Data set for "Data-based Detection and Diagnosis of Faults in Linear Actuators"
Date
2018-03-07 08:40
Authors
Supervisor/s
Journal Title
Journal ISSN
Volume Title
Publisher
Cranfield University
Department
Type
Dataset
ISSN
Format
Free to read from
Citation
Ruiz Carcel, Cristobal; Starr, Andrew (2018). Data set for "Data-based Detection and Diagnosis of Faults in Linear Actuators". Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.5097649
Abstract
This data set presents the raw original data used in "Data-based detection & diagnosis of faults in Linear actuators".The data was acquired from a linear actuator rig operated using different loading conditions and motion profiles. In addition, three different faults (lack of lubrication, spalling and backlash) were gradually seeded to the system in order to study fault detection and diagnosis capabilities of different algorithms. The data set includes actuator position and motor current measurements for the different conditions mentioned. In addition to the data, the file "Data description.pdf" contains all the details about the test rig set up, cases studied and data structure.
Description
Software Description
Software Language
Github
Keywords
'fault detection', 'diagnosis', 'linear actuator', 'EMA', 'feature extraction', 'multivariate analysis', 'Automation and Control Engineering', 'Dynamics, Vibration and Vibration Control', 'Manufacturing Processes and Technologies (excl. Textiles)', 'Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)', 'Mechanical Engineering', 'Signal Processing'
DOI
10.17862/cranfield.rd.5097649
Rights
CC BY 4.0
Relationships
Relationships
Supplements
Funder/s
This research was funded by the Engineering and Physical Sciences Research Council (EPSRC) Centre for Through-life Engineering Services, Rolls-Royce, BAE Systems, Bombardier Transportation, Babcock and the Ministry of Defence.