An Investigation of Enhanced Lubrication Frequency and Wear Monitoring on ACME Screws

With many power screws being in remote places, it can be time consuming and costly to conduct maintenance or inspection on electromechanical actuators (EMAs). This means increased downtime and less productivity, whatever the application may be. While there has been a lot of research on health monitoring of systems, as well as friction and wear on screws, there is a need to more easily show which parts of ACME screws would see intermittent motion, leading to a better prediction of which areas of the screw will see more wear and friction changes than others. In collaboration with Safran Landing Systems, this project involved the application of a newly developed, patent-pending method for motion tracking of ACME screws, called the Statistical Motion Tracking (SMT) method. The SMT method involves using 𝑛-points, a range of points on the screw, whereby each 𝑛-point can be each thread, or otherwise a segmented length, for instance, every millimeter can be one n-point. As ACME screws can undergo intermittent motion profiles based on different applications during their lifetime, the SMT method helps to tally which areas may undergo higher wear as well as friction changes due to certain of the 𝑛 points seeing more use than others. This accommodates predictive maintenance schedules as well as specifies which areas of the screw may need closer attention than others during inspection. Data from a test on an anti- skid braking system conducted by NASA was used, whereby the motion profile of brakes were extracted from different test cases. Three selected profiles were then cycled using an EMA at Safran LS test facility. It was found that the SMT method provides a good tally of which the areas of the screw saw higher use than others. With that, after post-processing test data, it was found that minor changes in friction occurred, which relate to higher motion around certain threads. Additionally, higher local temperature attributes to these changes, hence an added factor of friction changes. This project helped to prove the applicability of the SMT method, and can be made even more reliable by incorporating an AI/machine learning algorithm that can track friction changes in real-time at different actuation speeds and loads.