Version 1
: Received: 19 March 2023 / Approved: 22 March 2023 / Online: 22 March 2023 (04:31:53 CET)
How to cite:
Wardhana, I. Feature Selection in the Context of Prognostic Health Management. Preprints.org2023, 2023030391. https://doi.org/10.20944/preprints202303.0391.v1
Wardhana, I. Feature Selection in the Context of Prognostic Health Management. Preprints.org 2023, 2023030391. https://doi.org/10.20944/preprints202303.0391.v1
Cite as:
Wardhana, I. Feature Selection in the Context of Prognostic Health Management. Preprints.org2023, 2023030391. https://doi.org/10.20944/preprints202303.0391.v1
Wardhana, I. Feature Selection in the Context of Prognostic Health Management. Preprints.org 2023, 2023030391. https://doi.org/10.20944/preprints202303.0391.v1
Abstract
In the chemical processing industries, sensors for pumps are among the most commonly used machinery. Condition-based maintenance (CBM) and prognosis health management (PHM) determine the most cost-effective time to overhaul pumps. In order to determine the status of the pump, a signal-emitting accelerometer is employed. Stationarity-based feature extraction from amplitude signals is used to process the signal. Utilizing the time-domain function, multiple statistical results were produced. Eight fault codes were classified using support vector machine method. The enormous amount of data points necessitated the use of feature selection. In terms of accuracy, precision, recall, and F1 score, the Chi-square feature selection method exceeds other approaches.
Keywords
prognosis and health management, preprocessing data, feature extraction, feature selection.
Subject
Medicine and Pharmacology, Veterinary Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
