IEEE Transactions on Industrial Electronics · 2018 · 41 citations · 20 references
EngineeringElectrical EngineeringMechanical EngineeringElectric MachinesMotor ControlPower ElectronicsBrushless Dc MachinesChaos TheoryElectric DrivesElectric Motor Drive SystemsElectric MotorsFault DetectionBrushed Dc MotorsBrushless Direct Current MachinesElectric Motor DiagnosticsElectric Motor Fault DiagnosisFault Diagnosis In DrivesFault Diagnosis In Motor DrivesMotor Fault DiagnosisTemporal Signal Analysis
The paper proposes a method to quantify chaotic behavior for characterizing electromechanical systems, introducing the Signal Analysis based on Chaos using Density of Maxima (SAC‑DM). SAC‑DM applies a peak‑counting algorithm to the current signal’s time domain to detect faults, demonstrated on a small BLdc motor running at various speeds with both regular and unbalanced propellers. The method achieved 99.16 % accuracy in speed detection and 99.79 % accuracy in identifying an unbalanced system at 50 % motor speed.
This paper presents an approach developed to quantify the chaotic behavior for the characterization of electromechanical systems. A technique named Signal Analysis based on Chaos using Density of Maxima (SAC-DM) is presented. This technique uses a simple peak counting algorithm in the time domain of the current signal to detect faults. To demonstrate the potential of SAC-DM, an experiment is presented where a small brushless direct current (BLdc) motor at different speeds, with a regular and an unbalanced propeller, is used. The results demonstrate that the SAC-DM was able to detect the speed of the BLdc motor in 99.16% of the cases, and to identify the unbalanced system in 99.79% of the cases, when the speed is at 50%.
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Leo Breiman · Machine Learning · 2001 · 126.2K citations · Full text
Time Series Analysis: Univariate and Multivariate Methods
Alice Z. Chuang, William W. S. Wei · Technometrics · 1991 · 1.3K citations