Genetic Programming-Based Feature Construction for System Setting Recognition and Component-Level Prognostics

Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components' behavior understanding. The literature proposes various methods, including signal processing in the time, frequency, and time-frequency domains, feature selection, and unsupervised feature learning. An emerging task in data science is Feature Construction (FC), which has the advantages of both feature selection and feature learning. In particular, the constructed features address a specific objective function without requiring a label during the construction process. Genetic Programming (GP) is a powerful tool to perform FC in the PHM context, as it allows to obtain distinct feature sets depending on the analysis goal, i.e., diagnostics and prognostics. This paper adopts GP to extract system-level features for machinery setting recognition and component-level features for prognostics. Three distinct fitness functions are considered for the GP training, which requires a set of statistical time-domain features as input. The methodology is applied to vibration signals extracted from a test rig during run-to-failure tests under different settings. The performances of constructed features are evaluated through the classification accuracy and the Remaining Useful Life (RUL) prediction error. Results demonstrate that GP-based features classify known and novel machinery operating conditions better than feature selection and learning methods.

Automated summary

Extracting representative feature sets from raw signals is crucial in understanding components' behavior in Prognostics and Health Management (PHM). This paper adopts Genetic Programming (GP) to extract system-level and component-level features, and evaluates their performance through classification accuracy and Remaining Useful Life (RUL) prediction error.