An Intelligent Health diagnosis and Maintenance Decision-making approach in Smart Manufacturing

[Display omitted] •the vulnerability-based equipment diagnosis and maintenance method is proposed.•the contribution graph of anomalous performance parameters is drawn.•the data-driven method of screening abnormal factor is proposed.•the health status of equipment is predicted real-timely. Unexpected risks and faults have made health diagnosis and maintenance decision-making of smart manufacturing difficult. Based on CPPS (cyber physical production systems) technology and the nonlinear kernel mapping algorithm,an intelligent health diagnosis and maintenance decision-making method is proposed for the equipment in smart manufacturing. The vulnerability of equipment is employed to conduct the health diagnosis strategies, the proposed method aids maintenance personnel in accurately diagnosing the equipment health and promptly implementing maintenance plans. This method enables maintenance personnel to understand the interactions within and across the equipment and identify the significant influencing factors without having to know the health degradation and potential disturbances of equipment. Moreover, the proposed intelligent health maintenance methodology can enhance equipment health by highlighting how anomalous factors are identified based on the vulnerability of the equipment and the CPPS technology. This paper discusses the main processes of the proposed methodology and demonstrates its application to a robot servo system.