This paper describes a multiple-variable analysis (MVA) methodology to detect and diagnose three types of faults associated with an electromechanical actuator (EMA): (1) loading faults, such as friction, on the shaft of an EMA motor, (2) shorting faults in the stator windings of the EMA motor, and (3) on-resistance faults in one or more power-switching transistors used to convert direct voltage/current into alternating current. The presented methodology overcomes difficulties associated with typical multivariate analysis (as opposed to multiple-variable analysis) methods such as the following examples: solving simultaneous equations and performing a statistical-based analysis such as K-nearest neighbor (KNN) regression and other Euclidean-based distance methods. Examples of those difficulties are the following: (1) analysis methods that produced information suitable for classification rather than diagnosis or prognosis; (2) noisy data; (3) dependent data, rather than independent data; and (4) difficulty in processing test data to identify, extract, and use leading indicators of failure for prognostic purposes. The primary MVA solution methods included (1) noise mitigation, (2) a unique root-mean-square (RMS) of quantifying phase current values, and (3) a combination of nearest neighbor and distance methods of processing phase-current data to unequivocally identify and isolate faults and to diagnose a future time at which functional failure is likely to occur. *ARULEAV is a trademark of Ridgetop Group, Inc.