TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Knöbel, Christian
A1  - Strommenger, Daniel
A1  - Reuter, Johannes
A1  - Gühmann, Clemens
T1  - Health index generation based on compressed sensing and logistic regression for remaining useful life prediction
T2  - PHM 2019 : Proceedings of the Annual Conference of the PHM Society 2019, September 23-26, Scottsdale, Arizona, USA
N2  - Extracting suitable features from acquired data to accurately depict the current health state of a system is crucial in data driven condition monitoring and prediction. Usually, analogue sensor data is sampled at rates far exceeding the Nyquist-rate containing substantial amounts of redundancies and noise, imposing high computational loads due to the subsequent and necessary feature processing chain (generation, dimensionality reduction, rating and selection). To overcome these problems, Compressed Sensing can be used to sample directly to a compressed space, provided the signal at hand and the employed compression/measurement system meet certain criteria. Theory states, that during this compression step enough information is conserved, such that a reconstruction of the original signal is possible with high probability. The proposed approach however does not rely on reconstructed data for condition monitoring purposes, but uses directly the compressed signal representation as feature vector. It is hence assumed that enough information is conveyed by the compression for condition monitoring purposes. To fuse the compressed coefficients into one health index that can be used as input for remaining useful life prediction algorithms and is limited to a reasonable range between 1 and 0, a logistic regression approach is used. Run-to-failure data of three translational electromagnetic actuators is used to demonstrate the health index generation procedure. A comparison to the time domain ground truth signals obtained from Nyquist sampled coil current measurements shows reasonable agreement. I.e. underlying wear-out phenomena can be reproduced by the proposed approach enabling further investigation of the application of prognostic methods.
KW  - Fault diagnosis
KW  - Preventive maintenance
KW  - Actuators
KW  - Data compression algorithms
Y1  - 2019
UR  - https://phmpapers.org/index.php/phmconf/article/view/867
SN  - 978-1-936263-29-5
SB  - 978-1-936263-29-5
U6  - https://doi.org/10.36001/phmconf.2019.v11i1.867
DO  - https://doi.org/10.36001/phmconf.2019.v11i1.867
VL  - 11
IS  - 1
SP  - 1
EP  - 7
ER  -