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Equipment Failure Prediction and Health Assessment Project for a Subway Operator
Providing Intelligent Assistance for Shield Door Status Monitoring
Client Profile
The subway operator focuses on providing safe and efficient subway transportation services and seeks to improve operational efficiency and equipment maintenance levels through technological innovation.
Business Challenges

Regular inspections of subway shield doors require a lot of time and resources.

 

Traditional inspection methods may lack specificity and cannot accurately identify potential problems with shield doors.

 

There is a need to improve the operational efficiency and predictive maintenance capabilities of subway shield doors.

Solution

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.

Solution Advantages
  • Technology Integration: Combine machine learning technology with industrial control equipment data to achieve data-driven intelligent analysis.
  • Intelligent Assessment: Use advanced data processing technology for accurate assessment of the health of shield doors.
  • Dynamic Monitoring: Real-time collection of DCU sensor data to achieve dynamic monitoring of the status of shield doors.
Client Value
Improve the Targeting of Inspections:

Accurately identify shield doors that require maintenance through intelligent assessment, improving inspection efficiency.

Reduce Inspection Frequency:

Reduce unnecessary inspections and lower operational costs.

Improve Work Efficiency:

Improve the work efficiency of the subway operation company through automated data analysis and reporting.

Predictive Maintenance:

Achieve predictive maintenance and reduce unexpected failures through health scoring and trend analysis.

Stock Code：000034.SZ
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