Hitachi > Case Studies > Predictive maintenance of medical devices based on years of experience and advan

Predictive maintenance of medical devices based on years of experience and advan

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Technology Category
  • Analytics & Modeling - Machine Learning
  • Analytics & Modeling - Predictive Analytics
Applicable Industries
  • Healthcare & Hospitals
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Challenge

Failure prediction by human operators requires advanced skills, and the limited number of experts cannot monitor all MRI systems around the world. "Corrective maintenance" for repairs after breakdowns has also become inevitable.

The Customer
Kashiwa Health Check Clinic
About The Customer
Kashiwa Health Check Clinic in Chiba, Japan
The Solution

Hitachi analyzed three years’ worth of sensor data from 100 MRI systems and created a mechanism to investigate the cause patterns that lead to device failures. Then machine learning was used to define a normal operational state to achieve successful early detection of abnormalities and changes in status that lead to failures.

Operational Impact
  • [Efficiency Improvement - OEE]

    Schedule maintenance before systems break down has been made possible

  • [Cost Reduction - Maintenance]

    Improve medical services and reduce costs for hospitals

Quantitative Benefit
  • Equipment downtime has been reduced by 16.3% compared to before its introduction

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