Name
Abstract | ||
|---|---|---|
In this paper, a systematic approach for the automated training of condition monitoring systems for complex hydraulic systems is developed and evaluated. We analyzed different fault scenarios using a test rig that allows simulating a reversible degradation of component's conditions. By analyzing the correlation of features extracted from raw sensor data and the known fault characteristics of experimental obtained data, the most significant features specific to a fault case can be identified. These feature values are transferred to a lower-dimensional discriminant space using linear discriminant analysis (LDA) which allows the classification of fault condition and grade of severity. We successfully implemented and tested the system for a fixed working cycle of the hydraulic system. Furthermore, the classification rate for random load cycles was enhanced by a distribution analysis of feature trends. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/I2MTC.2015.7151267 | Instrumentation and Measurement Technology Conference |
Keywords | Field | DocType |
condition monitoring,feature extraction,hydraulic systems,LDA,complex hydraulic system,complex hydraulic systems,component reversible degradation,condition monitoring systems,distribution analysis,feature extraction,linear discriminant analysis,multivariate statistics,random load cycles,raw sensor data,systematic approach,condition monitoring,hydraulic system,linear discriminant analysis,multivariate statistics | Correlation coefficient,Hydraulic machinery,Pattern recognition,Discriminant,Multivariate statistics,Feature extraction,Correlation,Condition monitoring,Artificial intelligence,Engineering,Linear discriminant analysis | Conference |
Citations | PageRank | References |
9 | 0.65 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| nikolai helwig | 1 | 9 | 0.99 |
| Pignanelli, E. | 2 | 9 | 0.65 |
| Andreas Schütze | 3 | 15 | 3.57 |