Version 1
: Received: 13 August 2024 / Approved: 13 August 2024 / Online: 13 August 2024 (13:05:48 CEST)
How to cite:
Lee, J.-H.; Okwuosa, C. N.; Shin, B. C.; Hur, A. J.-W. Spectral-Based Fault Diagnosis Methodology for Industrial Shot Blast Machinery Leveraging XGBoost and Feature Importance. Preprints2024, 2024080942. https://doi.org/10.20944/preprints202408.0942.v1
Lee, J.-H.; Okwuosa, C. N.; Shin, B. C.; Hur, A. J.-W. Spectral-Based Fault Diagnosis Methodology for Industrial Shot Blast Machinery Leveraging XGBoost and Feature Importance. Preprints 2024, 2024080942. https://doi.org/10.20944/preprints202408.0942.v1
Lee, J.-H.; Okwuosa, C. N.; Shin, B. C.; Hur, A. J.-W. Spectral-Based Fault Diagnosis Methodology for Industrial Shot Blast Machinery Leveraging XGBoost and Feature Importance. Preprints2024, 2024080942. https://doi.org/10.20944/preprints202408.0942.v1
APA Style
Lee, J. H., Okwuosa, C. N., Shin, B. C., & Hur, A. J. W. (2024). Spectral-Based Fault Diagnosis Methodology for Industrial Shot Blast Machinery Leveraging XGBoost and Feature Importance. Preprints. https://doi.org/10.20944/preprints202408.0942.v1
Chicago/Turabian Style
Lee, J., Beak Cheon Shin and And Jang-Wook Hur. 2024 "Spectral-Based Fault Diagnosis Methodology for Industrial Shot Blast Machinery Leveraging XGBoost and Feature Importance" Preprints. https://doi.org/10.20944/preprints202408.0942.v1
Abstract
The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery which has direct impact on it’s longevity and profitability. Therefore, failure of a mechanical system or any of it component would be detrimental to production continuity and availability. Consequently,this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework involves analyzing the spectral characteristics of the vibration signals generated by Industrial Shot Blast. Additionally, a peak detection algorithms is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into 10 machine learning classifier, with Extreme gradient boosting (XGB) as the core classifier. Results show that the XGB classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 seconds. Other global assessment metrics were also implemented in the study to further validate the model.
Keywords
Fast Fourier Transform; Peak Detection; Feature Importance; Fault Detection and Isolation; Extreme Gradient Boosting; Machine Learning; Discriminative Features
Subject
Engineering, Safety, Risk, Reliability and Quality
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.
