1. A method for machine condition monitoring, comprising the steps of: in a machine condition monitoring system processor, determining a robust correlation coefficient ρ xy between pairs of sensors using data from a group of samples (x i , y i ) from the pairs of sensors, the robust correlation coefficient ρ xy being determined by: initializing a weight w i for each sample (x i , y i ), wherein 0≦w i ≦1 and Σw i =1, each weight w i being proportional to an inverse of a distance between the sample (x i , y i ) and a sample mean; estimating a mean μ and covariance matrix Ω of the sample as 
 μ=Σw i z i and Ω=Σw i (z i −μ)(z i −μ) T , wherein z i =[x i y i ] T ; updating the weight w i for each observation (x i ,y i ) as w i = w ⁡ ( x i , y i ) ∑ w ⁡ ( x j , y j ) wherein ⁢ ⁢ w ⁡ ( x i , y i ) = { 1 f ⁡ ( x i , y i ) < f 0 f 0 / f ⁡ ( x i , y i ) f ⁡ ( x i , y i ) ≥ f 0 , ⁢ wherein ⁢ ⁢ f ⁡ ( x i , y i ) = ( z i - μ ) T ⁢ Ω - 1 ⁡ ( z i - μ ) ; and f 0 is a predetermined threshold probability; and repeating the estimating and updating steps until convergence; calculating the robust correlation coefficient as ρ xy = ∑ i = 1 n ⁢ w i ⁡ ( x i - x _ ) ⁢ ( y i - y _ ) ∑ i = 1 n ⁢ w i ⁡ ( x i - x _ ) 2 ⁢ ∑ i = 1 n ⁢ w i ⁡ ( y i - y _ ) 2 where x =Σw i x i and y =Σw i y i ; and predicting sensor readings using the robust correlation coefficient.