Date of Award

2025

Document Type

Open Access Master's Thesis

Degree Name

Master of Science in Mechanical Engineering (MS)

Administrative Home Department

Department of Mechanical and Aerospace Engineering

Advisor 1

Gordon G. Parker

Committee Member 1

Chad Walber

Committee Member 2

Shangyan Zou

Abstract

Bipolar stepper motors are common in scenarios that require low-cost precision including 3d printers. The ability to estimate stepper motor load provides an opportunity for health monitoring. For example, excessive load estimates may indicate a future problem in the driven system such as bearing wear. Stepper motor drivers often have built-in stall detection strategies that exploit the driver's precise knowledge of the high-frequency voltage pulses being sent to the motor. The goal of this work was to develop a retrofit approach to stepper motor load estimation using externally measured voltage, current, and speed. A stepper motor dynamometer was created to generate motor responses for repeatable applied loads using a d.c. motor. A classification neural network was successfully trained to estimate four different loads at two speeds using images of the sensed current versus voltage Lissajous plot. The measurement system was implemented using dSPACE, however, its signal processing and sensing suite was not exotic and could be implemented on an embedded processor of opportunity. The neural network was implemented on an analysis computer that received batch samples of current, voltage and speed at 1 Hz. The main contribution of this work was to illustrate the feasibility of a retrofit solution for load estimation. Extension to continuous load estimation is left for future work.

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