Data-Driven Hybrid Approach for Early Fault Detection of AHU using Electrical Signals

Available online health monitoring systems (HMS) using mechanical signals such as vibration & temperature for ACMV (Air-Conditioning and Mechanical Ventilation) system detect some of the critical faults only at high severity levels resulting in higher operation and maintenance (O&M) cost. Moreover, multiple monitoring systems are required one for each single component at the sub-system level further decreasing affordability. In this paper, a unique, single hybrid scheme involving both feature extraction and classification using electrical signals based holistic HMS for various types of critical faults of an AHU (Air Handling Unit) and its associated component in the ACMV system is proposed. The proposed approach is capable of detecting anomalies at an early stage and provides efficient condition monitoring and predictive maintenance (PdM) scheduling in advance using mostly electrical signals such as power. We used the electrical power to detect faults at incipient levels as oppesed to mechanical signals based anamoly detection.