HL Mando Develops AI-Driven Predictive Maintenance Platform for Testing Automotive Components
Digital Twins Accelerate Component Testing While Reducing Costs
HL Mando built Smart Lab entirely with MATLAB and Simulink—combining AI, IoT, and digital twins into a predictive maintenance platform.
Key Outcomes
- HL Mando created a connected, AI-based web platform for predictive maintenance that effectively estimates remaining useful life
- The team augmented their data set using digital twins in Simscape, enabling them to train accurate AI models
- MATLAB and Simulink enabled the team to significantly reduce the costs and time required for equipment maintenance
HL Mando, a global automotive parts supplier, needed to ensure that its autonomous vehicle components met the highest standards of reliability and durability. To address the demanding testing requirements of automotive manufacturers, the HL Mando team used MATLAB® and Simulink® to develop Smart Lab—a connected system of testbenches featuring IoT-based monitoring, AI-driven data analysis, and predictive maintenance capabilities. This solution significantly enhanced the efficiency and effectiveness of their component testing process.
The team used digital twins in Simscape™ to augment their data set and Simulink® for system-level simulation and control design. Using AI tools such as Predictive Maintenance Toolbox™, they estimated the remaining useful life of equipment components. The resulting predictive maintenance platform, which was deployed with MATLAB Web App Server™, significantly reduced costs, labor, and time required for maintenance.
Products Used
Related Resources
