The effectiveness of fire detection with convolutional neural networks

Modeling the operation process of technical objects, especially complex and intelligent systems, is an important diagnostic problem. Such models are relevant when designing objects, making decisions regarding maintenance, and evaluating operation processes. A common assumption is that the model of the operation process can only record changes in states that occur over a finite period of time. In the traditional approach, when analyzing the number of possible different operating states of an object, a finite set of them is used. The model presented in this article allows analyzing a wide range of issues related to the operation process of complex technical objects. Using the probability functions of states and the readiness functions of a technical object, this model allows modeling the operation process at the initial design stage, assessing the compliance of the operation process of the object with the expected maintenance schedule, and assessing the potential results of the operation process of a complex technical object.