Methodology on Exact Extraction of Time Series Features for Robust Prognostics and Health Monitoring
Chao Jin · OhioLink ETD Center (Ohio Library and Information Network) · 2017
Maintaining health model robustness has always been a challenge in prognostics and health management.Research on developing advanced machine learning algorithms has shown great promise, but the prognostic performance is limited when the feature quality is poor.This thesis proposes an extensible preprocessing methodology that applies time series pattern recognition to transient-rich and background-rich systems for robust prognostics and health monitoring.This method recognizes patternsof-interest accurately to facilitate exact extraction of diagnostic information, namely, features.It takes three phases to realize exact feature extraction.First, hierarchical time series classifiers filter out the signals with few critical patterns and prepare the pattern recognition tools for segmentation.Second, time series pattern recognition identifies and segments the patterns-of-interest. Third, extract pattern-specific features as the input for health modeling.The developed exact feature extraction method is validated on two case studies: semiconductor etching process health monitoring and gas type classification using uncalibrated chemical sensors in complex environment.The proposed method is validated to outperform conventional feature extraction such as summary statistics and observation in both studies.The benefits of exact feature extraction include accuracy, consistency, generality, and extensibility.The recognition of patterns enables accurate description of critical process properties and accelerates segmentation compared to human observation.The extracted features are more consistent in healthy condition and more sensitive to faults.Also, the pattern recognition tools are designed for general engineering systems which can be applied to a wide range of industries.Besides, the semi-automated process allows human intervention to include additional patterns for an extensible and customized solution.This thesis embraces domain knowledge and attempts to generalize them and build engineering syntax and semantics at the fundamental level in the PHM system with the assistance of pattern recognition.Instead of making a decisive conclusion, this study hopes to usher in more research on feature quality and broaden the research frontier for prognostics and health management.iii