Fault Detection Assessment using an extended FMEA and a Rulebased 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 rulebased expert system for multifault detection and diagnosis based on the digitalised eFMEA. This expert system uses the communication protocol OPCUA to feed the current data from the equipment, to detect faults. It can detect multifaults simultaneously. The fault detection and diagnosis are visualised through a web application.

Analysis of the applicability of fault detection and failure prediction based on unsupervised learning and Monte Carlo simulations for real devices in the industrial automobile production Machine learning and statisticalbased methods have been widely used to estimate the Remaining Useful Life of devices. However, in most of the cases, the transferability of these methods to production environments is complicated. The majority of these algorithms have been tested by using simulated data, or by the construction of testbeds. The complication is generated by some real conditions in such environments which are not considered during the algorithm development. This research addresses those drawbacks by considering real data delivered by a production device in order to construct the right condition monitoring parameter. This parameter will be obtained by analyzing the correlation among available data, and failure on the critical element on the machine. Moreover, the benefits and drawbacks of several datadriven approaches are analyzed, and a method is proposed based on the combination of the best properties of each one.