Adaptive Decoupling of Nonideal Machine and Sensor Properties for Extraction of Fine Details When Using the Motor Drive as a Diagnostic Sensor

This paper presents methods to enhance the quality of spatial information that can be extracted when using the motor drive as a diagnostic sensor. When using the state-of-the-art motor drive-based techniques for extracting spatial information related to the state of wear in a gear train driven by an ac machine, position sensor-induced periodic position measurement errors and machine-induced spatial harmonic torque ripple can degrade the quality and the detail of the information that can be extracted. In this work, this degradation of information is displayed experimentally, and it is demonstrated that the quality of the spatial information extracted can be significantly improved through implementation of existing model reference adaptive system (MRAS)-based nonideal sensor property decoupling techniques and newly developed MRAS-based spatial harmonic torque ripple decoupling techniques. Furthermore, it is identified and demonstrated in this work that by removing the gear mesh harmonic component from the gear wear information extracted using the state-of-the-art techniques, and then calculating the spatial rate of change of this new information, a new signal is generated that strongly correlates to local gear tooth defects in a gear train driven by an ac machine.