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This paper proposes the use of a change detection algorithm to monitor the degradation of mechanical components of Electro-Mechanical Actuators (EMA) employed in the aerospace industry. Contrary to the standard on-line application of change detection methods, the presented approach can be applied in a batch mode, leveraging on the knowledge of when the data were collected. The methodology is applied to data measured during an endurance test campaign on a real EMA employed in aerospace, by means of a developed test bench, progressively bringing the EMA to failure. Three rationales for building an indicator of degradation are tested. Results show how the method is able to assess the degradation of the actuator over time, constituting a first step towards a condition monitoring solution for the more-electric-aircraft of the future.

Preference-based optimization algorithms are iterative procedures that seek the optimal value for a decision variable based only on comparisons between couples of different samples. At each iteration, a human decision-maker is asked to express a preference between two samples, highlighting which one, if any, is better than the other. The optimization procedure must use the observed preferences to find the value of the decision variable that is most preferred by the human decision-maker, while also minimizing the number of comparisons. In this work, we propose GLISp-r, an extension of a recent preference-based optimization procedure called GLISp. The latter uses a Radial Basis Function surrogate to describe the tastes of the individual. Iteratively, GLISp proposes new samples to compare with the current best candidate by trading off exploitation of the surrogate model and exploration of the decision space. In GLISp-r, we propose a different criterion to use when looking for a new candidate sample that is inspired by MSRS, a popular procedure in the black-box optimization framework (which is closely related to the preference-based one). Compared to GLISp, GLISp-r is less likely to get stuck on local optimizers of the preference-based optimization problem. We motivate this claim theoretically, with a proof of convergence, and empirically, by comparing the performances of GLISp and GLISp-r on different benchmark optimization problems.

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This paper presents a health monitoring approach for Electro-Mechanical Actuators (EMA). We define four different indicators to continuously evaluate the health state of the system. The four indicators are computed by leveraging the output from a Statistical Process Monitoring (SPM) method based on multivariate statistics, such as the Hotelling's T 2 statistic and the Q statistic. SPM approaches give a dichotomous answer, i.e. the presence/absence of a fault. In this work, we propose four ways to compute a continuous indicator starting from the discrete SPM output, that is better suited for health monitoring. We test the approach using a dataset collected from a large experimental campaign on a 1:1 scale EMA for primary flight controls of small aircrafts, that led to EMA failure. Results show the effectiveness of the method.

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