Customer Cases

Industry

Transportation

Product

DeepFlow

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Optimize operations, cut disruptions and costs

Case Description

In the traditional maintenance model, Taoyuan MRT Company faces the problem of shutdown in order to detect and excessive or insufficient maintenance. In order to solve these problems, it is hoped to obtain status data of equipment during operation and monitor mechanical equipment through various measurement methods to optimize the equipment maintenance process, reduce the scope of maintenance, save maintenance costs, reduce maintenance workload, and provide equipment reliability and Utilization rate.

In cooperation with Taoyuan MRT company, maintenance projects are evaluated based on performance indicators and AI assistance, and abnormal conditions are detected early to determine the time for replacement, reducing equipment shutdowns without warning, reducing regular maintenance costs, and preventing unnecessary disassembly and lowering of machinery. Precision, extended equipment life

Solution

Through the predictive maintenance artificial intelligence system, predict the best time when the vehicle or carriage body needs maintenance.
The predictive maintenance artificial intelligence system uses the DeepFlow product independently developed by InvnetAI and is based on K8s. The functional modules cover the model life cycle (preparation, training, deployment, inference, monitoring, optimization, etc.) to build the ability to self-model and maintain models in the future.

Benefit

Reduce losses caused by unwarranted shutdowns and problems affecting transportation caused by driving interruptions
Improve equipment reliability and availability through data acquisition and AI prediction application introduction