Brushless DC motor actuator health monitoring and degradation compensation via real-time multiple parameter estimation

Patankar, Ravindra; Zhu, Liangtao

doi:10.1504/ijaac.2007.013297

Cited by 2 publications

(1 citation statement)

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“…Although advancements in model-based (and amongst them, parameter estimation) approaches since 2002 are reported, [6][7][8][11][12][13][14] not all of them apply to complex systems such as the EMA dealt with here. In the few instances that do, 6, 14 the approaches are either too simple with unaddressed issues 6 or sufficiently accurate but not cost effective enough to be put into operation.…”

Section: B Previous Workmentioning

confidence: 99%

Bearing Fault Detection in Electromechanical Actuators from Empirically Extracted Features

Sridhar

Kolodziej

Hall

2013

AIAA Atmospheric Flight Mechanics (AFM) Conference

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This paper proposes an approach to detecting bearing faults in electromechanical actuators (EMAs) using features extracted from experimental data. The method of feature extraction proposed uses established parameter estimation techniques based on system identification followed by an orthogonal transformation of estimated parameters to derive the required features. A Bayesian classifier is then used to create health classes from the extracted features. The performance of the approach is tested using both data obtained from simulations of bearing faults in a permanent magnet DC motor system as well as data recorded from a Moog MaxForce EMA. The approach shows a misclassification performance of 10% when tested with 50 different data sets generated via simulations. Marginally inferior performance is observed when using 40 different data sets collected from the Moog MaxForce EMA. The conclusion is that bearing fault detection in EMAs is possible via the proposed approach, although further refinements are required. Nomenclature f cBearing fault frequency reflected in stator current y(t), u(t) Generic discrete-time output and input data e(t) Generic discrete-time white-noise process/Equation-error q −1 , φ Backward shift operator and regression vector v m , i m , ω m Armature voltage, current and angular speed L, T ex Armature inductance and external torque load J, BNet system inertia and net viscous friction coefficient K e , K t

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