Innovative Approaches for Dynamic System Monitoring: Signal Processing and Parameter Estimation Strategies
Journal of Engineering Research and Reports · pp. 31–41 · Published 30 Mar 2024
10.9734/jerr/2024/v26i51131Abstract
Dynamic system monitoring is essential for ensuring the optimal performance and reliability of various systems across multiple domains. This Abstract introduces innovative approaches focusing on signal processing and parameter estimation strategies for dynamic system monitoring. Signal processing techniques such as wavelet transform and adaptive filtering are utilized for noise reduction and feature extraction from sensor data. Additionally, parameter estimation strategies including Kalman filtering and Bayesian inference aid in accurately estimating system parameters and states in real-time. These advanced methods, integrating machine learning and statistical inference, promise enhanced monitoring capabilities, facilitating proactive maintenance and fault detection in complex dynamic systems. Through case studies and simulation results, the effectiveness and versatility of these approaches in addressing real-world challenges are demonstrated, illustrating their potential for advancing the field of dynamic system monitoring.
Cited by 2
Rui Huang, Ke Zhang, Jiaxi Zhang · 2025 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS) · 2025
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