In recent years, the model-based safety analysis (MBSA) has been developing continuously. The Functional Failure Identification and Propagation (FFIP) method is a graphics processing technology which supports the analysis of fault propagation paths before making costly design commitments. However, the traditional FFIP has some deficiencies. In this paper, we extend the functional failure logic (FFL) in the FFIP and introduce the concept of deviation. So, FFIP can be used to analyze the failure process of the systems and make the logical analysis of functional failure easier. Based on the extended FFL, we present a new overview of the FFIP. The FFIP is improved by using mathematical logic and Systems Modeling Language (SysML). The standard expression of FFL is realized, which is conducive to the subsequent modeling and modification. Additionally, we use the failure logic analysis in the FFIP to improve the state machine diagram (SMD) in SysML. Finally, the improved FFIP method is used to analyze the fault propagation paths of the system and Simulink is used for simulation. The fault tree is generated according to the simulation results, the minimum cut set is calculated, and the key failure parts of the system are obtained.

With the complexity of the socio-technical system, the requirement for safety analysis is growing. In actuality, system risk is frequently created by the interaction of numerous nonlinear-related components. It is essential to use safety assessment methods to identify critical risk factors in the system and evaluate the safety level of the system. An integrated safety assessment framework combining the system theoretic process analysis (STPA), the analytic network process (ANP) and system dynamics (SD) is suggested to analyze the safety level of socio-technical systems to achieve qualitative and quantitative safety evaluation. Our study constructs an STPA and SD integration framework to demonstrate the practical potential of combining STPA and SD approaches in terms of risk factors and causality. The framework uses the STPA method to define the static safety control structure of the system and analyzes the primary risk factors. The unsafe control actions (UCAs) from the STPA method are transformed into network layer elements of ANP. The ANP method is used to calculate the element weights, which are the impact coefficients between the system dynamics (SD) variables. The SD method is used to assess the safety level of the system. Finally, a specific coal mining system is used to demonstrate how the proposed hybrid framework works. The results indicated that the safety level of the system was low on days 38 and 120 of the simulation cycle (one quarter). Our work can overcome the limitations of conventional STPA quantitative analysis and simplify SD qualitative modeling to serve as a reference for complicated system safety/risk analysis work.