Current Characteristic Frequency Research Articles

Motor Current Model for Detecting Localized Defects in Planet Rolling Bearings

A motor current model for the diagnosis of localized defects in planet rolling bearings (PRBs) is proposed in this study. A dynamic analysis of the motor-driven planetary gearbox system is conducted, and a translational-torsional vibration model of the system is developed. Both the additional time-varying impact caused by localized defects and the influence of the Hertzian contact and radial clearance on the translational support forces of PRBs are considered in the model. Based on the magnetic equivalent circuit network model, vibrational-electromagnetic coupling analysis is performed with the aid of the air-gap permeance and electromagnetic torque. By iteratively solving the nonlinear algebraic differential equations describing the dynamic characteristics of the coupling system, the motor current variation curve with localized defects in the PRB is obtained. Taking a typical motor-driven planetary gearbox system as the object, electromagnetic finite element calculations and dynamic response tests are performed to verify the proposed motor current model. The results show that the characteristic motor current frequencies are a combination of the fault passing frequency and power supply frequency when the inner race of the PRB has a localized spall fault. When the outer race (or roller) has a localized spall fault, the combination of current characteristic frequencies, which generally consists of the fault passing frequency, power supply frequency, and PRB outer-race rotational frequency (outer-race spall) or cage revolution frequency (roller spall), become complex. By discussing the effect of the spall width on the motor current spectra in detail, the characteristic frequencies sensitive to defects are extracted, providing a theoretical basis for the quantitative diagnosis of PRB-localized defects.

Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

Bearing faults are the most frequent faults of induction motors. The current spectrum analysis is an easy and popular method for the monitoring and detection of bearing faults. After a presentation of the existing fault models, this paper illustrates an analytical approach to evaluate the effects of the slot harmonics on the stator current in an induction motor with bearing fault. Simple and clear theoretical analysis results in some new current characteristic frequencies. Experimental tests with artificial bearing outer raceway fault are carried out by measuring stator current signals. The experimental results by spectral analysis of the stator current agree well with the theoretical inference.

Current signature analysis and its application in the condition monitoring of wind turbine for rotor faults

This paper proposes a condition monitoring technique using current signature analysis for diagnosis of Rotor eccentricity fault in Squirrel cage induction generators used in wind power systems. The proposed method measures the current characteristic frequencies of the excitations generated by a Wind turbine generator. The excitations generated by wind turbine generator eccentricity fault are extracted and analyzed under no load, full load and varying wind conditions. Side bands are calculated using FFT approach. Fault frequencies are further analyzed using wavelet-based analysis for steady state and dynamic conditions. Multi-resolution mean power indicator has been used for the quantification of the fault. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effectual diagnostic procedure for condition monitoring.

Adjustable-Speed Drive Bearing-Fault Detection Via Wavelet Packet Decomposition

Adjustable-speed drives perform many vital control functions in the industry, serving in such diverse applications as rolling mills, variable-speed compressors, fans, and pumps. When an adjustable-speed drive fails due to a bearing failure, it is usually catastrophic. Bearing defects introduce vibration anomalies that alter the current characteristic frequencies. This paper addresses the application of motor current signature analysis using wavelet packet decomposition to detect bearing faults in adjustable-speed drives.