Critical infrastructure (CI) modeling and analysis is a very challenging research topic. One of the most pressing issues is to find an effective representation for addressing the system vulnerabilities caused by interdependencies, which, if exploited, could result in nontrivial accident scenarios. Until now, this question has been tackled for different sector-specific infrastructures (electricity grid, telecommunications networks, supply chains, etc.), and very few generalizable analysis tools have been developed. However, all CI share some features that can be leveraged in order to build a common modeling framework. This paper identifies these common features, which it exploits to develop a modeling language: the infrastructure resilience-oriented modeling language (I®ML). I®ML is designed to facilitate the analysis of operational interdependencies among the infrastructure components and overall resilience, i.e., the ability of the infrastructure to withstand and recover under off-nominal (anomalous) conditions. A number of examples are used to illustrate the modeling concepts and highlight the analytical capability of I®ML.

In this paper we introduce an analysis of dependability of an elementary yet critical component of robotic systems designed to operate in environments shared with humans, i.e., the joint-level actuation system. We consider robot joints that implement the variable impedance actuation (VIA) paradigm. The VIA has been demonstrated to be an effective mean to achieve high performance while constantly keeping injury risks to humans by accidental impacts below a given threshold. The paper describe possible implementations of the VIA concept which use the Antagonistic Actuation (AA) in three different arrangements. This study follows a previously reported paper dealing with safety. Here a detailed comparative dependability and performability analysis in front of possible specific failure modes is conducted, whose results provide additional and useful guidelines for design of safe and dependable actuation systems for physical human-robot interaction.

This paper describes a way of managing the modeling and analysis of Scheduled Maintenance Systems (SMSs) within an analytically tractable context. We chose a significant case study having a variety of interesting features like a heavily redundant architecture and a test and maintenance policy whose execution is made on-line without halting the system. We applied a methodology we previously developed based on the Deterministic Stochastic Petri Net (DSPN) approach, where the underlying stochastic process is Markov regenerative (MRGP) solved in our setting using an efficient analytical solution method. This methodology is implemented by the DEEM tool specifically developed for modeling and evaluating the dependability of Phased Mission Systems (PMSs). We test our methodology with such a case study to check whether it can master real and complex SMS problems and to compare its efficacy with traditional approaches (fault trees). The paper also investigates the problem of the optimal tuning of a maintenance program, giving a useful decision support tool for evaluating the system performance from the early design stage.

Multiple-phased systems, whose operational life can be partitioned into a set of disjoint periods called "phases", include several classes of systems, such as phased mission systems and scheduled maintenance systems. Because of their deployment in critical applications, the dependability modeling and analysis of multiple-phased systems is a task of primary relevance. However, the phased behavior makes the analysis of multiple-phased systems extremely complex. This paper is centered on the description and application of DEEM, a dependability modeling and evaluation tool for multiple-phased systems. DEEM supports a powerful and efficient methodology for the analytical dependability modeling and evaluation of multiple-phased systems, based on deterministic and stochastic Petri nets and on Markov regenerative processes.