Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes and services. However, the conventional FMEA method has been criticized for some inherent drawbacks which limit its effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members' weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison with some existing methods.

A system is said to be opaque if a given secret behavior remains opaque (uncertain) to an intruder who can partially observe system activities. This work addresses the verification of state-based opacity in systems modeled with Petri nets. The secret behavior of a system is defined as a set of states. More precisely, two state-based opacity properties are considered: current-state opacity and initial-state opacity. We show that both current-state and initial-state opacity problems in bounded Petri nets can be efficiently solved by using a compact representation of the reachability graph, called basis reachability graph (BRG). This approach is practically efficient since the exhaustive enumeration of the reachability space can be avoided.

A novel risk analysis approach is developed by merging interval-valued fuzzy sets, improved Dempster-Shafer (D-S) evidence theory and fuzzy Bayesian networks, acting as a systematic decision support approach for safety insurance on the entire life cycle of a complex system under uncertainty. Aiming to alleviate the problem of insufficient and imprecise data collected from the complicated environment, the expert judgment in linguistic expressions is employed to describe risk levels for all risk factors, which are represented by interval-valued fuzzy sets using Gaussian membership function to fully consider such fuzziness and uncertainty. In regard to interval fusion and highly conflicting data, an improved combination rule based on D-S evidence theory is developed. Then, fuzzy prior probability for each risk factor can be generated from fused intervals and fed into a fuzzy Bayesian network model for fuzzy-based Bayesian inference, including predictive, sensitivity and diagnosis analysis. Furthermore, a case study is used to demonstrate the feasibility of the proposed risk analysis. A comparison of risk analysis based upon the hybrid improved D-S, classical D-S and arithmetic average method is illustrated to show the outstanding performance of the developed approach in fusing multi-source information with ubiquitous uncertainty and conflicts in an efficient manner, leading to more reliable risk evaluation. It is concluded that the proposed risk analysis provides a deep insight on risk control especially for complex project environment, which enables to not only reduce the likelihood of failure ahead of time, but also mitigate risk magnitudes to some degree after the occurrence of a failure.