On the implications of artificial intelligence methods for feature engineering in reliability sector with computer knowledge graph
This work employs support vector machine (SVM), K-Nearest Neighbors (KNN) and logistic regression models to predict the health state of the pump and to establish fault diagnosis. From the features like vibration, temperature of the motor, pressure, and flow rate, the models categorize the state of t...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001206 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This work employs support vector machine (SVM), K-Nearest Neighbors (KNN) and logistic regression models to predict the health state of the pump and to establish fault diagnosis. From the features like vibration, temperature of the motor, pressure, and flow rate, the models categorize the state of the pump into two; normal or No Fault, and Fault Detected. This makes it possible to detect specific faults and assist in creating preventive maintenance. Post analysis, it was inferred that with an accuracy of 0.92, the SVM with a linear kernel outperformed the competing models. While the KNN performed marginally worse with an accuracy of 0.85, the SVM with RBF and polynomial kernels as well as logistic regression both attained accuracy of 0.91. These findings highlight the SVM with a linear kernel’s superior generalization skills, which make it the best option for pump system defect identification. For defect detection, giving the SVM with a linear kernel priority guarantees precise predictions, allowing for proactive maintenance and minimizing downtime. To improve operational efficiency and lower long-term maintenance costs, policy ideas include standardizing data collection techniques, investing in real-time monitoring systems, and implementing machine learning-based predictive maintenance across industries. |
|---|---|
| ISSN: | 1110-0168 |