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Infrastructure sensing from onboard diagnostic channels

Description

Intelligent assessment of infrastructure condition based on data obtained from onboard diagnostic means of the high-speed EMU. Built using mathematical data analysis algorithms applied to data from vibration acceleration sensors located on the running gear of the EMU.

Goals and objectives

Creation of a system that enables determination of infrastructure technical condition based on data obtained through centralised collection and processing of information from onboard measurement and monitoring means

Characteristics

1. The intelligent system for monitoring interaction between infrastructure and the high-speed EMU is built using mathematical data analysis algorithms applied to data from vibration acceleration sensors on the EMU running gear and from line current and voltage measurement sensors, as well as based on visual inspection by train and locomotive crew members using video cameras installed on the EMU roof.

2. Measured accelerations used for track condition monitoring serve to detect local track geometry deviations that affect EMU dynamic behaviour.

3. Measured line current and voltage values that are part of the high-voltage traction system of the EMU, as well as video signals from roof-mounted cameras, can be used to detect deviations in catenary parameters.

Applied technologies

1. Diagnostic data is collected by connecting to data transmission networks using networks built on various technologies and principles, including but not limited to: CAN;

Ethernet.

2. Data accumulation and storage uses a relational database optimised for real-time systems, with heightened requirements for data reliability and security.

3. Use of mathematical data analysis algorithms, including artificial intelligence.

4. Software is developed using:

  • programming languages: C, C++, Python;
  • development tools: Visual Studio, Eclipse, NetBeans;
  • source code management tools: SVN, Git;
  • requirements management tools: T-FLEX RM.

5. Data analysis algorithms are developed using mathematical computation and dynamic modelling environments Engee and SimInTech.

Infrastructure sensing from onboard diagnostic channels