Digital Behavioral Model of CHP Plant Operating Personnel as a Human Factor Reliability Management Tool

One of the factors that determines the risk of disasters, primarily those that are human-induced, as well as the severity of their consequences, is the so-called human factor (HF). Managing HF reliability to predict and prevent possible human-induced accidents and disasters is an extremely urgent and complex task that involves finding a solution to a number of scientific, methodological, and practical problems. Unfortunately, the existing procedural and technical means of monitoring an employee's condition and predicting possible changes, that are currently applied, are ineffective and, for the most part, simply fail to ensure direct monitoring of employees' current productivity or performance of official duties. The goal of the work is to develop the methodological and technical tools necessary to create an automated information and measurement system (AIMS), capable of monitoring and assessing the functional and psycho-emotional state (FPES) of workers, as well as predicting its possible changes for effective management of the HF reliability. The result of the study is the development of a methodology for clarifying the recorded bioparameters of a worker performing movements of varying degrees of intensity. This methodology was the basis for the creation of personal qualification tables that facilitate the assessment of the current state of an employee in the event of emergency and abnormal operating conditions of CHP systems. The practical significance of the work is due to the demand at many industrial enterprises for automated monitoring and forecasting of the condition of workers, working in real time.