PurposeThe purpose of this paper is to present a new disruptive maintenance model based on new technologies.Design/methodology/approachThe approach is carrying out through the impact of the Industry 4.0, Internet of things, big data, virtual reality and additive manufacturing on maintenance.FindingsThe findings are that new technologies are an evolutionary challenge that is immediately affecting maintenance engineering. It presents a unique opportunity to make a disruptive evolution of maintenance.Research limitations/implicationsThe correct development of Maintenance 4.0 relates to the correct implementation of Industry 4.0.Practical implicationsMaintenance 4.0 will greatly improve the main operating indicators: safety, reliability, availability and cost.Social implicationsMaintenance 4.0 will contribute to a circular and sustainable economy.Originality/valueFor the first time, a complete new Maintenance Engineering 4.0 model is proposed. The application of the new technologies appears in each specific maintenance process of the product life cycle.

Purpose The purpose of this paper is to present the results of the application of various models to estimate the reliability in railway repairable systems. Design/methodology/approach The methodology proposed by the International Electrotechnical Commission (IEC), using homogeneous Poisson process (HPP) and non-homogeneous Poisson process (NHPP) models, is adopted. Additionally, renewal process (RP) models, not covered by the IEC, are used, with a complementary analysis to characterize the failure intensity thereby obtained. Findings The findings show the impact of the recurrent failures in the times between failures (TBF) for rejection of the HPP and NHPP models. For systems not exhibiting a trend, RP models are presented, with TBF described by three-parameter lognormal or generalized logistic distributions, together with a methodology for generating clusters. Research limitations/implications For those systems that do not exhibit a trend, TBF is assumed to be independent and identically distributed (i.i.d.), and therefore, RP models of “perfect repair” have to be used. Practical implications Maintenance managers must refocus their efforts to study the reliability of individual repairable systems and their recurrent failures, instead of collections, in order to customize maintenance to the needs of each system. Originality/value The stochastic process models were applied for the first time to electric traction systems in 23 trains and to 40 escalators with ten years of operating data in a railway company. A practical application of the IEC models is presented for the first time.

No abstract

No abstract

Energy storage in an uninterruptible power supply (UPS) is one of the most frequent applications of batteries. This can be found in hospitals, communication centers, public centers, ships, trains, etc. Most frequent industrial methods for battery state-of health estimation require a technician to move to the battery's location and, in some cases, require the use of heavy equipment and disconnection of the battery from the UPS. For example, in railway applications, trains must stop at the maintenance depot producing significant total costs. This article proposes a new method to assess a battery's health by measuring the battery's internal resistance, based on the measurement of its voltage ripple in response to the current ripple imposed by the charger which in most UPS applications is permanently connected to the battery. Unlike most traditional methods, this system makes it possible a continuous on-line and on-board monitoring, and, therefore, it eases condition-based maintenance (CBM). To verify its viability, a low cost measuring prototype has been built and measurements in a railway battery with its charger have been carried out.Energies 2019, 12, 2836 2 of 13 spent long periods of time floating without activity. Therefore, it is crucial to know aspects such as the process of battery degradation, how much energy they actually store, how much energy they will be able to provide, and how much time they have left before a failure in order to carry out an optimized preventive maintenance. These characteristics are summarized by the state of charge (SOC) and the state of health (SOH) of the battery [5-10].State-prediction techniques (prognostics and health management, PHM) have evolved in line with the knowledge of the internal operation of batteries, their chemical reactions and the process of degradation of their components. The existing methods to determine the state of health and the charge of a battery can be divided into direct methods and computational methods.Direct methods are based on tests, which usually require the intervention of a technician; that by direct measurements of a physical parameter of the battery can give directly or can estimate the battery state of charge SOC and state of health SOH. Some of these methods are: a.