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In the automotive industry a strong effort has been undertaken to reduce the weight of modern vehicles. In order to reduce the energy consumption and to improve the environmental sustainability, the importance of weight reduction activities is even growing faster. As lightweight designing is becoming more and more expensive and show less potential savings, new approaches are needed. One promising technology could be the use of shape memory elements. In the last years a lot of potential application possibilities were presented, demonstrating the benefit of these functional elements in automotive design solutions: they often reduce complexity, weight and design space of an actuation device and enable new functions. In addition they work silently and are therefore ideally suitable for comfort applications in the passenger cabin. Because of the current trend to electric vehicle the hitherto existing drawback of a high electrical energy consumption of shape memory actuators in some design proposals is not given any more.
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.