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...
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,...
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...
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...
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...