LPST-Net: A Novel Deep Learning Framework for Bearing-Rotor System Health Monitoring and Prediction Under Complex Operating Conditions

This paper presents LPST-Net, a novel deep interval health monitoring and prediction framework specifically designed for bearing-rotor systems operating under complex and challenging conditions. The framework aims to provide accurate and reliable health status assessments and predict potential failures, enabling proactive maintenance strategies and minimizing downtime.

LPST-Net leverages a powerful combination of advanced signal processing techniques and deep neural networks. The framework effectively captures subtle degradation patterns and complex dynamics inherent in bearing-rotor systems by extracting and analyzing relevant features from raw sensor data.

Key features of LPST-Net include:

• Robustness to complex operating conditions: The framework is designed to handle variations in speed, load, and environmental factors, ensuring accurate monitoring and prediction even in dynamic operating environments.* High accuracy and precision: LPST-Net's deep learning architecture allows it to learn complex relationships within the data, resulting in improved accuracy in health status assessment and failure prediction.* Adaptive learning capability: The framework continuously adapts and improves its performance over time as more data becomes available, enhancing its predictive capabilities.

This paper details the architecture and methodology of LPST-Net, highlighting its effectiveness through rigorous experimental validation using real-world bearing-rotor system datasets. The results demonstrate significant improvements in accuracy and robustness compared to existing methods, showcasing its potential to revolutionize condition-based maintenance practices in various industrial applications.