A method for creating and using a fault model representative of a system comprising one or more components, the fault model comprising one or more fault model components, the method comprising: creating a reusable fault model component associated to one of the one or more components of the system; associating one or more algorithms to the reusable fault model component, the one or more algorithms describing behavior of the reusable fault model component, wherein the one or more algorithms comprises at least one of a transfer function, a signal-to-noise ratio, estimated non-linearities, and an error counter; incorporating the reusable fault model component and the one or more algorithms into the fault model; and during execution of the fault model, recognizing a current regime of the component associated with the reusable fault model component, wherein the current regime recognition provides a context for collected data, and wherein the current regime comprises an indication of equipment mode and state affecting a rate at which equipment usage is accumulated, at which fault indications are viewed as legitimate, at which condition indicators are reliable, and at which data needs to be collected; collecting the data associated with the reusable fault model component, based on the context provided by the current regime; tuning the reusable fault model component, using available data associated with the fault model; and predicting occurrence of degradation of the reusable fault model component based on trend and/or comparisons of output data generated during the step of tuning; determining an alerting level, based on the trend and associated with the outcome of the predicting step. creating a reusable fault model component associated to one of the one or more components of the system; associating one or more algorithms to the reusable fault model component, the one or more algorithms describing behavior of the reusable fault model component, wherein the one or more algorithms comprises at least one of a transfer function, a signal-to-noise ratio, estimated non-linearities, and an error counter; incorporating the reusable fault model component and the one or more algorithms into the fault model; and during execution of the fault model, recognizing a current regime of the component associated with the reusable fault model component, wherein the current regime recognition provides a context for collected data, and wherein the current regime comprises an indication of equipment mode and state affecting a rate at which equipment usage is accumulated, at which fault indications are viewed as legitimate, at which condition indicators are reliable, and at which data needs to be collected; collecting the data associated with the reusable fault model component, based on the context provided by the current regime; tuning the reusable fault model component, using available data associated with the fault model; and predicting occurrence of degradation of the reusable fault model component based on trend and/or comparisons of output data generated during the step of tuning; determining an alerting level, based on the trend and associated with the outcome of the predicting step. recognizing a current regime of the component associated with the reusable fault model component, wherein the current regime recognition provides a context for collected data, and wherein the current regime comprises an indication of equipment mode and state affecting a rate at which equipment usage is accumulated, at which fault indications are viewed as legitimate, at which condition indicators are reliable, and at which data needs to be collected; collecting the data associated with the reusable fault model component, based on the context provided by the current regime; tuning the reusable fault model component, using available data associated with the fault model; and predicting occurrence of degradation of the reusable fault model component based on trend and/or comparisons of output data generated during the step of tuning; determining an alerting level, based on the trend and associated with the outcome of the predicting step.