Work

On the implications of artificial intelligence methods for feature engineering in reliability sector with computer knowledge graph

Year: 2025

Type: article

Abstract: 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.

Source: Alexandria Engineering Journal

DOI: 10.1016/j.aej.2025.01.093

Authors: Heling Jiang, Yongping Xia, Changjie Yu, Zhao Qu, Huaiyong Li

Institution: Guizhou University of Finance and Economics

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Good health and well-being

Open Access status: gold

Estimated APC paid: $1,900