Safeguarding Track Safety with Edge Intelligence: BRAV-7131 Empowers Integrated Intelligent Track Inspection System

With the increasing density of urban rail transit networks and the continuous increase in operational load, the health status of track facilities directly affects the operational safety and efficiency of the entire line. The traditional Operation and Maintenance (O&M) model, relying on manual inspections and post-event analysis, is no longer sufficient to meet the safety requirements of modern subway operations in terms of real-time performance, accuracy, and coverage frequency. Against this backdrop, the industry is actively leveraging cutting-edge technologies such as artificial intelligence (AI), the Internet of Things (IoT), and edge computing to drive the evolution of Operations and Maintenance (O&M) Systems for Facilities towards a smarter approach characterized by "real-time perception, intelligent diagnosis, and proactive early warning." This aims to achieve full-process dynamic monitoring and precise control of track conditions.

Customer Application Requirements

High-Precision, Real-Time, and Intelligent Track Inspection System

In intelligent operation and maintenance systems for metro infrastructure, the core task of track inspection is to achieve high-precision data acquisition and intelligent defect identification and analysis. This not only places high demands on algorithms and systems, but also imposes clear and stringent functional requirements on the underlying edge computing hardware:

✔ Powerful AI Computing Capabilities: Sufficient computing power is required to run complex deep learning models in real time on the vehicle, ensuring a defect identification accuracy rate exceeding 95%.

✔ Multi-Source Heterogeneous Data Access and Synchronization Capabilities: Abundant high-speed I/O interfaces are needed to simultaneously access data from multiple sensors, with hardware-level synchronization capabilities to ensure data spatiotemporal consistency.

✔ High-Speed Real-Time Processing and Low Latency: Real-time acquisition, processing, and analysis of massive amounts of image data are essential for trains operating at speeds of 80 km/h and above, with consistently low latency and no frame drops to meet the requirements of "inspection and judgment on the spot."

✔ Flexible Scalability and Integration: A modular design facilitates interface expansion and compatibility with multiple communication protocols (such as CAN and Ethernet), adapting to the integration needs of different vehicle models and inspection equipment.

JHCTECH Solution:

Orin Processor-Based BRAV-7131 Edge Computer

JHCTECH's BRAV-7131 is a high-performance edge computer equipped with an NVIDIA Jetson AGX Orin module, boasting up to 275 TOPS of AI computing power. Its powerful capabilities perfectly meet the stringent requirements of onboard intelligent inspection systems for rail transit.

Deployed on the onboard integrated track inspection platform of the railway intelligent operation and maintenance system, it undertakes the tasks of front-end data acquisition, intelligent processing, and real-time analysis for track inspection vehicles. It processes massive amounts of data in real time under high-speed train operation, achieving an intelligent inspection mode of "acquiring, analyzing, and making decisions simultaneously," thereby significantly improving track inspection efficiency and data value.

🔹Multi-source data acquisition capability

The BRAV-7131 features 5 LAN, 4 USB, 4 COM, and 2 CAN interfaces, enabling easy simultaneous connection to multiple peripheral acquisition devices such as 4K high-definition industrial cameras, laser profilometers, and inertial measurement units (IMUs) for simultaneous high-definition imaging of key track components such as rails, turnouts, fasteners, sleepers, and ballast.

🔹 Powerful AI Computing Capabilities Enable Intelligent Image Processing and Real-Time Edge Recognition

The BRAV-7131 leverages the powerful GPU computing power of the NVIDIA Orin platform (up to 275 TOPS AI performance) to directly run deep learning models on-board, performing real-time intelligent analysis of acquired track images. The customer's onboard track inspection system uses convolutional neural networks (CNN) and object detection algorithms. Through the BRAV-7131, it can automatically identify and label track surface defects (such as cracks, missing pieces, looseness, foreign object intrusion, etc.), with an accuracy rate of over 95%. Simultaneously, the recognition results are fused in real-time with train operation information (mileage, section, train number, route, time, etc.) to generate structured data, directly serving the backend maintenance system.

🔹 Edge Intelligent Analysis and Rapid Decision-Making

Supporting a complete edge computing architecture, the system performs data preprocessing, preliminary defect screening, and alarm triggering directly on the train side, realizing a closed-loop process of “data acquisition – analysis – decision-making.” This significantly shortens the response cycle for track defect detection and maintenance.

🔹 High Reliability and Excellent Onboard Adaptability

The BRAV-7131 features a high-reliability design with wide temperature operation, vibration resistance, and wide-range power input, ensuring long-term stable performance in the complex operating environments of metro vehicles.

Its modular design facilitates interface expansion, is compatible with various sensors and communication protocols, and adapts to different railway lines and vehicle models for various detection configurations.

Application Value

Four-dimensional improvement in efficiency, accuracy, safety, and cost

Through the deployment of the BRAV-7131 edge computing platform, the track inspection process has evolved from the traditional model of “data acquisition – upload – offline analysis” to a new paradigm of “onboard real-time detection – intelligent identification – instant alert.”

⭐Efficiency Improvement: Enables integrated data acquisition and analysis, reducing manual intervention and offline processing time.

⭐Accuracy Enhancement: Combines deep learning–based recognition algorithms with high-resolution imaging to improve the precision of track defect detection.

⭐Safety Enhancement: Provides real-time alerts and rapid responses to reduce operational risks caused by track defects.

⭐Cost Optimization: Reduces manual inspection frequency, extends the service life of track equipment, and lowers maintenance costs.

With its powerful AI computing performance, rich I/O functionality, and outstanding onboard adaptability, the BRAV-7131 precisely meets the core edge computing requirements of intelligent rail inspection, becoming an indispensable cornerstone of smart operation and maintenance systems across the industry.

Moving forward, JHCTECH will continue to deepen its expertise in edge intelligence, delivering more efficient and reliable embedded solutions for rail transportation, intelligent manufacturing, and smart logistics applications.

JHCTECH – Your Reliable Edge Computing Partner

Making Intelligent Operations and Maintenance Simpler and More Efficient

Learn more about BRAV-7131 series