A method comprising: receiving training data representing a set of operational behaviors associated respectively with a set of components that make up an engineered system ; generating a predictive model , based on the training data , configured to predict a normal operational behavior associated with a component of the set of components relative to other normal operational behaviors associated respectively with other components of the set of components ; receiving real-time data representing a set of real-time operational behaviors associated respectively with the set of components , the set of real-time operational behaviors including a real-time operational behavior associated with the component ; categorizing the real-time operational behavior associated with the component as normal or anomalous based on the predictive model ; and in response to categorizing the real-time operational behavior as anomalous, generating an indication of fault diagnosis ; the method further comprising: generating a second predictive model , based on the training data , configured to predict a second normal operational behavior associated with a second component of the set of components relative to the other operational behaviors associated respectively with the other components of the set of components , wherein the set of real-time operational behaviors includes a second real-time operational behavior of the second component ; categorizing the second real-time operational behavior associated with the second component as normal or anomalous; and in response to categorizing the second real-time operational behavior as anomalous, generating the indication of fault diagnosis , wherein the set of components includes a set of measurement sensors; and whereby the method further comprises analyzing a pattern based on the first predictive model and the second predictive model to enable identification of the component, wherein the indication of fault diagnosis identifies the component. receiving training data representing a set of operational behaviors associated respectively with a set of components that make up an engineered system ; generating a predictive model , based on the training data , configured to predict a normal operational behavior associated with a component of the set of components relative to other normal operational behaviors associated respectively with other components of the set of components ; receiving real-time data representing a set of real-time operational behaviors associated respectively with the set of components , the set of real-time operational behaviors including a real-time operational behavior associated with the component ; categorizing the real-time operational behavior associated with the component as normal or anomalous based on the predictive model ; and in response to categorizing the real-time operational behavior as anomalous, generating an indication of fault diagnosis ; generating a second predictive model , based on the training data , configured to predict a second normal operational behavior associated with a second component of the set of components relative to the other operational behaviors associated respectively with the other components of the set of components , wherein the set of real-time operational behaviors includes a second real-time operational behavior of the second component ; categorizing the second real-time operational behavior associated with the second component as normal or anomalous; and in response to categorizing the second real-time operational behavior as anomalous, generating the indication of fault diagnosis , wherein the set of components includes a set of measurement sensors; and whereby the method further comprises analyzing a pattern based on the first predictive model and the second predictive model to enable identification of the component, wherein the indication of fault diagnosis identifies the component. <EOS>