Pose Estimation and Damage Characterization of Turbine Blades during Inspection Cycles and Component-Protective Disassembly Processes.

borescopic fringe projection feature segmentation film-cooling holes point cloud registration pose estimation

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
11 Jul 2022
Historique:
received: 02 06 2022
revised: 05 07 2022
accepted: 07 07 2022
entrez: 27 7 2022
pubmed: 28 7 2022
medline: 28 7 2022
Statut: epublish

Résumé

Inspection in confined spaces and difficult-to-access machines is a challenging quality assurance task and particularly difficult to quantify and automate. Using the example of aero engine inspection, an approach for the high-precision inspection of movable turbine blades in confined spaces will be demonstrated. To assess the condition and damages of turbine blades, a borescopic inspection approach in which the pose of the turbine blades is estimated on the basis of measured point clouds is presented. By means of a feature extraction approach, film-cooling holes are identified and used to pre-align the measured point clouds to a reference geometry. Based on the segmented features of the measurement and reference geometry a RANSAC-based feature matching is applied, and a multi-stage registration process is performed. Subsequently, an initial damage assessment of the turbine blades is derived, and engine disassembly decisions can be assisted by metric geometry deviations. During engine disassembly, the blade root is exposed to high disassembly forces, which can damage the blade root and is crucial for possible repair. To check for dismantling damage, a fast inspection of the blade root is executed using the borescopic sensor.

Identifiants

pubmed: 35890871
pii: s22145191
doi: 10.3390/s22145191
pmc: PMC9316098
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Deutsche Forschungsgemeinschaft
ID : SFB 871/3 - 119193472

Références

Sensors (Basel). 2019 May 05;19(9):
pubmed: 31060318
Sensors (Basel). 2021 Mar 30;21(7):
pubmed: 33808238
Appl Opt. 2021 Jun 10;60(17):5124-5133
pubmed: 34143079

Auteurs

Philipp Middendorf (P)

Institute of Measurement and Automatic Control, An der Universität 1, 30823 Garbsen, Germany.

Richard Blümel (R)

Institute of Assembly Technology, 30823 Garbsen, Germany.

Lennart Hinz (L)

Institute of Measurement and Automatic Control, An der Universität 1, 30823 Garbsen, Germany.

Annika Raatz (A)

Institute of Assembly Technology, 30823 Garbsen, Germany.

Markus Kästner (M)

Institute of Measurement and Automatic Control, An der Universität 1, 30823 Garbsen, Germany.

Eduard Reithmeier (E)

Institute of Measurement and Automatic Control, An der Universität 1, 30823 Garbsen, Germany.

Classifications MeSH