With the rapid advancement of sensor and network technology, there has been a notable increase in the availability of condition-monitoring data such as vibration, temperature, pressure, voltage, and other electrical and mechanical parameters. With the introduction of big data, it is possible to prevent potential failures and estimate the remaining useful life of the equipment by developing advanced mathematical models and artificial intelligence (AI) techniques. These approaches allow taking maintenance actions quickly and appropriately. In this scenario, this paper presents a systematic literature review of statistical inference approaches, stochastic methods, and AI techniques for predictive maintenance in the automotive sector. It provides a summary on these approaches, their main results, challenges, and opportunities, and it supports new research works for vehicle predictive maintenance.

Hydrodynamical models for charge transport in graphene can be obtained as moment equations of the semiclassical Boltzmann equation in which the needed closure relations are obtained by resorting to the Maximum Entropy Principle (Jaynes 1957; Jou and Lebon 2010; Mascali and Romano 2005; Müller and Ruggeri, 1998). Several choices of the weight functions defining the moments can be made. The aim of this paper is analyzing the case in which the moments are expectation values of powers of the energy and a comparison is performed with the results given by directly solving the transport equation through the method in Coco et al. (2016) and Romano et al. (2015). It has been found out that adding new moments, representing further expectation values of powers of energy, with respect to those already considered in Camiola and Romano (2014) does not improve the accuracy of the model.