Skip to Main content Skip to Navigation
Conference papers

Wear-oriented state-of-health calculation and classification using operating data

Abstract : Reliability and availability of technical complex and safety-critical systems are of increasing importance. The degree of wear as well as the quality of mechatronic systems are significant for the system reliability. To classify the machines state using easy-to-measure signals, two issues are important: filtering and interpretation of the data [1]. Core of this contribution is the development and first application of a simple, easy to use, easy to apply, and easy to handle algorithm to be used directly with industrial data or measurements from technical systems during operation. In this contribution a hydraulically driven machine part sliding over another is used as example. A connection between measured hydraulic data to the degree of wear of the lubricated surface is established to calculate information about the state of a sliding surface. As experimental data the time behavior of hydraulic pressure data is taken and filtered for better evaluation. To the further generation of suitably defined characteristics, the data are edited and analyzed. The results based on four measurements with two different operating conditions show that the developed approach allows a detailed judgment of wear-oriented state of health as part of a new structural health monitoring system.
Complete list of metadata

Cited literature [7 references]  Display  Hide  Download

https://hal.inria.fr/hal-01021231
Contributor : Anne Jaigu <>
Submitted on : Wednesday, July 9, 2014 - 10:24:40 AM
Last modification on : Tuesday, March 20, 2018 - 2:48:45 PM
Long-term archiving on: : Thursday, October 9, 2014 - 11:27:42 AM

File

0041.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01021231, version 1

Collections

Citation

Sandra Rothe, Dirk Söffker. Wear-oriented state-of-health calculation and classification using operating data. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01021231⟩

Share

Metrics

Record views

1454

Files downloads

1138