Diagnosing combinations of failures in a system

An apparatus adapted to diagnose combinations of failures in a system, the apparatus including:

• a processor configured to receive symptom data including information relating to observed or detected symptoms in a system;
• a processor configured to generate failure data including information relating to at least two most probable failures in the system based on the symptom data;
• a processor configured to process the failure data and the symptom data using an L-rank inference class algorithm in order to generate failure set data, the failure set data including information relating to at least one most probable combination of the at least two most probable failures that explain the symptoms;
• and
• a processor configured to select an algorithm for use in generating the failure data, wherein a comparison based on a number of present symptoms in the symptom data and a user-configurable value is used to select the algorithm for use in generating the failure data, and if the number of present symptoms is greater than a user-configurable value then an approximate marginal inference algorithm is selected;
• otherwise, an exact marginal inference is selected, and
• wherein the processors may be the same processor or may include multiple processors.

A non-transitory computer program product encoded with instructions that when executed by one or more processors cause a process of diagnosing combinations of failures in a system to be carried out, the process including:

• receiving symptom data including information relating to observed or detected symptoms in a system;
• generating failure data including information relating to at least two most probable failures in the system based on the symptom data;
• and
• processing the failure data and the symptom data using an L-rank inference class algorithm in order to generate failure set data, the failure set data including information relating to at least one most probable combination of the at least two most probable failures that explain the symptoms;
• selecting an algorithm for use in generating the failure data, wherein a comparison based on a number of present symptoms in the symptom data and a user-configurable value is used to select the algorithm for use in generating the failure data, and if the number of present symptoms is greater than a user-configurable value then an approximate marginal inference algorithm is selected;
• otherwise, an exact marginal inference is selected.