Nonintrusive Ventilation System Diagnostics

Abstract

Ventilation systems depend on periodic preventive maintenance. Unfortunately, mission critical systems, exposed to varying levels of particulates and other contaminants, may require unpredictable and highly variable maintenance intervals. Direct sensing for pressure drops or other condition metrics can provide prognostic indicators, but at the expense of the installation and maintenance burden of the sensors themselves. Rotor slot harmonics from motors have been observed for decades to track motor speed. This paper presents hardware instrumentation and a signal processing algorithm that can nonintrusively track the rotor slot harmonics of multiple fan motors in an aggregate electrical stream. Combined with a physics-based model of the motors under observation, this nonintrusive data can be used to track the maintenance condition of multiple fan motors on a single electrical service, minimizing the costs of installation and data analysis and reducing the total number of sensors necessary to track fan health. Results are demonstrated with experiments on-board a US Coast Guard Cutter.