Ensemble Based Real-Time Adaptive Classification System for Intelligent Sensing Machine Diagnostics

• Published in: IEEE transactions on reliability 2012-06, Vol.61 (2), p.303-313
• Main Authors: Minh Nhut Nguyen, Chunyu Bao, Kar Leong Tew, Teddy, S. D., Xiao-Li Li
• Format: Article
• Language: English
• Subjects: Adaptation models; Adaptive classifiers; Adaptive systems; Classification; classifiers; data driven diagnostics and prognostics; Data models; Diagnostic systems; ensemble learning; Monitoring; Monitoring systems; Networks; Predictive models; Product introduction; Sampling; sensor data classification; Sensors; Support vector machines; Systems health monitoring; Training; Vibrations; Wireless sensor networks
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2012.2194352

The deployment of a sensor node to manage a group of sensors and collate their readings for system health monitoring is gaining popularity within the manufacturing industry. Such a sensor node is able to perform real-time configurations of the individual sensors that are attached to it. Sensors are capable of acquiring data at different sampling frequencies based on the sensing requirements. The different sampling rates affect power consumption, sensor lifespan, and the resultant network bandwidth usage due to the data transfer incurred. These settings also have an immediate impact on the accuracy of the diagnostics and prognostics models that are employed for system health monitoring. In this paper, we propose a novel adaptive classification system architecture for system health monitoring that is well suited to accommodate and take advantage of the variable sampling rate of sensors. As such, our proposed system is able to yield a more effective health monitoring system by reducing the power consumption of the sensors, extending the sensors' lifespan, as well as reducing the resultant network traffic and data logging requirements. We also propose an ensemble based learning method to integrate multiple existing classifiers with different feature representations, which can achieve significantly better, stable results compared with the individual state-of-the-art techniques, especially in the scenario when we have very limited training data. This result is extremely important in many real-world applications because it is often impractical, if not impossible, to hand-label large amounts of training data.