Nowadays dc distribution is gaining importance, as an increasing amount of loads is natively biased in dc. In addition, dc micro-grids are usually characterized by high efficiency and simple design. Moreover, they can better accommodate renewable generation and storage systems. These are the main benefits attributed to dc distribution grids. Reliability aspects, usually, are not sufficiently taken into account.
This paper describes an initial methodological approach to compare (in both analytic and numerical way) ac and dc micro-grids. In particular, the analysis has been focused on the effects on loads of main supply interruptions. The performed analysis showed that dc micro- grids have a higher immunity to these events than that displayed by ac micro-grids. On the other hand, the front-end converter (FEC) interfacing ac main supply and dc micro-grid is a single point failure for the whole system. The paper describes (and analyses) some technical solutions to reduce the effect of FEC failure on loads. Moreover, the signals and alarms that should be used in order to maximize the obtained quality-of-service levels are reported, along with some diagnostic considerations.

This paper describes a method for the uncertainty-based combination of signal processing techniques for the identification of rotor imbalance. The main idea of the proposed method is to compute the imbalance with different algorithms and to average their results. The method is based on the data fusion at feature level and uses the measurement uncertainty of the imbalance as a figure of merit for the weight computation. A static and a dynamic implementation are presented. In the static one, the weights are computed in a dedicated training phase, in which four algorithms (Fourier transform and quasi-harmonic fitting of signal denoised with Hilbert-Huang Transform, Hilbert Vibration decomposition, and Wavelet Packet decomposition) have been used to estimate the known imbalance of car wheels. In the dynamic one, the weights are computed at runtime by estimating the difference between each predictor and the actual signal. Experimental results evidenced the validity of the proposed method, with uncertainty reductions between 10 and 40%, with larger benefits in presence of localized disturbances.

In this work, a methodological approach based on the evaluation of the measurement uncertainty is used, in order to face the issues connected to the validation of measurements of distributed measuring systems, taking into account the requirements set by a scenario of high connectivity and big data. The methodology provided involves and follows from the rigorous methods applied in metrology discipline. It focuses on the measurement uncertainty evaluation, to be used as an innovative tool for better knowing the underlying systems/subsystems - from time to time analysed, towards problem-solving, setting or directing some improvement actions and thus, towards the possibility of being more confident in reaching the targets that have been set.

This paper addresses the problem of estimating uncertainty when recovering the basic modal parameters of a given mechanical system using a generic vision based displacement measurement system. The particular nature of photogrammetry makes the calculation of uncertainty a challenging task because of the complex phenomena that occur during image formation (motion blur, optical aberration, etc.). To overcome this problem, a Monte Carlo simulation of the whole measurement process (including structure dynamics and its impact on image formation) is proposed. In this way, it is possible to retrieve the posterior probability density functions (PDFs) for the identification of main modal parameters (resonant frequencies and amplitudes). Eventually the proposed probabilistic framework is a suitable tool to assess the performance of a vision based monitoring system on the design stage.