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Thursday, May 16 • 11:00am - 11:30am
Electromechanical Actuator a Case Study: Multivariate Analysis of Phase Currents to Detect Three Types of Faults

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This paper describes an multivariate-analysis (MVA) methodology to detect and prognose three types of faults associated with an electromechanical actuator (EMA). The faults are the following: (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 overcome difficulties associated with typical MVA methods such as the following examples: solving simultaneous equations, performing a statistical-based analysis such principal component analyses (PCA), and a K-nearest neighbor (KNN) regression or other Euclidean-based distance methods. Examples of those difficulties are the following: (1) 'noise' in the data containing the signal(s) of interest, (2) method produces information suitable for classification' rather than diagnosis or prognosis; and (3) the data does not include known independent variables, rather all variables in the data are dependent - which is the case for the phase currents of an EMA. A unique root-mean-square (RMS) of quantifying phase current values and a methodology for using those values are presented with examples that demonstrate that phase-current data for each of the three faults can be processed to unequivocally identify and isolate the fault, and to prognose a future time at which functional failure is likely to occur.

Speakers
avatar for James Hofmeister

James Hofmeister

Distinguished Engineer, Ridgetop Group, Inc.


Thursday May 16, 2019 11:00am - 11:30am
Freedom Ballroom V