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research-article

A Data-Driven, Statistical Feature Based, Neural Network Method for Rotary Seal Prognostics

[+] Author and Article Information
Madhumitha Ramachandran

School of Industrial and Systems Engg. Felgar Hall, Rm 147, 865 Asp Ave Norman, OK 73019 madhumitha23@ou.edu

Zahed Siddique

865 ASP Ave Felgar Hall, Rm 212 Norman, OK 73019 zsiddique@ou.edu

1Corresponding author.

Manuscript received September 19, 2018; final manuscript received January 18, 2019; published online xx xx, xxxx. Assoc. Editor: Francesco Lanza di Scalea.

ASME doi:10.1115/1.4043191 History: Received September 19, 2018; Accepted January 18, 2019

Abstract

Failure of rotary seal is one of the foremost causes of breakdown in rotary machinery, and such a failure can be catastrophic, resulting in costly downtime and large expenses. Assessing performance degradation of rotary seal is very important for maintenance decision making. Although a significant progress has been made over the last five years to understand the degradation of seals using experimental verification and numerical simulation, there is research gap on the data-driven based tools and methods to assess the health condition of rotary seals. In this paper, we propose a data-driven based performance degradation assessment approach to classify the running/health condition of rotary seals, which was not considered in previous studies. Statistical time domain features are extracted from friction torque based degradation signals collected from a rotary set-up. Wrapper based feature selection approach is used to select relevant features, with multilayer perceptron neural network utilized as classification technique. To validate the proposed methodology, an accelerated aging and testing procedure is developed to capture performance of rotary seals. The study findings indicate that MLP classifier built using features related to the amplitude of torque signal has a better classification accuracy for unseen data when compared to logistic regression and random forest classifiers.

Copyright © 2019 by ASME
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