Abstract
Abstract Enterprises and institutions engaged in social production activities need to apply for mandatory verification of general instruments and meters on production equipment according to law, and its verification period is determined by national or local verification and calibration procedures, but the health condition in the calibration intervals is difficult to know. To improve equipment reliability, this paper relies on the linear regression model to research the health condition of general instruments and meters, uses MATLAB tools to analyze the data samples of typical equipment pressure gauge, and combines radial basis function neural network to form a complete algorithm, and finally provides a complete implementation scheme for the health condition prediction of general instruments and meters in service.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
