18260385. Method and Apparatus for Identifying an Abnormality in Mechanical Apparatus or Mechanical Component simplified abstract (Robert Bosch GmbH)
Method and Apparatus for Identifying an Abnormality in Mechanical Apparatus or Mechanical Component
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Method and Apparatus for Identifying an Abnormality in Mechanical Apparatus or Mechanical Component - A simplified explanation of the abstract
This abstract first appeared for US patent application 18260385 titled 'Method and Apparatus for Identifying an Abnormality in Mechanical Apparatus or Mechanical Component
Simplified Explanation
The patent application describes a method for identifying abnormalities in mechanical apparatus or components using undersampled measurement data and machine learning.
- Acquiring at least two classes of undersampled measurement data that are different from each other in terms of delay relative to trigger event occurrence time and sampling frequency.
- Using an abnormality identification model based on machine learning to identify abnormalities in the mechanical apparatus or component.
- Providing a cost-effective and reliable fault diagnosis or predictive maintenance solution.
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- Potential Applications
- Predictive maintenance in industrial machinery.
- Fault diagnosis in automotive systems.
- Monitoring of rotating equipment in manufacturing plants.
- Problems Solved
- Early detection of abnormalities in mechanical systems.
- Reduction of unexpected downtime.
- Improved efficiency in maintenance operations.
- Benefits
- Cost-effective fault diagnosis.
- Reliable predictive maintenance.
- Increased lifespan of mechanical equipment.
Original Abstract Submitted
A method for identifying an abnormality in a mechanical apparatus or mechanical component includes: i) acquiring at least two classes of undersampled measurement data collected in or on a mechanical apparatus or mechanical component, all of the at least two classes of undersampled measurement data being different from one another in either one of or both of the following aspects: delay relative to occurrence time of a trigger event, and sampling frequency; and ii) based on the at least two classes of undersampled measurement data acquired, using an abnormality identification model to identify an abnormality in the mechanical apparatus or mechanical component, the abnormality identification model being based on machine learning and used for identifying an abnormality in the mechanical apparatus or mechanical component. Also disclosed is a method for training an abnormality identification model based on machine learning, a computer apparatus, a computer program product, and a detection apparatus. The above provides a fault diagnosis or predictive maintenance solution which is cost-effective and gives reliable results.
