A method for diagnosis detection in an industrial system, comprising: (a) describing a plurality of possible diagnoses, each diagnosis description being defined by symptoms; (b) prioritizing said plurality of possible diagnoses based on a plurality of confidence levels based on a relative number of observed symptoms among all symptoms defining a diagnosis description; (c) generating a plurality of hypotheses for the observed symptoms via an abduction logical algorithm, wherein the abduction logical algorithm consists of computing hypotheses based on the observed symptoms and the possible diagnoses; (d) selecting a finite input of selected hypotheses from said generated plurality of hypotheses by applying a relevance measure based on their priorities; (e) obtaining information regarding a plurality of system observations for automated hypothesis selection from said finite input of selected hypotheses, wherein a relevance measure is used being stated as equal to P2(Si)xP3(Si)xI(Si), where: P2 is a calculated value, based on the relative number of diagnoses, which were identified as possible due to this symptom; P3 is a relative value of the observed features for this particular symptom; and I(Si) is a measure of the interestingness of the symptom, that is defined as I Si = ∑ j P 1 Dj x | Dj \ ObservedSituation ∩ Si | where: P1 is a calculated value, based on the relative number of the observed symptoms among all symptoms, which define a diagnosis description; |( Dj \ ObservedSituation)∩ Si | represents whether Si belongs to the definition of the respective diagnosis, and takes the values of 1 if true and 0 if false, and ∑ j is a sum of all preference values of the respective diagnoses to which the symptom actually belongs; and (f) obtaining a diagnosis for the industrial system based on said automated hypothesis selection. (a) describing a plurality of possible diagnoses, each diagnosis description being defined by symptoms; (b) prioritizing said plurality of possible diagnoses based on a plurality of confidence levels based on a relative number of observed symptoms among all symptoms defining a diagnosis description; (c) generating a plurality of hypotheses for the observed symptoms via an abduction logical algorithm, wherein the abduction logical algorithm consists of computing hypotheses based on the observed symptoms and the possible diagnoses; (d) selecting a finite input of selected hypotheses from said generated plurality of hypotheses by applying a relevance measure based on their priorities; (e) obtaining information regarding a plurality of system observations for automated hypothesis selection from said finite input of selected hypotheses, wherein a relevance measure is used being stated as equal to P2(Si)xP3(Si)xI(Si), P2 is a calculated value, based on the relative number of diagnoses, which were identified as possible due to this symptom; P3 is a relative value of the observed features for this particular symptom; and I(Si) is a measure of the interestingness of the symptom, that is defined as I Si = ∑ j P 1 Dj x | Dj \ ObservedSituation ∩ Si | P1 is a calculated value, based on the relative number of the observed symptoms among all symptoms, which define a diagnosis description; |( Dj \ ObservedSituation)∩ Si | represents whether Si belongs to the definition of the respective diagnosis, and takes the values of 1 if true and 0 if false, and ∑ j is a sum of all preference values of the respective diagnoses to which the symptom actually belongs; and (f) obtaining a diagnosis for the industrial system based on said automated hypothesis selection.