*5.1. Implementation Targets*

As stated in Section 4, the proposed PHM system works off-line with respect to the iron bird, analyzing data periodically; thus, no specific targets are set in terms of execution time of the algorithms. However, since the iron bird is meant to act as a technological demonstrator, it is useful to check whether the proposed algorithms are suitable for realtime applications. For this purpose, the PHM system employed in the GUI is embedded with a tool monitoring the computational performances of the PHM routines. Monitored activities include the execution time of the fault-diagnosis algorithm and of the failureprognosis routine with its subroutine. Test conditions include the analysis of signals coming from three identical electromechanical actuators, two operating on a morphing winglet and one on the wingtip surface, for a total of 24 signals, sampled at 800 Hz, and 19 features, and downsampled at 80 Hz. The analysis was performed on an Intel Core i9-9880H CPU running at 2.30 GHz with 16 GB of DDR4 RAM, which was compatible with the performances of the HMSM and was conducted considering 200 degradation patterns, each comprehensive of a number ranging between 150 and 400 position/load sequences, which correspond to 25 degradation patterns for the seven considered faults and 25 simulation cycles performed in healthy conditions. The fault-detection algorithm completed the analysis of one data batch corresponding to advancement of a single time step in 11.3 ms. Such a result is compatible with the features sampling (80 Hz), hence suggesting that the feature extraction and fault-detection scheme is suitable for real-time applications. The same simulation cycles were also used to assess the computational performance of the long-term prognosis algorithm. As depicted in Figure 21, a single cycle of prediction and subsequent state updates of the particle-filtering routine requires 7.4 ms on the employed test machine, with the RLS algorithms responsible for the tuning of the degradation model accounting for the 2.25% of such a number. Such results were obtained for a particle-filtering scheme operating with 200 particles and are expected to scale almost linearly, increasing the number of particles.

**Figure 21.** Elapsed time breakdown—average execution time of a particle-filtering step.
