The human factor is an essential aspect of the operability and safety of many technical systems. This paper focuses on the analysis of human errors in the railway domain. The subject of human reliability analysis is the behavior of operators of station-signaling systems responsible for rail traffic management. We use a technique for human-error rate prediction as the 1st generation human reliability analysis to deal with task analyses, error identification and representation, and the quantification of human error probabilities. The paper contributes to the comparison of three technologically different railway traffic control systems, having different degrees of automation—from the manually operated (electro-mechanical), through semi-automated (relay-based) to almost fully automated (computer-based) station-signaling systems. We observe the frequency of individual operations performed in time intervals and calculate human error probability and human success probability values for each operation. Thus, we can analyze human reliability and compare the workload of operators working with control systems of different degrees of automation.

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.

A tunnel tube is a relatively small space that allows for the accumulation of gaseous and liquid substances containing harmful substances. Given this fact, a ventilation system is the most critical component of a tunnel’s technological equipment, greatly influencing its reliability and safe operation. The dynamic behaviour of pollutants in the tunnel tube is characterized by a significant stochastic component and changing parameters over time due to pressure, airflow, and atmospheric condition changes. This work addresses the issue of modelling individual parts of the tunnel tube for optimal tunnel ventilation control. It is necessary to create a model of a controlled system that is used for predicting process variables to calculate optimal control action. By using recursive identification methods in conjunction with a predictive controller, the proposed concept can be applied to numerous similar applications.

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.

The safety of small CNC (Computer Numerical Control) milling machines is typically ensured through organizational measures. However, when such a CNC milling machine is used in a manner that may involve contact with untrained individuals or children (e.g., for demonstration purposes), it becomes essential to ensure safety through appropriate technical measures. The required safety can be achieved by implementing safety functions to mitigate identified hazards. This paper addresses the design and application of a Safety-Related Control System (SRCS) that implements safety functions for a small CNC milling machine. A notable aspect of this implementation is that commonly available sensors (e.g., optical barriers) with a defined Safety Integrity Level (SIL) are unsuitable for small CNC milling machines in size, cost, and performance parameters. For instance, these sensors might fail to detect the entry of a child’s hand. Consequently, it was necessary to design suitable custom sensors for realizing an SRCS. The paper also demonstrates the achieved SIL for the implemented safety functions.