1. A method for identifying root cause failure in a multi-parameter self learning machine application model comprising: providing at least one multi-function sensor having the capability to measure at least one of a voltage and current of the machine application model; measuring voltages and currents of a multi-phase load with the multi-function sensors in a passive manner which includes sensing existing voltages and currents; storing and accepting the measured voltages and currents into memory by a computer controlled analog to digital converter; calculating at least one of a time-varying variable KW, PF, kVAr, or Z out of the measured voltages and currents; calculating at least one of a first, second or third order derivative of the at least one time-varying variable; classifying segments of at least one of the time-varying variables depending on a state; choosing at least one of the calculated time-varying variables and learning their normal behavior; comparing the normal behavior to a pattern difference; identifying a root cause meaning to the pattern difference; plotting Min, Max and Median values of the measured voltages and currents in a candlestick chart format; and determining the directions with which the measured voltages and currents are heading using the Min, Max and Median values.