Each safety-related function must be implemented with a defined safety integrity level (SIL) if the control system implements safety-related functions (SFs) in addition to the standard control functions. The required SIL of the SF depends on the quantity of the risk associated with the failure of this one SF. The SIL against random failure can be expressed through the dangerous failure rate of the SF for an electronic safety-related control system (ESRCS) operating in a continuous mode of operation. The proof must be provided (among other things) that the SIL requirements for the individual SFs are met so the ESRCS can be accepted and implemented. The assessment of the impact of random failures on the SIL of the SF must be performed using the quantitative analysis method. This paper describes the procedure and derives equations for evaluating the impact of random failure on SIL of the SF using Markov chains with two absorption states. The achieved results are presented for SF implemented by ESRCS with dual architecture based on composite fail-safety technique.

The paper deals with a systematic approach to programming a program for safety PLC (Programmable Logic Controllers) based on the description of the required function by the UML (Unified Modeling Language) statechart. This procedure can be used to achieve systematic safety integrity of the control system with the safety PLC. Rhapsody tool is used as software support for UML and the application example is implemented on safety PLC Simatic.

Two-channel sensor connection to safety PLC is one of the most widely used sensor connection methods used in industrial applications to perform safety functions. The level of safety integrity of the considered safety function is influenced not only by the hardware connection of the sensors, but also by the parametrization of the safety PLC input module. It is the influence of parameterization on the safety integrity of the considered safety function that is often neglected in practice, which may result in the occurrence of an unwelcome event. This issue is addressed in this post.

The implementation of safety functions, in contrast to ordinary control functions, requires a different approach to the design of the hardware and software of the control system. The reason for the different approaches is that each safety function must meet the required Safety Integrity Level (SIL). This has two aspects: safety integrity against random failures and safety integrity against systematic failures. Hardware is primarily related to random failure safety integrity, and software primarily to systematic failure safety integrity. The focus of this contribution lies in the design of a method that will allow the software of a safety function for industrial applications to be designed using a model of the functional behavior of this function. Since commercially available programmable logic controllers (PLCs) with a defined SIL level (so-called safety PLC) do not support such solutions, a dual architecture based on standard PLCs, including their software control, is proposed in the paper. Such an approach will make it possible to significantly limit the occurrence of systematic errors in the creation of application software, as well as to test the created application software and reveal deficiencies that arose in previous phases of the life cycle (e.g., when specifying safety functions). To implement the method proposed in this paper, a dual architecture created from the safety PLC Simatic series S7-1500 is used. With the help of this architecture, the safety function “Muting” is implemented.

Response time is one of the most important parameter of safety functions for Safety Related Control System (SRCS) in time critical applications. During realization of this safety function for response time optimization, detailed knowledge of factors which have influence on response time is unavoidable. This contribution deals with analysis of measured response time reached by various SRCS architectures. These architectures are various in parameters settings, used hardware and used communication network. Final analysis shows influence of above described aspects on SF response time, what can be helpful for suitable architecture choose and parameters optimization when SF with SRCS is realized.