A method of determining health of a component, comprising: using at least one processor to perform processes including: acquiring a plurality of measured values for the component corresponding to condition indicators; removing synchronous noise components from the measured values to provide revised measured values; and using the revised measured values to determine noise-filtered condition indicator values; determining a health indicator value using the noise-filtered condition indicator values by: mapping a plurality of condition indicator functions for the noise-filtered condition indicator values into a health indicator function for the health indicator value; determining probability distribution functions for the condition indicator functions; and then determining a probability distribution function for the health indicator function based on the probability distribution functions for the condition indicator functions; wherein removing synchronous noise components from the measured values to provide the revised measured values includes determining a predicted value of the synchronous noise components using covariance and autocovariance of the measured values and further includes: determining an autocovariance vector r where r = x (n-D) · x (n) and where x represents the measured values, n represents an index referring to the respective sample point and D represents a delay; determining a covariance vector R where R = x (n-D) · x t (n-D) and where x represents the measured values, x t represents the transpose of x, n represents an index referring to the respective sample point and D represents a delay; determining a vector a where a = -r · R -1 and where R -1 is the inverse of R; estimating the synchronous noise components using a'x (n-D), where a t represents the transpose of the a vector and D represents a delay; and removing the synchronous noise components by subtracting a t x (n-D) from x(n) to provide a residual signal that captures impulse signals indicative of component fault. using at least one processor to perform processes including: acquiring a plurality of measured values for the component corresponding to condition indicators; removing synchronous noise components from the measured values to provide revised measured values; and using the revised measured values to determine noise-filtered condition indicator values; acquiring a plurality of measured values for the component corresponding to condition indicators; removing synchronous noise components from the measured values to provide revised measured values; and using the revised measured values to determine noise-filtered condition indicator values; determining a health indicator value using the noise-filtered condition indicator values by: mapping a plurality of condition indicator functions for the noise-filtered condition indicator values into a health indicator function for the health indicator value; determining probability distribution functions for the condition indicator functions; and then determining a probability distribution function for the health indicator function based on the probability distribution functions for the condition indicator functions; mapping a plurality of condition indicator functions for the noise-filtered condition indicator values into a health indicator function for the health indicator value; determining probability distribution functions for the condition indicator functions; and then determining a probability distribution function for the health indicator function based on the probability distribution functions for the condition indicator functions; wherein removing synchronous noise components from the measured values to provide the revised measured values includes determining a predicted value of the synchronous noise components using covariance and autocovariance of the measured values and further includes: determining an autocovariance vector r where r = x (n-D) · x (n) and where x represents the measured values, n represents an index referring to the respective sample point and D represents a delay; determining a covariance vector R where R = x (n-D) · x t (n-D) and where x represents the measured values, x t represents the transpose of x, n represents an index referring to the respective sample point and D represents a delay; determining a vector a where a = -r · R -1 and where R -1 is the inverse of R; estimating the synchronous noise components using a'x (n-D), where a t represents the transpose of the a vector and D represents a delay; and removing the synchronous noise components by subtracting a t x (n-D) from x(n) to provide a residual signal that captures impulse signals indicative of component fault. determining an autocovariance vector r where r = x (n-D) · x (n) and where x represents the measured values, n represents an index referring to the respective sample point and D represents a delay; determining a covariance vector R where R = x (n-D) · x t (n-D) and where x represents the measured values, x t represents the transpose of x, n represents an index referring to the respective sample point and D represents a delay; determining a vector a where a = -r · R -1 and where R -1 is the inverse of R; estimating the synchronous noise components using a'x (n-D), where a t represents the transpose of the a vector and D represents a delay; and removing the synchronous noise components by subtracting a t x (n-D) from x(n) to provide a residual signal that captures impulse signals indicative of component fault.