Patent · US Active

Supervised fault learning using rule-generated samples for machine condition monitoring

US8868985B2 · kind B2 · utility

5Cited by

3References

20Claims

0Family size

Assignee

Siemens Aktiengesellschaft · DE

Inventors

Holger Hackstein · Dietzenbach, DE
Claus Neubauer · Monmouth Junction, US
Chao Yuan · Plainsboro, US

Key dates

Filing date	Sep 13, 2010
Grant date	Oct 21, 2014
Priority date	—
Expiry date	Oct 15, 2031

Classification

Technology area (CPC G)Physics
CPC primaryG05B23/0283
WIPO fieldControl
WIPO sectorInstruments

Abstract

A machine fault diagnosis system is provided. The system combines a rule-based predictive maintenance strategy with a machine learning system. A simple set of rules defined manually by human experts is used to generate artificial training feature vectors to portray machine fault conditions for which only a few real data points are available. Those artificial training feature vectors are combined with real training feature vectors and the combined set is used to train a supervised pattern recognition algorithm such as support vector machines. The resulting decision boundary closely approximates the underlying real separation boundary between the fault and normal conditions.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.