@article{BraunReuterRudolph,
author = {Braun, Tristan and Reuter, Johannes and Rudolph, Joachim},
title = {Observer Design for Self-Sensing of Solenoid Actuators With Application to Soft Landing},
series = {IEEE Transactions on Control Systems Technology},
number = {Published online: 10 Apr 2018},
issn = {1558-0865},
doi = {10.1109/TCST.2018.2821656},
pages = {8},
abstract = {A constructive nonlinear observer design for self-sensing of digital (ON/OFF) single coil electromagnetic actuators is studied. Self-sensing in this context means that solely the available energizing signals, i.e., coil current and driving voltage are used to estimate the position and velocity trajectories of the moving plunger. A nonlinear sliding mode observer is considered, where the stability of the reduced error dynamics is analyzed by the equivalent control method. No simplifications are made regarding magnetic saturation and eddy currents in the underlying dynamical model. The observer gains are constructed by taking into account some generic properties of the systems nonlinearities. Two possible choices of the observer gains are discussed. Furthermore, an observer-based tracking control scheme to achieve sensorless soft landing is considered and its closed-loop stability is studied. Experimental results for observer-based soft landing of a fast-switching solenoid valve under dry conditions are presented to demonstrate the usefulness of the approach.},
language = {en}
}
@inproceedings{KnoebelStrommengerReuteretal.,
author = {Kn{\"o}bel, Christian and Strommenger, Daniel and Reuter, Johannes and G{\"u}hmann, Clemens},
title = {Health index generation based on compressed sensing and logistic regression for remaining useful life prediction},
series = {PHM 2019 : Proceedings of the Annual Conference of the PHM Society 2019, September 23-26, Scottsdale, Arizona, USA},
volume = {11},
number = {1},
isbn = {978-1-936263-29-5},
doi = {10.36001/phmconf.2019.v11i1.867},
pages = {1 -- 7},
abstract = {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.},
language = {en}
}