This paper focuses on the development of a physics-based diagnostic tool for alternating current (AC) solenoid valves which are categorized as critical components of many machines used in the process industry. Signal processing and machine learning based approaches have been proposed in the literature to diagnose the health state of solenoid valves. However, the approaches do not give a physical explanation of the failure modes. In this work, being capable of diagnosing failure modes while using a physically interpretable model is proposed. Feature attribution methods are applied to CNN on a large data set of the current signals acquired from accelerated life tests of several AC solenoid valves. The results reveal important regions of interest on current signals that guide the modeling of the main missing component of an existing physical model. Two model parameters, which are the shading ring and kinetic coulomb forces, are then identified using current measurements along the lifetime of valves. Consistent trends are found for both parameters allowing to diagnose the failure modes of the solenoid valves. Future work will consist of not only diagnosing the failure modes, but also of predicting the remaining useful life.

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This is an author-deposited version published in: https://sam.ensam.eu Handle IDThe paper reassesses the mechanism of biodynamical feedthrough coupling to helicopter body motion in lateral-roll helicopter tasks. An analytical bio-aeroelastic pilot-vehicle model is first developed and tested for various pilot's neuromuscular adaptions in the lateral/roll axis helicopter tasks. The results demonstrate that pilot can destabilize the low-frequency regressing lead-lag rotor mode; however he/she is destabilizing also the high-frequency advancing lag rotor mode. The mechanism of pilot destabilization involves three vicious energy circles, i.e. lateral-roll, flap-roll and flap-lag motions, in a very similar manner as in the air resonance phenomenon. For both modes, the destabilization is very sensitive to an increase of the steady state rotor coning angle that increases the energy transfers from flap to lag motion through Coriolis forces. The analytical linear time-invariant model developed in this paper can be also used to investigate designs proneness to lateral/roll aeroelastic rotorcraft-pilot couplings.

Recently, designing mechatronic systems has become more and more dependent on models that are used to predict performance in a virtual environment, and the models involved are becoming increasingly more complex multiphysical systems. Instead of spending much time modeling increasingly detailed physical models, uncertainties can be explicitly considered to model the lack of knowledge. The mismatch between real-life experiments and model simulations due to parametric uncertainties can be quantified using likelihood estimation and Monte Carlo sampling techniques for propagation. In this paper, we attempt to significantly accelerate the process using polynomial chaos expansions for propagation and a genetic algorithm to maximize likelihood. The soundness of this approach is demonstrated on a wet friction clutch system. The results show that the method has a strong potential for scalability with respect to the number of uncertain parameters.