Tacholess Speed Estimation for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain
Xiaowang Chen, Zhipeng Feng
Abstract
In rotating machinery fault diagnosis, the accurate rotational speed is essential for interpretating the repeating fault-induced characteristics. However, installation of extra tachometer can be costly. The widely used induction motor not only drives the powertrain, but also serves as an embedded tachometer via electromagnetic coupling. Available trials on motor-current-based rotational speed estimation either demand the detailed motor configurations or oversimplify the spectral complexity. In this work, a novel rotational speed estimation method is proposed based on adaptive identification of the speed-related modulation sidebands in the induction motor stator current signal. No motor parameter like the numbers of pole pairs or rotor slots is required, and the rotor rotational speed can be automatically extracted from the numerous spectral peaks. The effectiveness of the proposed method is validated by three stator current datasets measured from different induction motors. The obtained rotational speed supports the diagnoses of gear chipping and shaft misalignment in the drivetrain. The feasibility, accuracy, and efficiency of the proposed rotational speed estimation method are quantitatively evaluated with examples.