MLOps Framework for Fault Diagnosis in Air Conditioners Using Field Noise

Abstract

Fault diagnosis of heating, ventilation and air‑conditioning (HVAC) equipment relies increasingly on data‑driven models. However, real‑world after‑service recordings captured by technicians are noisy, imbalanced and often contain meaningless segments. These are labeled by domain experts but sometimes mislabeled. This paper proposes an initial noise‑aware machine learning operations (MLOps) framework that enables robust classification, calibration as a prerequisite to uncertainty estimation and continuous improvement of air‑conditioner sound diagnostics. The framework performs data preprocessing, uncertainty-based identification of label noise, systematic relabeling through gradient-based class activation maps (Grad-CAM++, hereafter referred to as CAMs), and clustering. A comprehensive metrics tracking facilitates reproducible experiments. Experiments on field recordings demonstrate that removing label noise leads to better generalization, as the learned representation forms more distinct clusters in the logits space, reducing the presence of mislabeled samples within each cluster. The proposed approach yields better generalization and provides a scalable pathway toward automated labeling and open‑set recognition.