  This work proposes to put up a tool for diagnosing multi faults based on
model using techniques of detection and localization inspired from the
community of artificial intelligence and that of automatic. The diagnostic
procedure to be integrated into the supervisory system must therefore be
provided with explanatory features. Techniques based on causal reasoning are a
pertinent approach for this purpose. Bond graph modeling is used to describe
the cause effect relationship between process variables. Experimental results
are presented and discussed in order to compare performance of causal graph
technique and classic methods inspired from artificial intelligence (DX) and
control theory (FDI).
