2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 9 , Issue 2 , PP: 231-238, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Reem Atassi 1 * , Fuad Alhosban 2
Doi: https://doi.org/10.54216/JISIoT.090217
The Industrial Internet of Things (IoT) has ushered in a new era of predictive maintenance, revolutionizing the way industries manage and maintain their critical equipment. This paper presents a comprehensive exploration of predictive maintenance strategies, with a primary focus on early fault detection and classification in industrial equipment. We introduce the "Triplet Fault Injection Algorithm," capable of injecting three distinct fault types—spike, bias, and stuck—into sensor data for realistic and rigorous testing. Leveraging this algorithm, we employ the powerful Extreme Gradient Boosting (XGBoost) machine learning approach to detect and classify these faults. Our experimental results showcase the superiority of XGBoost over baseline machine learning methods, across various data types commonly found in industrial equipment. The consistent higher accuracy and F1 scores obtained with XGBoost underscore its effectiveness in minimizing false alarms and enhancing the reliability of early fault detection. Moreover, we discuss the transformative role of IoT in predictive maintenance, highlighting its potential to optimize equipment performance and reduce downtime in the industry 4.0 landscape. This paper contributes valuable insights and empirical evidence to the domain of predictive maintenance in IoT-enabled industries, emphasizing the significance of early fault detection for efficient and cost-effective maintenance practices.
Predictive Maintenance , IoT (Internet of Things) , Fault Detection , Failure Prediction , Industrial Equipment , Condition Monitoring , Sensor Data Analysis.
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