Impact of Environmental Temperature Variation on Vibration- Based Fault Detection for Air Compressors

Abstract

Condition-Based Maintenance Plus (CBM+) aims to enhance operational readiness for U.S. Navy assets by using predictive models to forecast equipment failures. Applying CBM+ in the U.S. Navy faces a unique challenge: ships operate globally for extended periods, exposing machinery to a wide range of ambient air and seawater temperatures that alter their characteristic vibration signatures and can compromise model performance. This paper investigates the extent to which these seasonal temperature variations degrade the performance of a vibration-based fault detection model for a naval air compressor. Using data from controlled testing, vibration data was collected under healthy and various induced-fault conditions during both winter and summer to create two environmentally distinct datasets. Power Spectral Density analysis was used to extract features for training classifiers. Results show that models trained exclusively on data from one season performed poorly when tested against data from the other, confirming that environmental shifts significantly degrade predictive accuracy. In contrast, a model trained on a combined dataset incorporating data from both seasons demonstrated substantially improved and more generalized performance. These findings underscore that the development of robust, field-ready CBM+ systems is critically dependent on training ML models with comprehensive and environmentally diverse datasets that reflect the full spectrum of anticipated operational conditions.