Fault Detection Assessment using an extended FMEA and a Rule-based Expert System

Early fault detection and diagnosis lead to enhanced production performance since the companies can avoid unplanned machines downtimes and final product quality issues. Expert systems are widely popular in the industry for fault detection and diagnosis applications. This is due to its easy implementation, and its versatility to transform expert knowledge into rules. This paper proposes an extended FMEA (eFMEA) as a systematic knowledge extraction tool to detail the failure causes of equipment in an industrial process. It outlines a methodology for the automatic generation of expert knowledge rules through the digitalisation of the eFMEA. The last contribution is a rule-based expert system for multi-fault detection and diagnosis based on the digitalised eFMEA. This expert system uses the communication protocol OPC-UA to feed the current data from the equipment, to detect faults. It can detect multi-faults simultaneously. The fault detection and diagnosis are visualised through a web application.