Regular inspections of subway shield doors require a lot of time and resources.
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Traditional inspection methods may lack specificity and cannot accurately identify potential problems with shield doors.
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There is a need to improve the operational efficiency and predictive maintenance capabilities of subway shield doors.
Digital China has helped the customer combine machine learning technology with industrial control equipment data, collecting dynamic data of shield door DCU control units for opening and closing doors. Through techniques such as clustering, dynamic time warping, and data interpolation simulation, intelligent assessment and modeling of the health of shield doors are performed.
Accurately identify shield doors that require maintenance through intelligent assessment, improving inspection efficiency.
Reduce unnecessary inspections and lower operational costs.
Improve the work efficiency of the subway operation company through automated data analysis and reporting.
Achieve predictive maintenance and reduce unexpected failures through health scoring and trend analysis.
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