Lennart Willms
Railway technology
Exploiting potential from unused data – a use case for automatic defect detection and predictive maintenance has been validated with our Data Lab Framework. This support puts our customer on the fast track to smart maintenance.
A previously implemented remote monitoring tool for train door systems retrieves diagnostic messages. Based on this data, evaluations, data extraction and visualizations are created. The smart assessment of millions of historical data points goes to make maintenance and scheduling more efficient and provide the underpinning for predictive maintenance.
The ITK Data Lab served to quickly pinpoint and specify the potential in the data pipeline. The team conducted an exploratory analysis and formulated hypotheses. It takes the business impact into the equation to identify several use cases, two being the defect detection tool and predictive maintenance.
Use case: A defect detection tool
Using data sourced from train doors markedly increased the defect detection rate and the doors’ uptime. The ability to automatically detect defects makes scheduling maintenance easier. This primarily increased the availability of the doors. In addition, the automatic detection of defects improved the predictability of maintenance for operators.
Buzzwords such as predictive maintenance and condition-based maintenance have been circulating in the railway sector for some time now. But few use cases have been put into practice. We can quickly assess data with our Data Lab framework and are already supporting our customers on their path to smart maintenance.
Lennart Willms, Business Development
The pre-developed Data Lab uses artificial intelligence for initial analyses to rapidly pinpoint potential on the path to smart maintenance. Its modular structure and highly automated routines deliver fast results with short runtimes and at low cost. In addition, work was carried out according to a tried-and-tested structured procedure, so that value is added as quickly as possible.
