Adaptively Estimating Rotation Speed From DC Motor Current Ripple for Order Tracking and Fault Diagnosis

Abstract

An adaptive signal decomposition and selection (ASDS) method is proposed to estimate the rotation speed of a direct current (dc) motor. This paper contributes to order tracking (OT) and fault diagnosis of dc motor independent of a shaft encoder and under the condition of speed variation. Wavelet transform is first applied to the current signal, and the wavelet ridge is extracted from the time–frequency plane. Synchronous demodulation and smoothing spline fitting are subsequently applied to reconstruct and adaptively select the signal component related to rotation ripple. The ASDS method is used to estimate the rotation speed accurately from the extracted rotation ripple, which is interfered with the dominant dc fluctuation and current noises. The proposed method is applied to variable-speed bearing fault diagnosis by combining vibration signal analysis. The effectiveness and robustness of the method are verified by the current signals acquired from both constant and variable load conditions. This method, as a new tacholess OT technique, can be extended to fault diagnosis of other mechanical and electrical components in a dc motor under variable-speed condition.