Servomotor Dataset: Modeling Health in Mechanisms with Typically Intermittent Operation

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Published Oct 26, 2023
Arun Subramanian Abhinav Saxena Jamie Coble

Abstract

Servomotors are used in a variety of industrial applications where precise movements are of critical importance. Degradation mechanisms in servomotors have been mostly studied and modeled for systems with long duration steady state modes. However, some specialized applications require health estimation from very short duration intermittent operations, which require different analysis techniques. With such applications in mind, a simulated dataset for servomotor health modeling and prediction is described and made available for public use. The application scenario is motivated by a fine motion control rod drive (FMCRD) mechanism used for intermittent, and typically infrequent, fine motion (insertion or withdrawal) adjustment of control rods in some nuclear reactor designs. Though the drives do not run continuously, servomotor and associated linear motion mechanisms do show wear and damage during its operational lifetime. Specifically, in FMCRD such degradations may be caused by internal as well as external damage to the system. While the causes of such damage can be diverse, in simulation we model the impact of cumulative damage as an external opposing load which resists the movement of the motor shaft. Such scenarios represent effects of rod-binding and debris in the fuel channels. The dataset includes measurements such as motor currents and rotor speed which would be part of the instrumentation in a typical deployments of rotating machinery. These observable measurements can be used to predict the health state of the servomotor. Also presented are baseline results on health state estimation, formulated as classification and regression problems, which can be used by the larger PHM community for performance comparisons. This dataset is hosted at the PHM Society Data repository [https://data.phmsociety.org/servomotor_dataset/].

How to Cite

Subramanian, A., Saxena, A., & Coble, J. (2023). Servomotor Dataset: Modeling Health in Mechanisms with Typically Intermittent Operation. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3580
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Keywords

servomotor drive, intermittent operation, rotating machinery, transient operation, health prediction, classification, regression, benchmark, dataset

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Section
Industry Experience Papers

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