ENBIS Spring Meeting 2026

Session

Next-Gen Session

29 May 2026, 15:30

Conveners

Next-Gen Session

• Antonio Piscopo (Scuola Superiore Meridionale; Université d'Angers)

11. A Finite-Time Prescriptive Maintenance Policy for Systems Exhibiting Bathtub-Shaped Degradation Rates

Antonio Piscopo (Scuola Superiore Meridionale; Université d'Angers)

29/05/2026, 15:35

Spring Meeting

Real-world degradation processes often exhibit three distinct phases, namely a running-in (accommodation) phase, a steady-state (normal) phase, and a catastrophic wear phase. As a consequence, the degradation rate function, here intended as the first derivative of the mean degradation function, is typically bathtub-shaped: it decreases during the first phase, remains approximately constant...

14. Connections between information measures and activation functions

Giulia Pisano (Università degli Studi di Salerno)

29/05/2026, 15:55

Spring Meeting

In this work, we introduce a generalized measure of uncertainty, namely the cumulative information $\psi$-measure, in order to provide a unified perspective to the uncertainty framework. Indeed, it is a variability measure which reduces to several well-known information measures for appropriate choices of the function $\psi$. In particular, cumulative versions of Shannon and Tsallis entropies,...

19. Bayesian Hierarchical Modeling for Reliable Large-Scale Sensors Deployments and Applications

Anna Ballario (Politecnico di Torino)

29/05/2026, 16:15

Spring Meeting

Reliability of large populations of sensors is a major challenge in modern industrial production and applied functional monitoring systems. The massive deployment of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors across several technological domains requires calibration strategies that ensure metrological reliability while remaining feasible at industrial scale. However, traditional...

27. Implementation of Automatic differentation on Maintenance optimization problems

Alberto Arias (University of Extremadura)

29/05/2026, 16:35

Spring Meeting

Abstract

Automatic Differentiation (autodiff or AD) is a technique for computing derivatives that differs from both symbolic and numerical differentiation. It relies on the implementation of the chain rule to functions composed of simpler smooth functions whose Jacobian matrices are known.

Given the implementation of a function composed of smooth operations, Automatic...

29. A Piecewise Hidden Markov Model for Degradation and Maintenance

Demian Benjamin Stuursma (Universidad de Granada)

29/05/2026, 16:55

Spring Meeting

Hidden Markov Models (HMMs) are increasingly recognized in reliability engineering as valuable tools for monitoring systems where the true operational state is not directly observable and must be inferred from certain indicators provided by a control system. Accurate estimation of these hidden health states and prediction of failures are crucial for minimizing unexpected downtime and...