Probabilistic Estimations of Increasing Expected Reliability and Safety for Intelligent Manufacturing

In the near future the possibilities of the modern probabilistic models, artificial intelligence and machine learning methods can provide an intelligent support of making decisions by an operator in real time. An agile recovery of intelligent manufacturing integrity can be implemented owing to the development of industrial robotics. For intelligent manufacturing it means the expected reliability and safety may be in the near future at the expense of intelligent support of decision making and the agile recovery of integrity. To answer the question “How much essential may be this increasing?” here are proposed: general analytical approaches for a probabilistic estimation of the expected reliability and safety for every monitored element or the system of intelligent manufacturing on a level of probability distribution functions (PDF) of the time between the losses of system integrity; estimations of increasing the expected reliability and safety for intelligent manufacturing at the expense of the intelligent support of decision making and agile recovery of integrity; the comparisons of the estimations on a prognostic period up to 10 years using the identical model in applications to expected reliability and safety. The applications of the proposed approaches allow the customers, designers, developers, users and experts of Industry 4.0 intelligent manufacturing to be guided by the proposed probabilistic estimations for solving problems of reliability and safety in the system life cycle. The results are demonstrated by examples.