August 26–28, 2026
Shanghai World Expo Exhibition and Convention Center

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New-generation rugged DAQ and modal analysis technology

Siemens Industry Software (Shanghai) Co., Ltd. Hall: 1 Stand: 11000
New-generation rugged DAQ and modal analysis technology

Facing the massive data volumes and stringent test environments brought by electric vehicles (EVs) and intelligent driving technologies, Siemens Simcenter has launched its new-generation RS rugged data acquisition system, integrating advanced 5G connectivity and test process automation solutions. The RS system, with its exceptional durability and modular design, ensures stable acquisition of high-precision data in any complex scenario. Concurrently, leveraging 5G's ultra-high-speed and low-latency transmission capabilities, it achieves real-time data backhaul and remote collaboration, breaking through the spatial and temporal limitations of traditional testing.  

More importantly, by deeply integrating hardware with the Simcenter software platform, Siemens has achieved a high degree of automation from data acquisition to analysis report generation. Combined with AI algorithms to optimize test parameters, this significantly enhances test efficiency and accuracy, enabling engineers to accelerate the R&D and validation of EVs and intelligent driving systems in a smarter, more efficient manner, quickly transforming innovative concepts into reality. 

In structural dynamics analysis, modal testing is crucial for understanding and optimizing a vehicle's NVH (noise, vibration and harshness) performance. Siemens Simcenter has introduced an enhanced version of its dual-hammer modal testing and AI-assisted modal analysis technology. This innovation optimizes traditional dual-hammer testing methods, allowing them to perform better in complex structures and high-frequency response analysis.  

More importantly, by integrating advanced AI algorithms, it greatly improves the efficiency and accuracy of modal parameter identification. AI can automatically identify and process noise and anomalies in test data, intelligently recommend optimal analysis parameters, and accelerate the establishment and validation of modal models. This enables engineers to obtain reliable modal results more quickly, thereby conducting structural optimization design more efficiently, ensuring excellent NVH performance for electric vehicles during operation. 

Booth 11000

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