats. This paper focuses on techniques for detecting faults mitigate risk freight transportation. Our observations show that vehicle uptime varies significantly even under similar operating conditions. variation stems from differences wear and tear moving stationary parts, characteristics transported loads, driving styles, quality maintenance, etc. These factors particularly crucial abnormal designed carry AILs (Abnormal Indivisible Loads). Such especially prone surprising threats, requiring efficient monitoring separate components providing drivers with vital information about their operational status. The presented article proposes an original concept integrated three-level system based AOP (All-in-One Platform) principle, using DBSCAN (Density-Based Spatial Clustering Applications Noise) algorithm, which is a tool oriented distinguish points three categories: basic, boundary, external. solution not yet found literature. It assessments LOFs (Local Outlier Factors) detect anomalies measured values parameters. purpose our study was determine whether truck current active threat warning could help reduce unplanned downtimes.

Язык: Английский

Процитировано

0