Physics-Informed Feature Space Evaluation for Diagnostic Power Monitoring

Abstract

Sensing solutions provide a rich feature set for electromechanical load monitoring and diagnostics. For example, qualities that describe the operation of an electromechanical load can include measurements of power, torque, vibration, electrical current demand, and electrical harmonic content. If properly interpreted, these measurements can be utilized for energy management, condition-based maintenance, and fault detection and diagnostics. When monitoring several loads from an aggregate data stream, a well-posed feature space will permit not only load identification, but also the characterization of faults and gradual changes in the health of an individual machine. Many feature selection methods assume static and generalizable data, without consideration of concept drift and evolving behavior over time. This article presents a method for evaluating load separability in a feature space prior to the application of a pattern classifier while accounting for changing operating conditions and load variability. A four-year load dataset is used to validate the method.