A machine learning classifier for automated fault detection and diagnosis (AFDD) of rooftop units, addressing practical challenges of application

Abstract

There have been many recent developments to take advantage of machine learning for the task of automated fault detection and diagnosis in air-conditioning equipment. A common approach is to develop a machine learning model to classify fault status, using simulated or measurement data, and test the model’s fault diagnostic capability on the same system that was used for training. However, such models don’t generalize well to different systems. Compounding this challenge, machine learning requires a rich dataset to train the models, and many of the measurement features require additional sensors, all making the potential for practical machine learning AFDD questionable. The current paper focuses on these challenges by: (i) training a model using simulation data from three rooftop units (RTU); (ii) rebalancing the training dataset to reduce false alarms; (iii) developing a feature set that minimizes sensor cost without significantly impacting performance; (iv) testing the model’s performance on a set of laboratory measurement data from the same RTUs that were simulated for training data; (v) testing the model on a faulted RTU in the field. The results demonstrate good progress toward practical application of machine learning based AFDD for RTU, but generalization to different systems remains a challenge.