Abstract: A system/method of diagnosing combinations of failures in a system includes receiving symptom data (116) including information relating to observed or detected symptoms in a system. The system/method generates (D4a, D4b, D5) failure data (118) including information relating to at least one most probable failures in the system based on the symptom data, and processes (D9) the failure data and the symptom data using an L-best inference (e.g. a Ranked Algorithm (RA)) technique in order to generate failure set data (120), the failure set data including information relating to at least one most probable combination of the failures that explain the symptoms.

Abstract: A method of assisting failure diagnosis in a system includes obtaining data including a probabilistic Bayesian Network describing a set of failures, a set of symptoms and probabilities of at least some of the symptoms being associated with at least some of the failures in a system. A cost value representing a cost associated with learning of a presence or absence of the symptom is obtained for at least some of the symptoms, as well as a plurality of information values, e.g. values representing measures of information gained by learning of the presence or absence of the symptom in relation to a respective plurality of the failures, associated with the symptom. The method then computes an information-for-cost value for the symptom based on the cost value and the plurality of information values.

Abstract: A method of generating probability data for use in assessing performance of a system and a mission involving the system. The method includes receiving (302) a combined model describing a system diagnosis model describing symptoms, failures and link strengths (conditionals) of components/subsystems of the system, and a mission impact model, the mission impact model describing effects of the system on an ability to perform the mission. Observation data regarding a state of the system is also received (306) The observation data is used (308) in combination with the combined model to generate probability data for use in assessing performance of the system and the mission.

Abstract: A system/method of diagnosing combinations of failures in a system includes receiving symptom data (116) including information relating to observed or detected symptoms in a system. The system/method generates (D4a, D4b, D5) failure data (118) including information relating to at least one most probable failures in the system based on the symptom data, and processes (D9) the failure data and the symptom data using an L-best inference (e.g. a Ranked Algorithm (RA)) technique in order to generate failure set data (120), the failure set data including information relating to at least one most probable combination of the failures that explain the symptoms.

Abstract: A method of generating probability data for use in assessing performance of a system and a mission involving the system, the method including receiving (1002) model data representing a combined model of a system and a mission involving the system. The method further includes producing (1004) a Conjunctive Normal Form (CNF) encoding of the combined model data and producing (1006) a smooth deterministic Decomposable Negation Normal Form (sd-DNNF) representation of the CNF encoding. The method further includes producing (1008) an Arithmetic Circuit based on the sd-DNNF representation; receiving (306) observation data, and performing (308) inference on the observation data using the Arithmetic Circuit in order to produce probability values relating to performance of the system and the mission.

Abstract: A method of generating probability data for use in assessing performance of a system and a mission involving the system. The method includes receiving (302) a combined model describing a system diagnosis model describing symptoms, failures and link strengths (conditionals) of components/subsystems of the system, and a mission impact model, the mission impact model describing effects of the system on an ability to perform the mission. Observation data regarding a state of the system is also received (306) The observation data is used (308) in combination with the combined model to generate probability data for use in assessing performance of the system and the mission.

Abstract: The method includes obtaining system model data representing a set of failures in a system including a plurality of components, a set of symptoms and relationships between at least some of the failures and symptoms. The system model data is used to create a Bayesian Network. Failure cases data is also obtained, where each failure case describes the presence/absence of at least one of the symptoms and the presence/absence of at least one of the failures. A learning operation on the Bayesian Network using the failure cases data is then performed and the contribution made by at least some of the failure cases to updating the parameters of the Bayesian Network during the learning operation is assessed. Information representing the assessed contribution of the at least some failure cases is displayed.

Abstract: A method of assisting failure mode and effects analysis of a system having a plurality of components includes obtaining data associated with a component, or a group of components, of the system. The component or the group is associated with component type data or group type data, respectively, that includes data relating to at least one failure feature common to all components or groups, respectively, of that type. The component/group data and the component/group type data can then be stored and/or transferred for use in a failure mode and effects analysis of the system.

Abstract: The method includes obtaining system model data representing a set of failures in a system including a plurality of components, a set of symptoms and relationships between at least some of the failures and symptoms. The system model data is used to create a Bayesian Network. Failure cases data is also obtained, where each failure case describes the presence/absence of at least one of the symptoms and the presence/absence of at least one of the failures. A learning operation on the Bayesian Network using the failure cases data is then performed and the contribution made by at least some of the failure cases to updating the parameters of the Bayesian Network during the learning operation is assessed. Information representing the assessed contribution of the at least some failure cases is displayed.

Abstract: A method of assisting failure diagnosis in a system includes obtaining data including a probabilistic Bayesian Network describing a set of failures, a set of symptoms and probabilities of at least some of the symptoms being associated with at least some of the failures in a system. A cost value representing a cost associated with learning of a presence or absence of the symptom is obtained for at least some of the symptoms, as well as a plurality of information values, e.g. values representing measures of information gained by learning of the presence or absence of the symptom in relation to a respective plurality of the failures, associated with the symptom. The method then computes an information-for-cost value for the symptom based on the cost value and the plurality of information values.

Abstract: A method of assisting failure mode and effects analysis of a system having a plurality of components includes obtaining data associated with a component, or a group of components, of the system. The component or the group is associated with component type data or group type data, respectively, that includes data relating to at least one failure feature common to all components or groups, respectively, of that type. The component/group data and the component/group type data can then be stored and/or transferred for use in a failure mode and effects analysis of the system.