Software-based condition monitoring for machines

By receiving the machine's technical specifications and current operating parameters, continuously exporting and integrating the current load using a knowledge base, the current status of the machine and the optimal maintenance time are predicted. This solves the problem of improper maintenance caused by static maintenance intervals, improves machine availability and productivity, and reduces costs and errors.

CN115885468BActive Publication Date: 2026-06-30SIEMENS AG
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SIEMENS AG
Filing Date
2021-08-05
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

In existing technologies, the maintenance intervals of machines are designed as static values, which leads to maintenance being done too early or too late, affecting the machine's condition and lifespan. Furthermore, external sensor monitoring systems increase costs and errors.

Method used

By receiving the machine's technical specifications and current operating parameters, and continuously exporting and integrating the current load using a knowledge base, the machine's current condition and optimal maintenance time can be predicted, thus avoiding the use of external sensors.

Benefits of technology

This technology enables dynamic adjustment of maintenance time based on the actual load of the machine, improving machine availability and productivity, reducing maintenance costs and errors, and enhancing system robustness.

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Abstract

This invention relates to a computer-implemented method for predicting machine status, and a corresponding data processing system, computer program, and computer-readable medium. The method involves receiving the machine's technical specifications. It continuously receives a dataset including at least one current operating parameter of the machine. Based on the provided technical specifications and the received dataset, the method continuously derives the machine's current load via a knowledge base. The method continuously predicts the machine's current status by integrating the derived current load with all previously derived current loads.
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